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Paternal exposures (and other non-maternal factors) around pregnancy could have important effects on offspring health. One challenge is that data on partners are usually from a subgroup of mothers with data, potentially introducing selection bias, limiting generalisability of findings. We aimed to investigate the potential for selection bias in studies using partner data.We characterise availability of data on father/partner and mother health behaviours (smoking, alcohol, caffeine and physical activity) around pregnancy from three UK cohort studies: the Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford and the Millennium Cohort Study. We assess the extent of sample selection by comparing characteristics of families where fathers/partners do and do not participate. Using the association of parental smoking during pregnancy and child birthweight as an example, we perform simulations to investigate the extent to which missing father/partner data may induce bias in analyses conducted only in families with participating fathers/partners.In all cohorts, father/partner data were less detailed and collected at fewer timepoints than mothers. Partners with a lower socio-economic position were less likely to participate. In simulations based on ALSPAC data, there was little evidence of selection bias in associations of maternal smoking with birthweight, and bias for father/partner smoking was relatively small. Missing partner data can induce selection bias. In our example analyses of the effect of parental smoking on offspring birthweight, the bias had a relatively small impact. In practice, the impact of selection bias will depend on both the analysis model and the selection mechanism.

Genome-wide association studies (GWAS) have provided large numbers of genetic markers that can be used as instrumental variables in a Mendelian Randomisation (MR) analysis to assess the causal effect of a risk factor on an outcome. An extension of MR analysis, multivariable MR, has been proposed to handle multiple risk factors. However, adjusting or stratifying the outcome on a variable that is associated with it may induce collider bias. For an outcome that represents progression of a disease, conditioning by selecting only the cases may cause a biased MR estimation of the causal effect of the risk factor of interest on the progression outcome. Recently, we developed instrument effect regression and corrected weighted least squares (CWLS) to adjust for collider bias in observational associations. In this paper, we highlight the importance of adjusting for collider bias in MR with a risk factor of interest and disease progression as the outcome. A generalised version of the instrument effect regression and CWLS adjustment is proposed based on a multivariable MR model. We highlight the assumptions required for this approach and demonstrate its utility for bias reduction. We give an illustrative application to the effect of smoking initiation and smoking cessation on Crohn's disease prognosis, finding no evidence to support a causal effect., (© 2024 The Author(s). Genetic Epidemiology published by Wiley Periodicals LLC.)

This study aimed to determine whether the Relative Age Effect (RAE) was present in different sexes and age categories in 12 elite-level international amateur boxing competitions. A total of 4813 athletes competing between 2013 and 2022 were analysed. Athletes were split into four quartiles according to their birth date and compared to the expected (equal) birth date distribution using chi-squared goodness of fit. The results revealed greater RAE prevalence in youth compared to the senior group. RAE was more prominent in males compared to females, with the female group showing an inverse RAE trend. Odds ratio (OR) showed an overrepresentation of male boxers born in Q1 compared to Q4 (OR > 1.19-1.33), while senior female boxers presented an inverse trend (OR < 0.95). Odds Ratios for medallists within the youth group were greater than 1.07 (except Q3 × Q4 = 0.93) highlighting an overrepresentation of boxers born earlier in the year. A shift in strategy to reduce the RAE in young boxers whereby coaches focus less on the short-term pursuit of sporting success is encouraged. Organisations and coaches should increase awareness and provide systematic education around RAE, whilst creating equal competitive opportunities for all young boxers to reduce the extent of RAE in boxing.

Multiple imputation (MI) is commonly implemented to mitigate potential selection bias due to missing data. The accompanying article by Nguyen and Stuart (Am J Epidemiol. 2024;193(10):1470-1476) examines the statistical consistency of several ways of integrating MI with propensity scores. As Nguyen and Stuart noted, variance estimation for these different approaches remains to be developed. One common option is the nonparametric bootstrap, which can provide valid inference when closed-form variance estimators are not available. However, there is no consensus on how to implement MI and nonparametric bootstrapping in analyses. To complement Nguyen and Stuart's article on MI and propensity score analyses, we review some currently available approaches on variance estimation with MI and nonparametric bootstrapping., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: In aging societies, the prevalence of chronic kidney disease (CKD) is expected to increase but may be underestimated because many asymptomatic patients remain undiagnosed. This study aimed to estimate the CKD prevalence among the general older population in Japan., Methods: This cross-sectional study used health screening data from the Japan Health Insurance Association collected between April 2014 and March 2023. Data from older people aged 65-90 years who underwent renal function screening for estimated glomerular filtration rate (eGFR) and urine protein tests were analyzed. CKD was defined as eGFR < 60 mL/min/1.73 m 2 or proteinuria ≥ 1 + . Inverse probability weighting was used to account for the selection bias. The variables used for weighting were age, sex, insurance status, and the number of previous screenings., Results: Among 2.98 million older individuals, 588,809 (19.7%) had undergone screening (median [IQR] age, 69.9 [67.9-76.2] years, 337,862 women [57.4%]). Regarding the weighted CKD prevalence, 25.3% of the individuals aged 65-90 years had CKD; 11.8% of those aged 65-75 years and 34.6% of those aged 75 years and over showed an increase in prevalence with age. Among the patients with CKD, over half exhibited mild renal dysfunction without proteinuria. Hypertension and diabetes were common comorbidities in older patients with CKD., Conclusions: This cross-sectional study revealed that the weighted prevalence of CKD in the older population aged 65-90 years was high (one in four individuals), indicating that it increases with age. Further studies are required to examine the clinical significance of these findings., (© 2024. The Author(s).)

Background: The purpose of this study was to provide a detailed evaluation of the oncological advantages of surgery following neoadjuvant chemotherapy (NAC) for patients with borderline resectable (BR) or unresectable (UR) pancreatic ductal adenocarcinoma (PDAC), with a focus on minimizing biases. Recently, NAC has become the standard care for BR or UR locally advanced (UR-LA) PDAC, however, many studies have assessed survival benefits and favorable variables without consideration for biases, particularly immortal time bias., Patients and Methods: This study included patients diagnosed with BR or UR-LA PDAC at Juntendo University Hospital from 2019 to 2022. To mitigate bias, we applied methods such as propensity score matching (PSM), time-dependent covariate Cox proportional hazard regression analysis (TDC), landmark analysis, and multivariable Cox proportional hazards regression model., Results: The study analyzed 124 patients, dividing them into a surgery group (n = 57) and a chemotherapy-only group (n = 67). After PSM, there were 21 matched pairs. Survival analysis using TDC analysis showed that the surgery group had significantly better overall survival compared with the chemotherapy-only group in both the entire cohort and the matched pairs. Cox regression analysis of the entire cohort also revealed a similar superiority of surgery, while the landmark analysis showed varying results depending on the landmark setting., Conclusions: After careful adjustment for selection and immortal time biases, surgery following NAC appears to significantly extend survival in patients with BR or UR PDAC., (© 2024. Society of Surgical Oncology.)

Pharmacoepidemiological studies provide important information on the safety and effectiveness of medications, but the validity of study findings can be threatened by residual bias. Ideally, biases would be minimized through appropriate study design and statistical analysis methods. However, residual biases can remain, for example, due to unmeasured confounders, measurement error, or selection into the study. A group of sensitivity analysis methods, termed quantitative bias analyses, are available to assess, quantitatively and transparently, the robustness of study results to these residual biases. These approaches include methods to quantify how the estimated effect would be altered under specified assumptions about the potential bias, and methods to calculate bounds on effect estimates. This article introduces quantitative bias analyses for unmeasured confounding, misclassification, and selection bias, with a focus on their relevance and application to pharmacoepidemiological studies., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Background: Randomized controlled trials are the gold standard for determining treatment efficacy in medicine. To deter harmful practices such as p-hacking and hypothesizing after the results are known, any analysis of subgroups and secondary outcomes must be documented and pre-specified. However, they can still introduce bias (and routinely do) if they are not treated with the same consideration as the primary analysis., Methods: We describe several sources of bias that affect subgroup and secondary outcome analyses using published randomized trials and causal directed acyclic graphs (DAGs)., Results: We use the RECOVERY and START trials to elucidate sources of bias in analyses of subgroups and secondary outcomes. Chance imbalance can occur if the distribution of prognostic variables is not sought for any given subgroup analysis as for the main analysis. This differential distribution of prognostic variables can also occur in analyses of secondary outcomes. Selection bias can occur if the subgroup variable is causally related to staying in the trial. Given loss to follow up is not normally addressed in subgroups, attrition bias can pass unnoticed in these cases. In every case, the solution is to take the same considerations for these analyses as we do for primary analyses., Conclusions: Approval of treatments and clinical decisions can occur based on results from subgroup or secondary outcome analyses. Thus, it is important to give them the same treatment as primary analyses to avoid preventable biases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objective: We aimed to demonstrate the use of quantitative bias analysis (QBA), which reveals the effects of systematic error, including confounding, misclassification and selection bias, on study results in epidemiological studies published in the period from 2010 to mid-23., Method: The articles identified through a keyword search using Pubmed and Scopus were included in the study. The articles obtained from this search were eliminated according to the exclusion criteria, and the articles in which QBA analysis was applied were included in the detailed evaluation., Results: It can be said that the application of QBA analysis has gradually increased over the 13-year period. Accordingly, the number of articles in which simple is used as a method in QBA analysis is 9 (9.89%), the number of articles in which the multidimensional approach is used is 10 (10.99%), the number of articles in which the probabilistic approach is used is 60 (65.93%) and the number of articles in which the method is not specified is 12 (13.19%). The number of articles with misclassification bias model is 44 (48.35%), the number of articles with uncontrolled confounder(s) bias model is 32 (35.16%), the number of articles with selection bias model is 7 (7.69%) and the number of articles using more than one bias model is 8 (8.79%). Of the 49 (53.85%) articles in which the bias parameter source was specified, 19 (38.78%) used internal validation, 26 (53.06%) used external validation and 4 (8.16%) used educated guess, data constraints and hypothetical data. Probabilistic approach was used as a bias method in 60 (65.93%) of the articles, and mostly beta (8 [13.33%)], normal (9 [15.00%]) and uniform (8 [13.33%]) distributions were selected., Conclusion: The application of QBA is rare in the literature but is increasing over time. Future researchers should include detailed analyzes such as QBA analysis to obtain inferences with higher evidence value, taking into account systematic errors., (© 2024 John Wiley & Sons Ltd.)

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists' mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists' sampling effort is focused more in Barcelona's lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city's upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists' sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide., (© 2024. The Author(s).)

Background: The actual status of comprehensive genomic profiling (CGP) applications in Japan has not been clarified. We conducted a multicenter study to investigate the real-world application of CGP in gynecological malignancies., Methods: Nine designated cancer hospitals participated in this study. Patients who underwent CGP in 2020-2021 were assigned to the CGP group (n = 134). For the population that would have been eligible for CGP, patients who received initial treatment in 2015-2016 and were either alive with disease or died of disease at 5 years follow up were included in the control group (n = 316). We compared clinicopathological characteristics including tumor type (cervix, corpus, ovary, and others including sarcoma) and age. We also investigated the context of CGP-recommended treatment., Results: The CGP group had significantly fewer cervical cases and more others cases (cervix/corpus/ovary/others: CGP, 22/44/56/12; control, 89/79/142/6; p = 0.0003). The CGP group was significantly younger than the control group (median: CGP, 54.0; control, 65.0; p < 0.0001). Subgroup analyses revealed that patients with cervical and ovarian cancers were significantly younger in the CGP group. Among the CGP group, 17 patients (12.7%) received CGP-recommended treatments, 15 of which were not covered by public insurance. The survival time after CGP in 17 patients was longer than in the other 117 cases (median 21 vs. 11 months)., Conclusion: There was significant selection bias in tumor type and age for the application of CGP for gynecological malignancies in clinical practice in Japan. While CGP often recommended drugs not covered by public insurance, prognosis can be improved by use of CGP., (© 2024. The Author(s) under exclusive licence to Japan Society of Clinical Oncology.)

Objectives: Our objectives were to examine the following: physician survey response rates across a 20-year period; the impact of a token incentive on response rates; whether survey nonresponse bias is present and if it is associated with response rate; and the impact of a token incentive on nonresponse bias., Methods: We utilized data from 68 American Academy of Pediatrics (AAP) pediatrician surveys from 2000 to 2019 and an AAP administrative database, which included information for both respondents and non-respondents (target sample). Linear regression examined response rates over time. To assess nonresponse bias, a t-test or Wilcoxon rank test compared respondents and the target sample by age, gender, and US Census region. Linear regression or Spearman correlation examined the association of response rate and nonresponse bias. Interrupted time series analyses tested the introduction of a $2 token incentive on both response rates and nonresponse bias., Results: Overall mean survey response rate was 56.2%. Response rates declined across survey years (β = -0.58, P < 0.001). The $2 incentive generated an 8.7% response rate increase (P < 0.001). The respondent groups had more female pediatricians than the target samples (62.5% vs 60.1%, P < 0.001). Age nonresponse bias was associated with lower response rates (β = -0.47, P < 0.001). The $2 incentive was associated with nonresponse bias shifts toward older, away from female, and away from Northeastern respondents., Conclusions: Our study demonstrates an overall decline in pediatrician survey response rates and a meaningful impact of a token incentive on response rates and nonresponse bias, underscoring the importance of measuring nonresponse bias whenever possible., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.)

The recommender system (RS) has been widely adopted in many applications, including online advertisements. Predicting the conversion rate (CVR) can help in evaluating the effects of advertisements on users and capturing users' features, playing an important role in RS. In real-world scenarios, implicit rather than explicit feedback data are more abundant. Thus, directly training the RS with collected data may lead to suboptimal performance due to selection bias inherited from the nature of implicit feedback. Methods such as reweighting have been proposed to tackle selection bias; however, these methods omit delayed feedback, which often occurs due to limited observation times. We propose a novel likelihood approach combining the assumed parametric model for delayed feedback and the reweighting method to address selection bias. Specifically, the proposed methods minimize the likelihood-based loss using the multi-task learning method. The proposed methods are evaluated on the real-world Coat and Yahoo datasets. The proposed methods improve the AUC by 5.7% on Coat and 3.7% on Yahoo compared with the best baseline models. The proposed methods successfully debias the CVR prediction model in the presence of delayed implicit feedback.

Selection bias in clinical trials is a form of systematic error and may be detected using the I² test with a 0/>0% threshold (bias: I² > 0%, no bias: I² = 0%). The test operates on the premise that effective randomisation eliminates in-between study heterogeneity beyond the play of chance in a baseline variable meta-analysis of all the trial's baseline variables. Since the I² statistic was originally designed to measure in-between study heterogeneity in meta-analyses, the test requires the generation of at least two simulated comparator trials (SCTs). During this process, three parameters are set: SCT sample size (N SCT ), the minimum-maximum range of random values (R SCT ), and the number of generated SCTs to be used (SCT N ). Each of these parameters influences the 0/>0% threshold of the resulting I² point estimate, thereby affecting the test's sensitivity in indicating a positive result. The purpose of this technical report is to highlight the effect that SCT parameters have on the test's accuracy and to recommend appropriate parameter settings., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Mickenautsch et al.)

This short note is a commentary on the paper by Mathur and Shpitser (2024), with the aim to enlarge the class of graphs for which the conditional Average Treatment Effect is nonparametrically identified, by allowing the outcome to be on the pathway between the treatment and the selection indicator. A first straightforward generalization is possible when (i) the outcome Y is binary, and (ii) the population prevalence of Y is known a priori, or can be made the object of a sensitivity analysis. Furthermore, identification of the effect is possible also for Y having any nature, provided that a selection bias breaking node  V exists and the population prevalence of V is known., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Importance: Median organ waiting times published by transplant organizations may be biased when not appropriately accounting for censoring, death, and competing events. This can lead to overly optimistic waiting times for all transplant programs and, consequently, may deceive patients on the waiting list, transplant physicians, and health care policymakers., Objective: To apply competing-risk multistate models to calculate probabilities for transplantation and adverse outcomes on the Swiss national transplant waiting list., Design, Setting, and Participants: The WAIT (Waitlist Analysis in Transplantation) study was a retrospective cohort study of all transplant candidates in Switzerland listed from January 1, 2018, or later and observed until December 31, 2023. Transplant candidates were listed in 1 of the 6 transplant centers (Basel, Bern, Geneva, Lausanne, St Gallen, and Zurich) for heart, liver, lungs, kidney, or pancreas and/or islet transplant. A total of 4352 candidates were listed during the study period, of whom 709 (16.3%) were excluded due to living-donor transplant (691 in the kidney program and 18 in the liver program)., Exposure: Waiting for organ transplant., Main Outcomes and Measures: Time to transplantation, death, or delisting. Competing-risk multistate models were used to analyze time-to-event data from the national organ waiting list with the Aalen-Johansen estimator to compute probabilities for both transplant and adverse outcomes. Results were compared with the sample median among only those undergoing transplant and the Kaplan-Meier method with censoring of competing events., Results: Data from 3643 transplant candidates (2428 [66.6%] male; median age, 56 [range, 0-79] years) were included in the analysis. The median time to transplantation (MTT) was 0.91 (95% CI, 0.83-1.07) years for heart, 3.10 (95% CI, 2.57-3.77) years for kidney, 1.32 (95% CI, 0.76-1.55) years for liver, 0.80 (95% CI, 0.37-1.12) years for lung, and 1.62 (95% CI, 0.91-2.17) years for pancreas and/or islet programs. Alternative estimation methods introduced bias to varying degrees: the sample median among only persons undergoing transplantation underestimated the waiting time by 38% to 61% and the Kaplan-Meier method by 2% to 12% compared with the MTT., Conclusions and Relevance: In this cohort study of transplant candidates in Switzerland, the MTT, the duration at which the transplant probability is 0.50, was used as a measure of average waiting time. Suboptimal methods led to biased and overly optimistic waiting time estimations; thus, applying appropriate competing-risk methods to address censoring and competing events is crucial.

Socioeconomic factors influence the survival of patients with early stage melanoma and thus should be accounted for in prognostication tools.

Background: Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively., Aims: The objective of this study is to propose a two-step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms., Materials and Methods: An estimator of causal effects related to the two mechanisms is constructed. A two-step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice., Results: A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real-world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated., Conclusion: This  study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit patients selectively, which may improve efficiency in downstream analyses. In this paper we propose a two-phase sampling design that leverages available information on auxiliary covariates in EHR data. A key challenge in using EHR data for multiphase sampling is the potential selection bias, because EHR data are not necessarily representative of the target population. Extending existing literature on two-phase sampling design, we derive an optimal two-phase sampling method that improves efficiency over random sampling while accounting for the potential selection bias in EHR data. We demonstrate the efficiency gain from our sampling design via simulation studies and an application evaluating the prevalence of hypertension among U.S. adults leveraging data from the Michigan Genomics Initiative, a longitudinal biorepository in Michigan Medicine.

Objectives: Quantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of QBA methods for summary-level data published in the peer-reviewed literature., Study Design and Setting: We searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was preregistered on the Open Science Framework (https://osf.io/ue6vm/)., Results: Our search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary-level data. Of the 57 QBA methods, 53 (93%) were explicitly designed for observational studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 19 (33%) misclassification bias, 6 (11%) selection bias, and 3 (5%) multiple biases. Thirty-eight (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. Twenty-two (39%) articles provided code or online tools to implement the QBA methods., Conclusion: In this systematic review, we identified a total of 57 QBA methods for summary-level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary-level epidemiologic data., Plain Language Summary: Quantitative bias analysis (QBA) methods can be used to evaluate the impact of biases on observational study results. However, little is known about the full range and characteristics of available methods in the peer-reviewed literature that can be used to conduct QBA using information reported in manuscripts and other publicly available sources without requiring the raw data from a study. In this systematic review, we identified 57 QBA methods for summary-level data from observational studies. Overall, there were 29 methods that addressed unmeasured confounding, 19 that addressed misclassification bias, six that addressed selection bias, and three that addressed multiple biases. This systematic review may help future investigators identify different QBA methods for summary-level data., Competing Interests: Declaration of competing interest In the past 36 months, T.L.L. served as a member of the Amgen Methods Advisory Council, for which he received consulting fees and travel support. J.S.R. reported receiving grants from the US Food and Drug Administration; Johnson and Johnson; Medical Device Innovation Consortium; Agency for Healthcare Research and Quality; National Heart, Lung, and Blood Institute; and Arnold Ventures outside the submitted work and is also is an expert witness at the request of relator attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen Inc. that was settled in September 2022. M.M.J reported receiving grants from the US Food and Drug Administration; Agency for Healthcare Research and Quality; National Heart, Lung, and Blood Institute; National Center for Advancing Translational Sciences; National Institute on Drug Abuse; and American Cancer Society. J.D.W. is supported by Arnold Ventures, Johnson & Johnson through the Yale Open Data Access project, and the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under award 1K01AA028258 and previously served as a consultant to Hagens Berman Sobol Shapiro LLP and Dugan Law Firm APLC., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: This study compared the United States and China in examining the predictors of community-based residential relocation and its associated mortality risk., Methods: Data from the 2010 to 2018 US Health and Retirement Study and from 2011 to 2018 China Health and Retirement Longitudinal Study were used (N US = 20,292 and N China = 11,694). Community-dwelling respondents (aged 50+) reported whether they had relocated and were followed up until 2018. Log-binomial regression and Cox survival analysis were used., Results: In both countries, younger age, higher education, urban residence, and being a renter were associated with higher likelihood of relocation. Community-based relocation was associated with a lower mortality risk (US: HR = 0.63, 95% CI = 0.57, 0.70; China: HR = 0.40, 95% CI = 0.31, 0.50), and this association was significantly stronger in China compared to the United States., Discussion: Common predictors of community-based relocation were found in the United States and China. The relocation-related survival advantages may be attributed to a better post-move adaptation and living environment., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

This study investigated the effectiveness of quitline service intensity (high vs. low) on past 30-day tobacco abstinence at 7-months follow-up, using observational data from the Oklahoma Tobacco Helpline (OTH) between April 2020 and December 2021. To assess the impact of loss to follow-up and non-random treatment assignment, we fit the parameters of a marginal structural model to estimate inverse probability weights for censoring (IPCW) and treatment (IPTW) and combined (IPCTW). The Risk Ratio (RR) was estimated using modified Poisson regression with robust variance estimator. Of the 4,695 individuals included in the study, 64% received high-intensity cessation services, and 53% were lost to follow-up. Using the conventional complete case analysis (responders only), high-intensity cessation services were associated with abstinence (RR=1.18; 95 CI: 1.04, 1.34). The effect estimate was attenuated after accounting for censoring (RR=1.14; 95% CI: 1.00, 1.30). After adjusting for both baseline confounding and selection bias via IPTCW, high-intensity cessation services were associated with 1.23 times (95% CI: 1.08, 1.41) the probability of abstinence compared to low-intensity services. Despite relatively high loss to follow-up, accounting for selection bias and confounding did not notably impact quit rates or the relationship between intensity of quitline services and tobacco cessation among OTH participants., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Animal attrition in preclinical experiments can introduce bias in the estimation of causal treatment effects, as the treatment-outcome association in surviving animals may not represent the causal effect of interest. This can compromise the internal validity of the study despite randomization at the outset. Directed Acyclic Graphs (DAGs) are useful tools to transparently visualize assumptions about the causal structure underlying observed data. By illustrating relationships between relevant variables, DAGs enable the detection of even less intuitive biases, and can thereby inform strategies for their mitigation. In this study, we present an illustrative causal model for preclinical stroke research, in which animal attrition induces a specific type of selection bias (i.e., collider stratification bias) due to the interplay of animal welfare, initial disease severity and negative side effects of treatment. Even when the treatment had no causal effect, our simulations revealed substantial bias across different scenarios. We show how researchers can detect and potentially mitigate this bias in the analysis phase, even when only data from surviving animals are available, if knowledge of the underlying causal process that gave rise to the data is available. Collider stratification bias should be a concern in preclinical animal studies with severe side effects and high post-randomization attrition., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: AC was partly supported by a grant from the Stiftung Charité (granted to TK). HGK was partly supported by a grant from the Stiftung Charité (granted to TK) and from the Swedish Research Council (Vetenskapsrådet). TK reports outside the submitted work to have received research grants from the Gemeinsamer Bundesausschuss (G-BA – Federal Joint Committee, Germany), the Bundesministerium für Gesundheit (BMG – Federal Ministry of Health, Germany). He further has received personal compensation from Eli Lilly and Company, The BMJ, and Frontiers. MP reports having received partial funding for a self-initiated research project from Novartis Pharma and being awarded a research grant from the Center for Stroke Research Berlin (private donations), both outside of the submitted work. JLR reports having received a grant from Novartis Pharma for conducting a self-initiated research project about migraine outside of the submitted work.

Background: Disease latency is defined as the time from disease initiation to disease diagnosis. Disease latency bias (DLB) can arise in epidemiological studies that examine latent outcomes, since the exact timing of the disease inception is unknown and might occur before exposure initiation, potentially leading to bias. Although DLB can affect epidemiological studies that examine different types of chronic disease (e.g. Alzheimer's disease, cancer etc), the manner by which DLB can introduce bias into these studies has not been previously elucidated. Information on the specific types of bias, and their structure, that can arise secondary to DLB is critical for researchers, to enable better understanding and control for DLB., Development: Here we describe four scenarios by which DLB can introduce bias (through different structures) into epidemiological studies that address latent outcomes, using directed acyclic graphs (DAGs). We also discuss potential strategies to better understand, examine and control for DLB in these studies., Application: Using causal diagrams, we show that disease latency bias can affect results of epidemiological studies through: (i) unmeasured confounding; (ii) reverse causality; (iii) selection bias; (iv) bias through a mediator., Conclusion: Disease latency bias is an important bias that can affect a number of epidemiological studies that address latent outcomes. Causal diagrams can assist researchers better identify and control for this bias., (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Selection bias has long been central in methodological discussions across epidemiology and other fields. In epidemiology, the concept of selection bias has been continually evolving over time. In this issue of the Journal, Mathur and Shpitser (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) present simple graphical rules for using a Single World Intervention Graph (SWIG) to assess the presence of selection bias when estimating treatment effects in both the general population and a selected sample. Notably, the authors examine the setting in which the treatment affects selection, an issue not well-addressed in the existing literature on selection bias. To place the work by Mathur and Shpitser in context, we review the evolution of the concept of selection bias in epidemiology, with a primary focus on the developments in the last 20-30 years since the introduction of causal directed acyclic graphs (DAGs) to epidemiologic research., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Threat mapping is a necessary tool for identifying and abating direct threats to species in the ongoing extinction crisis. There are known gaps in the threat mapping literature for particular threats and geographic locations, and it remains unclear if the distribution of research effort is appropriately targeted relative to conservation need. We aimed to determine the drivers of threat mapping research effort and to quantify gaps that, if filled, could inform actions with the highest potential to reduce species' extinction risk. We used a negative binomial generalized linear model to analyze research effort as a function of threat abatement potential (quantified as the potential reduction in species extinction risk from abating threats), species richness, land area, and human pressure. The model showed that threat mapping research effort increased by 1.1 to 1.2 times per standardized unit change in threat abatement potential. However, species richness and land area were stronger predictors of research effort overall. The greatest areas of mismatch between research effort and threat abatement potential, receiving disproportionately low research effort, were related to the threats to species of agriculture, aquaculture, and biological resource use across the tropical regions of the Americas, Asia, and Madagascar. Conversely, the threat of linear infrastructure (e.g., roads and rails) across regions, the threat of biological resource use (e.g., hunting or collection) in sub-Saharan Africa, and overall threats in North America and Europe all received disproportionately high research effort. We discuss the range of methodological and sociopolitical factors that may be behind the overall trends and specific areas of mismatch we found. We urge a stronger emphasis on targeting research effort toward those threats and geographic locations where threat abatement activities could make the greatest contribution to reducing global species extinction risk., (© 2024 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

The HOPE cohort is a Danish nationwide cohort with ongoing follow-up, holding information on postpartum depression (PPD) symptoms and diagnoses on 170,218 childbirths (142,795 unique mothers). These data have been linked with extensive register data on health and socioeconomic information on the mothers, their partners, parents, and children. This cohort profile aimed to provide an overview of the data collection and content, describe characteristics, and evaluate potential selection bias. PPD screenings, using the Edinburgh Postnatal Depression Scale, were collected from 67 of the 98 Danish municipalities, covering the period January 2015 to December 2021. This data was linked with register data on PPD diagnoses (identified through medication prescriptions and hospital contacts) as well as background information. Cohort characteristics were compared to the source population, defined as all childbirths by women residing in Denmark during the same period (452,207 childbirths). Potential selection bias was evaluated by comparing odds ratios of five well-established associations between the cohort and the source population. The HOPE cohort holds information on 170,218 childbirths (38% of the source population) involving 142,795 unique mothers. The HOPE cohort only differed slightly from the source population on most characteristics examined, but larger differences were observed on specific characteristics with an underrepresentation of the youngest and oldest age groups, women with more than three children or twins/triplets, and women born outside Denmark. Similar associations were identified across the two populations within the five well-established associations. There was no indication of selection bias on the five examined associations, and the HOPE cohort is representative of the source population on important perinatal characteristics., (© 2024. The Author(s).)

Background: In population-based research, pregnancy may be a repeated event. Despite published guidance on how to address repeated pregnancies to the same individual, a variety of approaches are observed in perinatal epidemiological studies. While some of these approaches are supported by the chosen research question, others are consequences of constraints inherent to a given dataset (eg, missing parity information). These decisions determine how appropriately a given research question can be answered and overall generalizability., Objective: To compare common cohort selection and analytic approaches used for perinatal epidemiological research by assessing the prevalence of two perinatal outcomes and their association with a clinical and a social independent variable., Study Design: Using vital records linked to maternal hospital discharge records for singleton births, we created four cohorts: (1) all-births (2) randomly selected one birth per individual (3) first-observed birth per individual (4) primiparous-births (parity 1). Sampling of births was not conditional on cluster (ie, we did not sample all births by a given mother, but rather sampled individual births). Study outcomes were severe maternal morbidity (SMM) and preeclampsia/eclampsia, and the independent variables were self-reported race/ethnicity (as a social factor) and systemic lupus erythematosus. Comparing the four cohorts, we assessed the distribution of maternal characteristics, the prevalence of outcomes, overall and stratified by parity, and risk ratios (RR) for the associations of outcomes with independent variables. Among all-births, we then compared RR from three analytic strategies: with standard inference that assumes independently sampled births to the same mother in the model, with cluster-robust inference, and adjusting for parity., Results: We observed minor differences in the population characteristics between the all-birth (N=2736,693), random-selection, and first-observed birth cohorts (both N=2284,660), with more substantial differences between these cohorts and the primiparous-births cohort (N=1054,684). Outcome prevalence was consistently lowest among all-births and highest among primiparous-births (eg, SMM 18.9 per 1000 births among primiparous-births vs 16.6 per 1000 births among all-births). When stratified by parity, outcome prevalence was always the lowest in births of parity 2 and highest among births of parity 1 for both outcomes. RR differed for study outcomes across all four cohorts, with the most pronounced differences between the primiparous-birth cohort and other cohorts. Among all-births, robust inference minimally impacted the confidence bounds of estimates, compared to the standard inference, that is, crude estimates (eg, lupus-SMM association: 4.01, 95% confidence intervals [CI] 3.54-4.55 vs 4.01, 95% CI 3.53-4.56 for crude estimate), while adjusting for parity slightly shifted estimates, toward the null for SMM and away from the null for preeclampsia/eclampsia., Conclusion: Researchers should consider the alignment between the methods they use, their sampling strategy, and their research question. This could include refining the research question to better match inference possible for available data, considering alternative data sources, and appropriately noting data limitations and resulting bias, as well as the generalizability of findings. If parity is an established effect modifier, stratified results should be presented., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification test for the exclusion restriction assumption. Using simulations, we explored the statistical power of the randomization test to detect an association between a genetic instrument and a covariate set due to (a) selection bias or (b) horizontal pleiotropy, compared to three approaches examining associations with individual covariates: (i) Bonferroni correction for the number of covariates, (ii) correction for the effective number of independent covariates, and (iii) an r 2 permutation-based approach. We conducted proof-of-principle analyses in UK Biobank, using CRP as the exposure and coronary heart disease (CHD) as the outcome. In simulations, power of the randomization test was higher than the other approaches for detecting selection bias when the correlation between the covariates was low (r 2  < 0.1), and at least as powerful as the other approaches across all simulated horizontal pleiotropy scenarios. In our applied example, we found strong evidence of selection bias using all approaches (e.g., global randomization test p < 0.002). We identified 51 of the 58 CRP genetic variants as horizontally pleiotropic, and estimated effects of CRP on CHD attenuated somewhat to the null when excluding these from the genetic risk score (OR = 0.96 [95% CI: 0.92, 1.00] versus 0.97 [95% CI: 0.90, 1.05] per 1-unit higher log CRP levels). The global randomization test can be a useful addition to the MR researcher's toolkit., (© 2024. The Author(s).)

Associations of anticoagulation with primary endpoints in longitudinal studies are impacted by selection bias and time-varying covariates (e.g. comorbidities). We demonstrate how time-varying covariates and selection bias influence association estimates between anticoagulation and health-related quality of life (HRQoL) in patients with atrial fibrillation. We performed a secondary analysis of the Atrial Fibrillation Follow-up Investigation of Rhythm Management trial quality of life substudy. Dichotomized warfarin use was ascertained at the study baseline, 2 months later, and annually for up to 6 years. HRQoL was measured at every time point using a self-reported ordinal 5-point Likert-scale (lower score and lower odds ratio represents better health-related quality of life). Static and time-varying covariates were ascertained throughout the study period. Confounder-adjusted generalized mixed model and generalized estimating equation regressions were used to demonstrate traditional association estimates between anticoagulation and HRQoL. Inverse probability of treatment and censorship weights were used to ascertain the influence of time-varying confounding and selection bias. Age-stratified analysis (age ≥ 70 years) evaluated for effect modification. 656 individuals were included in the analysis, 601 on warfarin at baseline. The association of warfarin use with better HRQoL over time strengthened when accounting for time-varying confounding and selection bias (OR 0.30, 95% CI 0.14-0.55) compared to traditional analyses (OR 0.61, 95% CI 0.38-0.97), and was most pronounced in those ≥ 70 years upon stratified analysis. Anticoagulation is associated with higher HRQoL in patients with atrial fibrillation, with time-varying confounding and selection bias likely influencing longitudinal estimates in anticoagulation-HRQoL research., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: Certain associations observed in the National Birth Defects Prevention Study (NBDPS) contrasted with other research or were from areas with mixed findings, including no decrease in odds of spina bifida with periconceptional folic acid supplementation, moderately increased cleft palate odds with ondansetron use and reduced hypospadias odds with maternal smoking., Objectives: To investigate the plausibility and extent of differential participation to produce effect estimates observed in NBDPS., Methods: We searched the literature for factors related to these exposures and participation and conducted deterministic quantitative bias analyses. We estimated case-control participation and expected exposure prevalence based on internal and external reports, respectively. For the folic acid-spina bifida and ondansetron-cleft palate analyses, we hypothesized the true odds ratio (OR) based on prior studies and quantified the degree of exposure over- (or under-) representation to produce the crude OR (cOR) in NBDPS. For the smoking-hypospadias analysis, we estimated the extent of selection bias needed to nullify the association as well as the maximum potential harmful OR., Results: Under our assumptions (participation, exposure prevalence, true OR), there was overrepresentation of folic acid use and underrepresentation of ondansetron use and smoking among participants. Folic acid-exposed spina bifida cases would need to have been ≥1.2× more likely to participate than exposed controls to yield the observed null cOR. Ondansetron-exposed cleft palate cases would need to have been 1.6× more likely to participate than exposed controls if the true OR is null. Smoking-exposed hypospadias cases would need to have been ≥1.2 times less likely to participate than exposed controls for the association to falsely appear protective (upper bound of selection bias adjusted smoking-hypospadias OR = 2.02)., Conclusions: Differential participation could partly explain certain associations observed in NBDPS, but questions remain about why. Potential impacts of other systematic errors (e.g. exposure misclassification) could be informed by additional research., (© 2023 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: A prior study suggested that implementing a cut-off value of ≤30 mm for a short cervical length (CL) could potentially introduce selection bias and alter the distribution of CL measurements. As such, the objective of this study is to evaluate how CL distribution and incidence of short CL are affected when using different cut-off values for a short CL., Study Design: This is a secondary analysis of the Quadruple P (QP) Screening study; a prospective cohort study that included low-risk patients with singleton pregnancies undergoing fetal anomaly scan at 18-22 weeks of gestation, including a CL measurement. Patients with a short cervix, defined as ≤35 mm, were subsequently counseled for the QP trial; a randomized controlled trial (RCT) comparing progesterone to cervical pessary for the prevention of preterm birth. If participation to the RCT was refused, patients with a CL ≤25 mm were advised to use progestogen. The primary objective of this current study was to assess the normal distribution of CL across the entire cohort and to assess the incidence of short CL when using the cut-off values of ≤35 and ≤25 mm. Normal distributions for CL were simulated based on mean and standard deviation(SD) of the original data. The Kolmogorov-Smirnov test was used to evaluate the distribution of the CL measurements. Moreover, to evaluate the motives behind ultrasound measurements around the cut-off value, sonographers were asked to fill out a qualitative questionnaire., Results: The total cohort included 19.171 eligible participants who underwent CL measurement, with a mean CL of 43.9 mm (±8.1 SD). The distribution of all CL observed measurements deviated significantly from the normal distribution (p < 0.001). A total of 1.852 (9.7%) patients had short CL ≤35 mm, which was significantly lower than expected when compared to the simulated normal distribution (n = 2.661, 13.9%; p < 0.001). The incidence of short CL ≤25 mm in our cohort statistically differed from the simulated normal distribution (238, 1.2% vs 177, 0.9%; p=0.003). When comparing our data to the simulated normal distribution, the difference in distributions is most pronounced when examining the difference between 35 and 36 mm. Results of the questionnaire reveal sonographers claimed not to be influenced by a cut-off value for study participation or progesterone treatment., Conclusion: This study demonstrates that using any cut-off value for a short CL influences the incidence and distribution of CL. When using a cut-off value of ≤35 mm for study inclusion, the incidence of measurements of a short CL is lower than the anticipated incidence compared to a normal distribution. However, when using a cut-off value of ≤25 mm for progesterone treatment, the frequency of CL measurements is higher than expected below this threshold compared to a normal distribution. This study highlights the risk of introducing selection bias, most likely unintentionally, when cut-off values for short CL are used, regardless of the specific value chosen. Therefore healthcare providers should measure the CL with caution if essential decisions depend on a specific cut-off value., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Observational studies are rarely representative of their target population because there are known and unknown factors that affect an individual's choice to participate (the selection mechanism). Selection can cause bias in a given analysis if the outcome is related to selection (conditional on the other variables in the model). Detecting and adjusting for selection bias in practice typically requires access to data on nonselected individuals. Here, we propose methods to detect selection bias in genetic studies by comparing correlations among genetic variants in the selected sample to those expected under no selection. We examine the use of four hypothesis tests to identify induced associations between genetic variants in the selected sample. We evaluate these approaches in Monte Carlo simulations. Finally, we use these approaches in an applied example using data from the UK Biobank (UKBB). The proposed tests suggested an association between alcohol consumption and selection into UKBB. Hence, UKBB analyses with alcohol consumption as the exposure or outcome may be biased by this selection., (© 2024 The Author(s). Genetic Epidemiology published by Wiley Periodicals LLC.)

In a multi-center randomized controlled trial (RCT) with competitive recruitment, eligible patients are enrolled sequentially by different study centers and are randomized to treatment groups using the chosen randomization method. Given the stochastic nature of the recruitment process, some centers may enroll more patients than others, and in some instances, a center may enroll multiple patients in a row, for example, on a given day. If the study is open-label, the investigators might be able to make intelligent guesses on upcoming treatment assignments in the randomization sequence, even if the trial is centrally randomized and not stratified by center. In this paper, we use enrollment data inspired by a real multi-center RCT to quantify the susceptibility of two restricted randomization procedures, the permuted block design and the big stick design, to selection bias under the convergence strategy of Blackwell and Hodges (1957) applied at the center level. We provide simulation evidence that the expected proportion of correct guesses may be greater than 50% (i.e., an increased risk of selection bias) and depends on the chosen randomization method and the number of study patients recruited by a given center that takes consecutive positions on the central allocation schedule. We propose some strategies for ensuring stronger encryption of the randomization sequence to mitigate the risk of selection bias., (© 2024 John Wiley & Sons Ltd.)

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual-level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness-adjusted Wald ratio, two-stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real-data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Consent bias is a type of selection bias in biomedical research where those consenting to the research differ systematically from those not consenting. It is particularly relevant in precision medicine research because the complexity of these studies prevents certain subgroups from understanding, trusting, and consenting to the research. Because consent bias distorts research findings and causes inequitable distribution of research benefits, scholars propose two types of schemes to reduce consent bias: reforming existing consent models and removing the consent requirement altogether. This study explores the possibility of waiving consent in observational studies using existing data, because they involve fewer risks to participants than clinical trials if privacy safeguards are strengthened. It suggests that data protection mechanisms such as security enhancement and data protection impact assessment should be conducted to protect data privacy of participants in observational studies without consent., (© 2024. The Author(s).)

When respiratory viruses co-circulate in a population, individuals may be infected with multiple pathogens and experience possible virus-virus interactions, where concurrent or recent prior infection with one virus affects the infection process of another virus. While experimental studies have provided convincing evidence for within-host mechanisms of virus-virus interactions, evaluating evidence for viral interference or potentiation using population-level data has proven more difficult. Recent studies have quantified the prevalence of co-detections using populations drawn from clinical settings. Here, we focus on selection bias issues associated with this study design. We provide a quantitative account of the conditions under which selection bias arises in these studies, review previous attempts to address this bias, and propose unbiased study designs with sample size estimates needed to ascertain viral interference. We show that selection bias is expected in cross-sectional co-detection prevalence studies conducted in clinical settings, except under a strict set of assumptions regarding the relative probabilities of being included in a study limited to individuals with clinical disease under different viral states. Population-wide studies that collect samples from participants irrespective of their clinical status would meanwhile require large sample sizes to be sufficiently powered to detect viral interference, suggesting that a study's timing, inclusion criteria, and the expected magnitude of interference are instrumental in determining feasibility., Competing Interests: The authors declare no conflict of interest.

In clinical trials, multiple comparisons arising from various treatments/doses, subgroups, or endpoints are common. Typically, trial teams focus on the comparison showing the largest observed treatment effect, often involving a specific treatment pair and endpoint within a subgroup. These findings frequently lead to follow-up pivotal studies, many of which do not confirm the initial positive results. Selection bias occurs when the most promising treatment, subgroup, or endpoint is chosen for further development, potentially skewing subsequent investigations. Such bias can be defined as the deviation in the observed treatment effects from the underlying truth. In this article, we propose a general and unified Bayesian framework to address selection bias in clinical trials with multiple comparisons. Our approach does not require a priori specification of a parametric distribution for the prior, offering a more flexible and generalized solution. The proposed method facilitates a more accurate interpretation of clinical trial results by adjusting for such selection bias. Through simulation studies, we compared several methods and demonstrated their superior performance over the normal shrinkage estimator. We recommended the use of Bayesian Model Averaging estimator averaging over Gaussian Mixture Models as the prior distribution based on its performance and flexibility. We applied the method to a multicenter, randomized, double-blind, placebo-controlled study investigating the cardiovascular effects of dulaglutide., (© 2024 John Wiley & Sons Ltd.)

Our study aimed to establish the risk of selection bias in randomized controlled trials (RCT) that were overall rated as having "low bias" risk according to Cochrane's Risk of Bias, version 2 (RoB 2) tool. A systematic literature search of current systematic reviews of RCTs was conducted. From the identified reviews, RCTs with overall "high bias" and "low bias" RoB 2 risk ratings were extracted. All RCTs were statistically tested for selection bias risk. From the test results, true positive, true negative, false positive, or false negative ratings were established, and the false omission rate (FOR) with a 95% confidence interval (CI) was computed. Subgroup analysis was conducted by computing the negative likelihood ratio (-LR) concerning RoB 2 domain 1 ratings: bias arising from the randomization process. A total of 1070 published RCTs (median publication year: 2018; interquartile range: 2013-2020) were identified and tested. We found that 7.61% of all "low bias" (RoB 2)-rated RCTs were of high selection bias risk (FOR 7.61%; 95% CI: 6.31%-9.14%) and that the likelihood for high selection bias risk in "low bias" (RoB 2 domain 1)-rated RCTs was 6% higher than that for low selection bias risk (-LR: 1.06; 95% CI: 0.98-1.15). These findings raise issues about the validity of "low bias" risk ratings using Cochrane's RoB 2 tool as well as about the validity of some of the results from recently published RCTs. Our results also suggest that the likelihood of a "low bias" risk-rated body of clinical evidence being actually bias-free is low, and that generalization based on a limited, pre-specified set of appraisal criteria may not justify a high level of confidence that such evidence reflects the true treatment effect., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Mickenautsch et al.)

Background: Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases., Framework: A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis., Design: Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease., Results: When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment., Conclusions: A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself., Highlights: Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased "evidence" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection., Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RCA is a full-time employee of Mathematica Inc. JHC is co-founder of Reaction Explorer LLC, which develops and licenses organic chemistry education software. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Foundation for the National Institutes of Health (R01 AI154533, LM013365, T32 LM012409), Stanford University, School of Medicine, Stanford University, WHSDM Seed Grant, AIMI-HCAI Partnership grant, SAGE center funds, Stanford Clinical Excellence Research Center funds, Stanford Graduate Fellowship.

Introduction: The PRIME-NL study prospectively evaluates a new integrated and personalized care model for people with parkinsonism, including Parkinson's disease, in a selected region (PRIME) in the Netherlands. We address the generalizability and sources of selection and confounding bias of the PRIME-NL study by examining baseline and 1-year compliance data., Methods: First, we assessed regional baseline differences between the PRIME and the usual care (UC) region using healthcare claims data of almost all people with Parkinson's disease in the Netherlands (the source population). Second, we compared our questionnaire sample to the source population to determine generalizability. Third, we investigated sources of bias by comparing the PRIME and UC questionnaire sample on baseline characteristics and 1-year compliance., Results: Baseline characteristics were similar in the PRIME (n = 1430) and UC (n = 26,250) source populations. The combined questionnaire sample (n = 920) was somewhat younger and had a slightly longer disease duration than the combined source population. Compared to the questionnaire sample in the PRIME region, the UC questionnaire sample was slightly younger, had better cognition, had a longer disease duration, had a higher educational attainment and consumed more alcohol. 1-year compliance of the questionnaire sample was higher in the UC region (96%) than in the PRIME region (92%)., Conclusion: The generalizability of the PRIME-NL study seems to be good, yet we found evidence of some selection bias. This selection bias necessitates the use of advanced statistical methods for the final evaluation of PRIME-NL, such as inverse probability weighting or propensity score matching. The PRIME-NL study provides a unique window into the validity of a large-scale care evaluation for people with a chronic disease, in this case parkinsonism., (© 2024. The Author(s).)

Background: The largest case-control study (Interphone study) investigating glioma risk related to mobile phone use showed a J-shaped relationship with reduced relative risks for moderate use and a 40% increased relative risk among the 10% heaviest regular mobile phone users, using a categorical risk model based on deciles of lifetime duration of use among ever regular users., Methods: We conducted Monte Carlo simulations examining whether the reported estimates are compatible with an assumption of no effect of mobile phone use on glioma risk when the various forms of biases present in the Interphone study are accounted for. Four scenarios of sources of error in self-reported mobile phone use were considered, along with selection bias. Input parameters used for simulations were those obtained from Interphone validation studies on reporting accuracy and from using a nonresponse questionnaire., Results: We found that the scenario simultaneously modeling systematic and random reporting errors produced a J-shaped relationship perfectly compatible with the observed relationship from the main Interphone study with a simulated spurious increased relative risk among heaviest users (odds ratio = 1.91) compared with never regular users. The main determinant for producing this J shape was higher reporting error variance in cases compared with controls, as observed in the validation studies. Selection bias contributed to the reduced risks as well., Conclusions: Some uncertainty remains, but the evidence from the present simulation study shifts the overall assessment to making it less likely that heavy mobile phone use is causally related to an increased glioma risk., Competing Interests: Disclosure: M.F. was vice chairman (2012–2020) of the International Commission on Non-Ionizing Radiation Protection, an independent body setting guidelines for nonionizing radiation protection. She has served as advisor to a number of national and international public advisory and research steering groups concerning the potential health effects of exposure to nonionizing radiation, currently for the World Health Organization. M.F. is the principal investigator of the Swedish part of the COSMOS study, which has been funded by the Swedish Research Council, AFA Insurance, the Swedish Research Council for Health, Working Life and Welfare, the Swedish Radiation Safety Authority, and VINNOVA. VINNOVA received funds for this purpose from TeliaSonera AB, Ericsson AB, and Telenor Sverige AB, to cover part of the data collection (ended 2012). The provision of funds to the COSMOS study investigators via VINNOVA was governed by agreements that guarantee COSMOS’ complete scientific independence. The remaining authors declare they have no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

We aimed to explore, in a sample of systematic reviews (SRs) with meta-analyses of the association between food/diet and health-related outcomes, whether systematic reviewers selectively included study effect estimates in meta-analyses when multiple effect estimates were available. We randomly selected SRs of food/diet and health-related outcomes published between January 2018 and June 2019. We selected the first presented meta-analysis in each review (index meta-analysis), and extracted from study reports all study effect estimates that were eligible for inclusion in the meta-analysis. We calculated the Potential Bias Index (PBI) to quantify and test for evidence of selective inclusion. The PBI ranges from 0 to 1; values above or below 0.5 suggest selective inclusion of effect estimates more or less favourable to the intervention, respectively. We also compared the index meta-analytic estimate to the median of a randomly constructed distribution of meta-analytic estimates (i.e., the estimate expected when there is no selective inclusion). Thirty-nine SRs with 312 studies were included. The estimated PBI was 0.49 (95% CI 0.42-0.55), suggesting that the selection of study effect estimates from those reported was consistent with a process of random selection. In addition, the index meta-analytic effect estimates were similar, on average, to what we would expect to see in meta-analyses generated when there was no selective inclusion. Despite this, we recommend that systematic reviewers report the methods used to select effect estimates to include in meta-analyses, which can help readers understand the risk of selective inclusion bias in the SRs., (© 2024 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)