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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. [ABSTRACT FROM AUTHOR]

The investigator in a randomized open-label single-center trial knows the treatment assignments of all randomized subjects and thus can introduce a selection bias in the study results by allocating subjects with a better prognosis when he guesses the next allocation is to the experimental group. When the allocation procedure is known to the investigator, Blackwell and Hodges demonstrated that the truncated binomial design of the size N, where subjects are allocated at random with probability ½ until one of the treatment arms has N / 2 subjects, minimizes the selection bias among all 1:1 allocation procedures that assign N / 2 subjects to each treatment. In this article we demonstrate that for any allocation space symmetric with respect to the two treatments, the selection bias is also minimized with the procedure that allocates subjects with probability ½ whenever allocation to both treatments is allowed—the big stick design. We propose to control both the balance in treatment assignments and the selection bias by specifying the allocation space with acceptable, possibly increasing with time, imbalance and using the big stick randomization within it. We show how the selection bias can be further reduced when the investigator is not aware of the allocation procedure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The iterative process of ethnography not only constructs theory, but its methodology should embody theory. Developing a theoretical framework often demands adjustments in methodology, to leverage previous work and to avoid assumptions compounding through the magnification of blind spots. New theory in policy-engaged ethnography has emphasised the importance of analysing interactions between teachers. However, such analyses have not yet emerged in research on education in Japan, partly because of enduring conceptualisations of teachers as homogeneous. Methodological tools that do exist are grounded in anglosphere data and remain unsuited to research in Japan's schools. As such, participants are positioned amongst, but not in relation to, other teachers and school administrators. Yet, taking teacher heterogeneity seriously enhances the salience of selection bias. The same teachers who are enthusiastic about participating in the research are often those who are enthusiastic about the topic of the research. What is difficult in school ethnography is procuring, and preferably ensuring, participation from teachers who are not enthusiasts toward the topic of the research. Designing research with both the heterogeneity of teachers and the specifics of the local context in mind can provide more diverse data with potential to inform analyses of micro-politics in Japan's schools. In reference to a five-year research project and grounded in data from Japan, the paper proposes some procedures to access a diverse range of teachers based on a theory developed to conceptualise teachers' orientations towards policy work. [ABSTRACT FROM AUTHOR]

We examine the plausibility of using prejudice-based discrimination to explain the wage differences among young men and women in eleven European countries. Utilizing a sample of over 20,000 individuals and estimating a series of country-specific regression models that adjust for ability and selection bias, we find evidence that leads to a good possibility of prejudice-based discrimination in Greece, Italy, Spain, Czechia, Hungary, and the UK. In the UK, this possibility of discrimination is mitigated to an extent by gender differences in credentials. In Austria, Germany, Denmark, and Switzerland, we find no evidence for prejudice-based discrimination. Oaxaca decompositions reveal that if women's own human capital characteristics were rewarded at the same level as men's in the former (six) countries, they would expect a wage gain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as Influenza, Rotavirus, Dengue fever, and more recently COVID-19. In a TND study, individuals who experience symptoms and seek care are recruited and tested for the infectious disease which defines cases and controls. Despite TND's potential to reduce unobserved differences in healthcare seeking behavior (HSB) between vaccinated and unvaccinated subjects, it remains subject to various potential biases. First, residual confounding may remain due to unobserved HSB, occupation as healthcare worker, or previous infection history. Second, because selection into the TND sample is a common consequence of infection and HSB, collider stratification bias may exist when conditioning the analysis on tested samples, which further induces confounding by latent HSB. In this article, we present a novel approach to identify and estimate vaccine effectiveness in the target population by carefully leveraging a pair of negative control exposure and outcome variables to account for potential hidden bias in TND studies. We illustrate our proposed method with extensive simulations and an application to study COVID-19 vaccine effectiveness using data from the University of Michigan Health System. for this article are available online. [ABSTRACT FROM AUTHOR]

In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of the time to event, conventional methods adjusting for left truncation tend to rely on the quasi-independence assumption that the truncation time and the event time are independent on the observed region. This assumption is violated when there is dependence between the truncation time and the event time, possibly induced by measured covariates. Inverse probability of truncation weighting can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply semiparametric theory to find the efficient influence curve of the expectation of an arbitrarily transformed survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches were developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two datasets from practice, with different right-censoring patterns. [ABSTRACT FROM AUTHOR]

Objectives: We provide a brief overview of collider bias and its implications for criminological research. Methods: Owing to the nature of the topics studied, as well as the common data sources used to carry out much of this research, work in the field may often become vulnerable to a specific methodological problem known as collider bias. Collider bias occurs when exposure variables and outcomes independently cause a third variable, and this variable is included in statistical models. Colliders represent somewhat of a paradox in that there is scholarship discussing the issue, yet it has managed to remain a relatively cryptic threat compared to other sources of bias. Results: We argue that, far from being an obscure concern, colliders almost certainly have pervasive impact in criminal justice and criminology. Conclusion: We close by offering a general set of strategies for addressing the challenges posed by collider bias. While there is no panacea, there are better practices, many of which are underutilized in the disciplines that study crime and its attendant topics. [ABSTRACT FROM AUTHOR]

We study the effects of expected initial wages, expected wage growth, and observed and unobserved heterogeneity in the choice of college major in a sample of American college graduates. We propose a three-stage empirical model that relates future earnings to individual choices. In the first stage, starting from revealed choices, observed wages, and life-cycle wage profiles, we estimate the expectation on initial wages and wage growth from the individual point of view, where the panel structure of the data allows us to produce estimates corrected for self-selection bias. We find substantial differences in expected real wages and expected real wage growth between majors and that both characteristics of life cycle earnings influence major choice. Our parametric models show a strong correlation between salary trends and major choice, whereas semiparametric models yield less reliable results. We interpret our results as being consistent with agents being rational and as a validation for our estimation strategy based on counterfactual imputation. [ABSTRACT FROM AUTHOR]

With the relaxation of the One-Child Policy in China, women are likely to face more conflicts between childcare and work. How to boost the fertility rate and facilitate the female labor supply has become an urgent issue in China. With data from the China Health and Nutrition Survey (CHNS) 1993–2015, this study explored the impact of having more than one child on women's informal employment decisions using three-equation models. The models took into account the sample selection of working-age women in the labor force and the endogeneity of fertility decisions jointly. The results showed that women with more than one child were more likely to choose informal employment. The positive impact of having more children on women's probability of informal employment was different for each group. Notably, the positive impact was stronger for women with low educational attainment, rural hukou, and especially for rural‒urban migrants. These results were robust to several alternative specifications. These findings suggest that with more children, women in China choose informal employment as a way of balancing work and family. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 r2 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 (r2 < 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. [ABSTRACT FROM AUTHOR]

BACKGROUND: Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to reconstruct the unbiased data that would be observed based on the provided assumptions of the degree of bias. METHODS: We motivate and describe the steps necessary to implement this method. We also demonstrate the validity of this method through a simulation study with an exposure-outcome relationship that is biased by uncontrolled confounding, exposure misclassification, and selection bias. RESULTS: The study revealed that a non-biased effect estimate can be obtained when correct bias parameters are applied. It also found that incorrect specification of every bias parameter by +/-25% still produced an effect estimate with less bias than the observed, biased effect. CONCLUSIONS: Simultaneous multi-bias analysis is a useful way of investigating and understanding how multiple sources of bias may affect naive effect estimates. This new method can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies.

• Using a cut-off value for a short cervix introduces selection bias and affects the normal distribution and incidence of a short cervical length. • Irrespective of the chosen cut-off value, the deviation from a normal distribution is most pronounced around this threshold. • If essential decisions depend on a cut-off value, healthcare providers should be aware of potential bias and measure with caution. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Online hotel reviews on platforms like TripAdvisor are crucial in shaping customer choices and steering business strategies in the hospitality sector. However, the effectiveness of these platforms is partially hindered by the self-selection bias found in voluntary reviews. This bias can create false expectations and unsatisfactory experiences, mainly as the feedback generally comes from a non-representative group of self-motivated reviewers (SMRs). A common strategy to mitigate this bias is increasing the number of reviews through customer surveys, generating retailer-prompted reviews (RPRs). However, these RPRs, despite reducing selection bias, tend to lack the depth and insight of SMRs, resulting in a credibility gap and reduced representativeness. To address this, our study presents a novel approach using the propensity score adjustment (PSA) technique. This method leverages the distribution of RPRs to refine the accuracy of text data from SMRs, aiming to enhance the reliability and representativeness of online reviews. By combining the strengths of both RPRs and SMRs, we aim to create an online review environment that is both accurate and reliable. In conclusion, this research marks an important step toward improving online review platforms, aiming for a more transparent and trustworthy environment for reviews. [ABSTRACT FROM AUTHOR]

Background: Incomplete data due to nonparticipation and noncompliance with the study protocol can bias the results of studies. We investigated how a nationwide accelerometer sample of adolescents and young adults is affected by such incomplete data. Methods: We analyzed cross-sectional data from 6465 participants (11–31 y old) who participated in a national health survey in Germany (KiGGS Wave 2; 2014–2017). The data included information about the participation in the measurement of physical activity using accelerometers, compliance with the wear-time protocol, and sociodemographic and health-related variables. Multivariable regression analyses were conducted to detect factors associated with incomplete data. Results: Of the total sample, 78.0% participated in the accelerometer part of the study, and 83.5% of the participants with data available complied with the wear-time protocol. In 11- to 17-year-olds, the likelihood of having incomplete accelerometer data was higher in boys, older adolescents, adolescents with a lower sociodemographic status, adolescents with overweight, adolescents not participating in organized sport, adolescents not speaking only German at home, current smokers, and adolescents having a higher soft drink consumption. In 18- to 31-year-olds, the likelihood of having incomplete accelerometer data was higher in men, adults with a lower educational level, adults not speaking only German at home, and adults who smoke. Conclusions: Our results suggest that accelerometer samples are biased such that participants with more beneficial health behaviors provide complete accelerometer data more often. This knowledge should be used to design effective recruitment strategies and should be considered when interpreting results of accelerometer studies. [ABSTRACT FROM AUTHOR]

Abstract Web-based testing of cognitive abilities allows for large-scale assessments without geographical constraints. Yet, the extent to which it can reach populations beyond the typical demographic groups recruited for cognitive studies is unclear. This study focused on comparing the characteristics of individuals from a general population study (HUNT4) who chose to participate in a cognitive study (HUNT4-Hjernetrim) with those who did not. Additionally, we investigated participants' engagement and user experience. We obtained data on socio-demographics, health conditions (both physical and mental), self-reported cognitive or learning difficulties, and lifestyle factors of Hjernetrim participants and non-participants from the HUNT4 database. Hjernetrim involved 13 cognitive tests, administered through the online platform Memoro. We used logistic regressions to assess participation biases and linear regressions to assess participants’ engagement and user experience. Of 65,851 HUNT4 participants invited via regular mail to Hjernetrim, 5634 (9.4%, aged 13–97, 54% women) participated. The best represented in the sample were 50–79-year-olds, women, tertiary educated, living alone, from urban areas, not occupationally active, and reporting memory complaints. Individuals who were aged 80+, had motor or vision impairments, and teenagers with learning disabilities, were underrepresented. Participants were more likely to have mental health problems, have or survived cancer and less likely to have cardiovascular disease. Participants logged on mainly during weekdays, the preferred time of day varied by age. On average, participants used 42 min and completed 78% of the tasks. Using PCs provided the most complete data. In terms of user experiences, 65% were positive while 14% were negative or reported technical difficulties. Overall, the study demonstrated that web-based methodology allowed for a relatively well-represented sample that included groups typically difficult to reach. The presence of somatic and mental diseases had a variable influence on participation. Participants finished most tests and reported positive experiences overall.

Abstract Background Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP. Methods Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall ‘envisioned population’ survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census. Results Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings. Conclusions Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Quantitative historical analysis must be nested in a qualitative understanding of the empirical context if it is to be credible. Bruce Bueno de Mesquita and Ethan Bueno de Mesquita's analysis of the consequences of the Investiture Controversy (1075–1122) for the development of lay political authority and economic development illustrates this. They portray the Investiture Controversy and the concordats it produced as strengthening lay rulers' power over bishop appointment, but the dominant view among historians is that it did the opposite. They also operationalize bishop alignment in a problematic way, which makes it very difficult to draw firm conclusions from their quantitative analysis. Finally, they project a modern image of international agreements and state power onto a medieval period that looked very different, and their supporting qualitative evidence suffers from selection bias. The example shows how political scientists must do solid historical spadework before they model and interpret their data. [ABSTRACT FROM AUTHOR]

Objective: Randomized controlled trials are considered the gold standard in study methodology. However, due to their study design and inclusion criteria, these studies may not capture the heterogeneity of real-world patient populations. In contrast, the lack of randomization and the presence of both measured and unmeasured confounding factors could bias the estimated treatment effect when using observational data. While causal inference methods allow for the estimation of treatment effects, their mathematical complexity may hinder their application in clinical research. Methods: We present a practical, nontechnical guide using a common statistical package (Stata) and a motivational simulated dataset that mirrors real-world observational data from patients with rheumatic diseases. We demonstrate regression analysis, regression adjustment, inverse-probability weighting, propensity score (PS) matching and two robust estimation methods. Results: Although the methods applied to control for confounding factors produced similar results, the commonly used one-to-one PS matching method could yield biased results if not thoroughly assessed. Conclusion: The guide we propose aims to facilitate the use of readily available methods in a common statistical package. It may contribute to robust and transparent epidemiological and statistical methods, thereby enhancing effectiveness research using observational data in rheumatology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: We evaluated whether participants in the landmark Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial represent US adults aged ≥40 with diabetes. Methods: Using the nationally representative 2017–2020 prepandemic National Health and Nutrition Examination Survey data, we made operational definitions of ACCORD eligibility criteria. We calculated the percentage of individuals aged ≥40 with diabetes and HbA1c ≥ 6.0% or ≥ 7.5% who met operational ACCORD eligibility criteria. Results: Applying survey sampling weights to 715 National Health and Nutrition Examination Survey participants aged ≥40 with diabetes and HbA1c ≥ 6.0% (representing 29,717,406 individuals), 12% (95% confidence interval [CI] = 8%, 18%) met the operational ACCORD eligibility criteria. Restricting to HbA1c ≥ 7.5%, 39% (95% CI = 28%, 51%) of respondents met the operational ACCORD eligibility criteria. Conclusions: ACCORD represented a minority of US middle-aged and older adults with diabetes. Given the differential risk profile between ACCORD participants and the general population with diabetes, extrapolating the trial findings may not be appropriate. [ABSTRACT FROM AUTHOR]

Objectives To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. Materials and methods We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n  = 244 071), Michigan Genomics Initiative (MGI; n  = 81 243), and UK Biobank (UKB; n  = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results. Results For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. Discussion Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis. Conclusion EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Web-based testing of cognitive abilities allows for large-scale assessments without geographical constraints. Yet, the extent to which it can reach populations beyond the typical demographic groups recruited for cognitive studies is unclear. This study focused on comparing the characteristics of individuals from a general population study (HUNT4) who chose to participate in a cognitive study (HUNT4-Hjernetrim) with those who did not. Additionally, we investigated participants' engagement and user experience. We obtained data on socio-demographics, health conditions (both physical and mental), self-reported cognitive or learning difficulties, and lifestyle factors of Hjernetrim participants and non-participants from the HUNT4 database. Hjernetrim involved 13 cognitive tests, administered through the online platform Memoro. We used logistic regressions to assess participation biases and linear regressions to assess participants' engagement and user experience. Of 65,851 HUNT4 participants invited via regular mail to Hjernetrim, 5634 (9.4%, aged 13–97, 54% women) participated. The best represented in the sample were 50–79-year-olds, women, tertiary educated, living alone, from urban areas, not occupationally active, and reporting memory complaints. Individuals who were aged 80+, had motor or vision impairments, and teenagers with learning disabilities, were underrepresented. Participants were more likely to have mental health problems, have or survived cancer and less likely to have cardiovascular disease. Participants logged on mainly during weekdays, the preferred time of day varied by age. On average, participants used 42 min and completed 78% of the tasks. Using PCs provided the most complete data. In terms of user experiences, 65% were positive while 14% were negative or reported technical difficulties. Overall, the study demonstrated that web-based methodology allowed for a relatively well-represented sample that included groups typically difficult to reach. The presence of somatic and mental diseases had a variable influence on participation. Participants finished most tests and reported positive experiences overall. [ABSTRACT FROM AUTHOR]

Background: Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP. Methods: Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall 'envisioned population' survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census. Results: Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings. Conclusions: Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings. [ABSTRACT FROM AUTHOR]

Background Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. Methods Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. Results Volunteer bias in all associations, as naively estimated in UKB, was substantial—in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. Conclusions Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available. [ABSTRACT FROM AUTHOR]

For the conservation and management of the critically endangered European eel, it is crucial to maintain an overview on the biomass of the eels moving from freshwater feeding areas to the sea for spawning. Rohtla et al. suggested an assessment of the escapement of eels from the Narva River to the Baltic Sea based on their study of origin of yellow eels from Estonian coastal areas and eels from Finnish coastal areas. Silver eels from the Estonian coast have not been included in the study. The number of descending eels in the Narva River has not been investigated. The sampling areas in the sea were located far from the river mouth, ranging from 100 to 500 km away. The article does not include an analysis of how to calculate the number of eels descending from the river based on the samples used. Thus, estimates can be significantly biased. We demonstrate that the approach used to estimate escapement is methodologically incorrect and needs further development before applying in eel management. This comment recommends methodological improvements for estimating eel escapement using the indirect method. [ABSTRACT FROM AUTHOR]

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BackgroundSuicide is a pressing public health problem, and firearm owners are at especially elevated risk. Certain health conditions are markers of suicide risk, but more research is needed on clinical risk markers for suicide among firearm owners specifically. Our goal was to examine associations of emergency department and inpatient hospital visits for behavioral and physical health conditions with firearm suicide among handgun purchasers.MethodsThis was a case-control study of 5415 legal handgun purchasers in California who died between January 1, 2008, and December 31, 2013. Cases were firearm suicide decedents; controls were motor vehicle crash decedents. Exposures were emergency department and hospital visits for six categories of health diagnoses in the 3 years prior to death. To account for selection bias due to deceased controls, we used probabilistic quantitative bias analysis to generate bias-adjusted estimates.ResultsThere were 3862 firearm suicide decedents and 1553 motor vehicle crash decedents. In multivariable models, suicidal ideation/attempt (OR 4.92; 95% CI 3.27-7.40), mental illness (OR 1.97; 95% CI 1.60-2.43), drug use disorder (OR 1.40; 95% CI 1.05-1.88), pain (OR 1.34; 95% CI 1.07-1.69), and alcohol use disorder (OR 1.29; 95% CI 1.01-1.65) were associated with higher odds of firearm suicide. When adjusting for all conditions simultaneously, only the associations for suicidal ideation/attempt and mental illness remained significant. Quantitative bias analysis indicated that observed associations were generally biased downward. For example, the bias-adjusted OR for suicidal ideation/attempt was 8.39 (95% simulation interval 5.46-13.04), almost twice that of the observed OR.ConclusionsDiagnoses for behavioral health conditions were markers for firearm suicide risk among handgun purchasers, even for conservative estimates that did not adjust for selection bias. Encounters with the healthcare system may provide opportunities to identify firearm owners at high risk of suicide.

I argue that the PUP approach proposed by Goncalves and Ng is not only useful for reducing the mean squared error of treatment effect estimators but can also be motivated from an identification perspective when units select into treatment based on past shocks. [ABSTRACT FROM AUTHOR]

Treatment effect estimation (TEE) is widely adopted in various domains such as machine learning, dvertising and marketing, and medicine. During the TEE, there normally exist selection bias on counterfactual prediction, which results in different distributions of covariates between the treated and control groups. One important challenge in TEE is to mitigate the impact of selection bias, which has attracted a lot of research in recent years. To address this challenge, existing neural network-based methods generally aim to minimize the distribution differences using integral probability metrics. However, minimizing the distribution differences may inadvertently remove outcome-related information during the balancing procedure, which has negative impact on the accuracy of TEE. In this paper, we propose a novel self-supervised learning approach to conduct TEE. Rather than minimizing the distribution differences, we first introduce the concept of virtual samples which have identical covariates as observed samples but with different treatments. In this way, we aim to simulate the scenario where each sample receives both treatment and control. Next, we propose a self-supervised domain embedding learning (SDEL) approach to conduct TEE. In SDEL, we propose to learn both treated and control embeddings for observed and virtual samples, thereby learning the effects of different treatments. To the best of our knowledge, we are the first to introduce the concept of virtual samples and the first to conduct embedding learning in TEE. Building upon SDEL, we propose a feature extraction counterfactual regression network (FE-CFR), in which we propose a feature extraction module (FEM) to estimate the importance of different covariates. Compared with existing TEE methods, our proposed self-supervised learning approach to could improve the accuracy of TEE. Extensive experiments have been conducted on benchmark datasets for TEE, and the results demonstrate that our proposed approach outperforms the compared baseline approaches. [ABSTRACT FROM AUTHOR]

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9‐ to 11‐year‐old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow‐up (ages 9–11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health‐related research. We found that including all variables related to exposure‐outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias. [ABSTRACT FROM AUTHOR]

Probability sample (PS) surveys are considered the gold standard for population-based inference but face many challenges due to decreasing response rates, relatively small sample sizes, and increasing costs. In contrast, the use of nonprobability sample (NPS) surveys has increased significantly due to their convenience, large sample sizes, and relatively low costs, but they are susceptible to large selection biases and unknown selection mechanisms. Integrating both sample types in a way that exploits their strengths and overcomes their weaknesses is an ongoing area of methodological research. We build on previous work by proposing a method of supplementing PSs with NPSs to improve analytic inference for logistic regression coefficients and potentially reduce survey costs. Specifically, we use a Bayesian framework for inference. Inference relies on a probability survey with a small sample size, and through the prior structure we incorporate supplementary auxiliary information from a less-expensive (but potentially biased) NPS survey fielded in parallel. The performance of several strongly informative priors constructed from the NPS information is evaluated through a simulation study and real-data application. Overall, the proposed priors reduce the mean-squared error (MSE) of regression coefficients or, in the worst case, perform similarly to a weakly informative (baseline) prior that does not utilize any nonprobability information. Potential cost savings (of up to 68 percent) are evident compared to a probability-only sampling design with the same MSE for different informative priors under different sample sizes and cost scenarios. The algorithm, detailed results, and interactive cost analysis are provided through a Shiny web app as guidance for survey practitioners. [ABSTRACT FROM AUTHOR]