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In observational studies, identifying and adjusting for a sufficient set of confounders is crucial for accurately estimating the causal effect of the exposure on the outcome. Even in studies with large sample sizes, which typically benefit from small variances in estimates, there is a risk of producing estimates that are precisely inaccurate if the study suffers from systematic errors or biases, including confounding bias. To date, several approaches have been developed for selecting confounders. In this article, we first summarize the epidemiological and statistical approaches to identifying a sufficient set of confounders. Particularly, we introduce the modified disjunctive cause criterion as one of the most useful approaches, which involves controlling for any pre-exposure covariate that affects the exposure, outcome, or both. It then excludes instrumental variables but includes proxies for the shared common cause of exposure and outcome. Statistical confounder selection is also useful when dealing with a large number of covariates, even in studies with small sample sizes. After introducing several approaches, we discuss some pitfalls and considerations in confounder selection, such as the adjustment for instrumental variables, intermediate variables, and baseline outcome variables. Lastly, as it is often difficult to comprehensively measure key confounders, we introduce two statistics, E-value and robustness value, for assessing sensitivity to unmeasured confounders. Illustrated examples are provided using the National Health and Nutritional Examination Survey Epidemiologic Follow-up Study. Integrating these principles and approaches will enhance our understanding of confounder selection and facilitate better reporting and interpretation of future epidemiological studies. [ABSTRACT FROM AUTHOR]

Abstract Background In causal analyses, some third factor may distort the relationship between the exposure and the outcome variables under study, which gives spurious results. In this case, treatment groups and control groups that receive and do not receive the exposure are different from one another in some other essential variables, called confounders. Method Place of birth was used as exposure variable and age-specific childhood vaccination status was used as outcome variables. Three approaches of confounder selection techniques such as all pre-treatment covariates, outcome cause covariates, and common cause covariates were proposed. Multiple logistic regression was used to estimate the propensity score for inverse probability treatment weighting (IPTW) confounder adjustment techniques. The proportional odds model was used to estimate the causal effect of place of birth on age-specific childhood vaccination. To validate the result obtained from observed data, we used a plasmode simulation of resampling 1000 samples from actual data 500 times. Result Outcome cause and common cause confounder identification techniques gave comparable results in terms of treatment effect in the plasmode data. However, outcome causes that contain common causes and predictors of the outcome confounder identification gave relatively better treatment effect results. The treatment effect result in the IPTW confounder adjustment method was better than that of the regression adjustment method. The effect of place of birth on log odds of cumulative probability of age-specific childhood vaccination was 0.36 with odds ratio of 1.43 for higher level vaccination status. Conclusion It is essential to use plasmode simulation data to validate the reproducibility of the proposed methods on the observed data. It is important to use outcome-cause covariates to adjust their confounding effect on the outcome. Using inverse probability treatment weighting gives unbiased treatment effect results as compared to the regression method of confounder adjustment. Institutional delivery increases the likelihood of childhood vaccination at the recommended schedule.

Background: In causal analyses, some third factor may distort the relationship between the exposure and the outcome variables under study, which gives spurious results. In this case, treatment groups and control groups that receive and do not receive the exposure are different from one another in some other essential variables, called confounders. Method: Place of birth was used as exposure variable and age-specific childhood vaccination status was used as outcome variables. Three approaches of confounder selection techniques such as all pre-treatment covariates, outcome cause covariates, and common cause covariates were proposed. Multiple logistic regression was used to estimate the propensity score for inverse probability treatment weighting (IPTW) confounder adjustment techniques. The proportional odds model was used to estimate the causal effect of place of birth on age-specific childhood vaccination. To validate the result obtained from observed data, we used a plasmode simulation of resampling 1000 samples from actual data 500 times. Result: Outcome cause and common cause confounder identification techniques gave comparable results in terms of treatment effect in the plasmode data. However, outcome causes that contain common causes and predictors of the outcome confounder identification gave relatively better treatment effect results. The treatment effect result in the IPTW confounder adjustment method was better than that of the regression adjustment method. The effect of place of birth on log odds of cumulative probability of age-specific childhood vaccination was 0.36 with odds ratio of 1.43 for higher level vaccination status. Conclusion: It is essential to use plasmode simulation data to validate the reproducibility of the proposed methods on the observed data. It is important to use outcome-cause covariates to adjust their confounding effect on the outcome. Using inverse probability treatment weighting gives unbiased treatment effect results as compared to the regression method of confounder adjustment. Institutional delivery increases the likelihood of childhood vaccination at the recommended schedule. [ABSTRACT FROM AUTHOR]

Studies in empirical software engineering are often most useful if they make causal claims because this allows practitioners to identify how they can purposefully influence (rather than only predict) outcomes of interest. Unfortunately, many non-experimental studies suffer from potential endogeneity, for example, through omitted confounding variables, which precludes claims of causality. In this conceptual tutorial, we aim to transfer the proven solution of instrumental variables and two-stage models as a means to account for endogeneity from econometrics to the field of empirical software engineering. To this end, we discuss causality and causal inference, provide a definition of endogeneity, explain its causes, and lay out the conceptual idea behind instrumental variable approaches and two-stage models. We also provide an extensive illustration with simulated data and a brief illustration with real data to demonstrate the approach, offering Stata and R code to allow researchers to replicate our analyses and apply the techniques to their own research projects. We close with concrete recommendations and a guide for researchers on how to deal with endogeneity. [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]

Recommender systems are influenced by many confounding factors (i.e., confounders) which result in various biases (e.g., popularity biases) and inaccurate user preference. Existing approaches try to eliminate these biases by inference with causal graphs. However, they assume all confounding factors can be observed and no hidden confounders exist. We argue that many confounding factors (e.g., season) may not be observable from user–item interaction data, resulting inaccurate user preference. In this article, we propose a deconfounded recommender considering unobservable confounders. Specifically, we propose a new causal graph with explicit and implicit feedback, which can better model user preference. Then, we realize a deconfounded estimator by the front-door adjustment, which is able to eliminate the effect of unobserved confounders. Finally, we conduct a series of experiments on two real-world datasets, and the results show that our approach performs better than other counterparts in terms of recommendation accuracy. [ABSTRACT FROM AUTHOR]

Understanding causal mechanisms is a central goal in the behavioral, developmental, and social sciences. When estimating and probing causal effects using observational data, covariate adjustment is a crucial element to remove dependencies between focal predictors and the error term. Covariate selection, however, constitutes a challenging task because availability alone is not an adequate criterion to decide whether a covariate should be included in the statistical model. The present study introduces a non-Gaussian method for covariate selection and provides a forward selection algorithm for linear models (i.e., non-Gaussian forward selection; nGFS) to select appropriate covariates from a set of potential control variables to avoid inconsistent and biased estimators of the causal effect of interest. Further, we demonstrate that the forward selection algorithm has properties compatible with principles of direction of dependence, i.e., probing whether the causal target model is correctly specified with respect to the causal direction of effects. Results of a Monte Carlo simulation study suggest that the selection algorithm performs well, in particular when sample sizes are large (i.e., n ≥ 250) and data strongly deviate from Gaussianity (e.g., distributions with skewness beyond 1.5). An empirical example is given for illustrative purposes. [ABSTRACT FROM AUTHOR]

Objectives: Adjusting for potential confounders is crucial for producing valuable evidence in outcome studies. Although numerous studies have been published using the Korea National Health Insurance Claim Database, no study has critically reviewed the methods used to adjust for confounders. This study aimed to review these studies and suggest methods and applications to adjust for confounders. Methods: We conducted a literature search of electronic databases, including PubMed and Embase, from January 1, 2021 to December 31, 2022. In total, 278 studies were retrieved. Eligibility criteria were published in English and outcome studies. A literature search and article screening were independently performed by 2 authors and finally, 173 of 278 studies were included. Results: Thirty-nine studies used matching at the study design stage, and 171 adjusted for confounders using regression analysis or propensity scores at the analysis stage. Of these, 125 conducted regression analyses based on the study questions. Propensity score matching was the most common method involving propensity scores. A total of 171 studies included age and/or sex as confounders. Comorbidities and healthcare utilization, including medications and procedures, were used as confounders in 146 and 82 studies, respectively. Conclusions: This is the first review to address the methods and applications used to adjust for confounders in recently published studies. Our results indicate that all studies adjusted for confounders with appropriate study designs and statistical methodologies; however, a thorough understanding and careful application of confounding variables are required to avoid erroneous results.

Background: Cancer immunotherapy has emerged as a transformative approach for treating various malignancies, including melanoma, lung cancer, breast cancer, and leukemia. Animal models have been instrumental in elucidating the mechanisms and potential of these therapies. However, graft-versus-host disease (GVHD) is an inherent challenge in these studies, primarily because the introduction of foreign immune cells or tissues often triggers immune responses. Methods: A detailed systematic search was conducted across various scientific databases, including PubMed, Scopus, Embase, and Web of Science. The search aimed to identify peer-reviewed articles published in English from January 2000 to September 2023. Keywords and phrases used in the search included "Graft-versus-Host Disease", "GVHD", "animal models", "cancer immunotherapy", and combinations thereof. Boolean operators (AND/OR) were employed to refine the search. Finally, 6 articles were included in this systematic review, which is registered on PROSPERO (ID number CRD42024488544). Results: Our systematic review identified several mechanisms employed in animal studies to mitigate the confounding effects of GVHD. These included genetically modified mouse models, immunosuppressive drugs, and humanized mice. Furthermore, the review highlights innovative approaches such as selective T-cell depletion and the use of specific cytokine inhibitors. Conclusion: By systematically identifying and mitigating the confounding effects of GVHD, we can significantly improve the predictive validity of preclinical trials, obtain broadly applicable findings, improve the efficiency of drugs, enhance safety profiling, and develop better therapeutic strategies. This approach is crucial in ensuring that the immunotherapeutic strategies developed in the laboratory are reflective of the human physiological response, thereby bridging a critical translational gap in oncological research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Prevention & Treatment For Stomatological Diseases is the property of Journal of Prevention & Treatment For Stomatological Diseases Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several meta-analyses have found a positive association between a popular type of "fad diet", ketogenic diets, and their effect on anthropometric and blood parameters. However, the non-specific inclusion criteria for meta-analyses may lead to incorrect conclusions. The aim of this literature review is to highlight the main confounders and methodological pitfalls of meta-analyses on ketogenic diets by inspecting the presence of key inclusion criteria. The PubMed, Embase, and Web of Science databases and the Cochrane Database of Systematic Reviews were searched for meta-analyses. Most meta-analyses did not define the essential parameters of a ketogenic diet (i.e., calories, macronutrient ratio, types of fatty acids, ketone bodies, etc.) as inclusion criteria. Of the 28 included meta-analyses, few addressed collecting real, re-measured nutritional data from the ketogenic diet and control groups in parallel with the pre-designed nutritional data. Most meta-analyses reported positive results in favor of ketogenic diets, which can result in erroneous conclusions considering the numerous methodological pitfalls and confounders. Well-designed clinical trials with comparable results and their meta-analyses are needed. Until then, medical professionals should not recommend ketogenic diets as a form of weight loss when other well-known dietary options have been shown to be healthy and effective. [ABSTRACT FROM AUTHOR]

Copyright of Arthroskopie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of ESTUDAM Public Health Journal is the property of ESTUDAM Public Health Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows and precipitation, we introduce a new causal discovery methodology for heavy-tailed variables that allows the effect of a known potential confounder to be almost entirely removed when the variables have comparable tails, and also decreases it sufficiently to enable correct causal inference when the confounder has a heavier tail. We also introduce a new parametric estimator for the existing causal tail coefficient and a permutation test. Simulations show that the methods work well and the ideas are applied to the motivating dataset. [ABSTRACT FROM AUTHOR]

Quasi-experimental designs (QEDs) are used to estimate a treatment effect without randomization. Confounders have a causal relationship with the outcome and probability of treatment adoption and if unaccounted for can bias treatment effect estimates. A variable considered a confounder prior to treatment can change after treatment has occurred (i.e., a time-varying confounder) not as a result of treatment (what we call an exogenous time-varying confounder). If the post-treatment value causally affects the outcome to change and this post-treatment value of the exogenous time-varying confounder is unaccounted for, then the treatment effect may be biased. We review the Rubin Causal Model and QED assumptions and the effect an exogenous time-varying confounder has on the ability of QEDs to produce an appropriate counterfactual. We conduct a simulation study evaluating propensity score and difference-in-differences based methods for estimating a treatment effect with an exogenous time-varying confounder. Propensity score weighted two-way fixed effects, inverse probability weighted, or doubly robust difference-in-differences methods, each with propensity scores estimated using post-implementation values of the exogenous time-varying confounder, proved least biased when the exogenous time-varying confounder changed differentially for members of the treatment and control groups. [ABSTRACT FROM AUTHOR]

High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

Long‐term register data offer unique opportunities to explore causal effects of treatments on time‐to‐event outcomes, in well‐characterized populations with minimum loss of follow‐up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software. [ABSTRACT FROM AUTHOR]

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state‐of‐the‐art solutions follow a segmentation‐by‐registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well‐defined segmentations. However, registration‐based pipelines are time‐consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one‐step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU‐Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration‐based approach. We evaluated the generalizability of the network by performing a leave‐one‐dataset‐out cross‐validation, and independent testing on unseen datasets. Furthermore, we assessed cross‐domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration‐based gold standard. On our test system, the computation time decreased from 43 min for a reference registration‐based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage. [ABSTRACT FROM AUTHOR]

To improve the accuracy of the few-shot relation classification task, the research, which weakens the influence of confounder on the performance of the model and enhances the semantic representation and feature extraction ability of the model, is carried out based on the prototypical network. And then, the weaken-confounders method based on causal intervention(WCCI) is proposed, and the RBERTI-Proto model is constructed. In WCCI, the pre-trained knowledge is stratified by the backdoor adjustment based on causal intervention, the optimal stratified number is determined by a stratified method, and the BN layer is introduced for the gradient disappearance problem. In the RBERTI-Proto model, the abilities of semantic representation and feature extraction of the model are enhanced by which the RoBERTa is used as the feature extractor of the model. Experimental results demonstrate the effectiveness of our proposed methods and the RoBERTa model as feature extractor of our model, and the ACC value of the RBERTI-Proto model achieve 93.38% on the 5-way 5-shot scenario of the FewRel dataset.

• Gender was a potential confounding variable of hair 2-AG/1-AG, PEA, OEA, SEA levels. • Age was negatively associated with hair AEA, PEA, OEA levels in older adults. • BMI, waist circumferencewere negatively associated with hair PEA, OEA, SEA levels. • Hair washing frequency influencedhair AEA, PEA, OEA, SEA levels. • Medication intake and illegal drug use could confound hair AEA or 2-AG/1-AG levels. Hair analysis of endocannabinoids and N-acylethanolamines has emerged as a promising methodological advancement for the retrospective assessment of cumulative long-term endocannabinoids and N-acylethanolamines secretion in biopsychological research of stress-related symptoms and disorders. In this study, we investigated the influence of the potential associated factors on hair endocannabinoid and N-acylethanolamine levels in 760 adult participants aged between 21 and 86 years old. Gender, age, medication intake, drug use, body mass index, waist circumference, smoking status, alcohol consumption and perceived stress were assessed through questionnaires. Hair endocannabinoid and N-acylethanolamine concentrations were measured through liquid chromatography coupled to tandem mass-spectrometry. Results identified that gender, age, BMI, waist circumference, medication and drug use, hair washing frequency as potential confounding variables of hair endocannabinoid and N-acylethanolamine levels, while no associations were observed with respect to hair dyeing, smoking status, alcohol consumption and perceived stress. The present data indicate that hair endocannabinoids and N-acylethanolamines measurement may be a useful alternative to the current circulating endocannabinoids measurements in biopsychological research with the consideration of possible confounders. [ABSTRACT FROM AUTHOR]

Simple Summary: The continuing increase in mortality from malignant mesothelioma, often a late effect of asbestos exposure, has gained much public attention. Malignant mesothelioma is a devastating disease with limited therapeutic options. A major problem is that this cancer is usually diagnosed when the tumor is already large and has spread. An earlier diagnosis could be possible with blood tests that determine biomarkers like the protein mesothelin. The corresponding gene of mesothelin, however, can harbor genetic variants that could influence the proteins blood concentrations. We therefore studied four genetic variants in 410 asbestos-exposed males without cancer and 102 mesothelioma cases and revealed that the mesothelin concentration between the groups was significantly different (p < 0.0001) and that five to eight mutations of the four variants studied were associated with increased mesothelin concentrations (p = 0.001). These results may be a helpful tool to explain unusually high values of mesothelin protein in healthy people and provides a basis to consider the exclusion of influencing factors for an improvement of the diagnostic procedure. Finally, knowledge about confounders can be integrated into surveillance programs offered to high-risk groups of asbestos-exposed workers. Malignant mesothelioma (MM) is a severe disease mostly caused by asbestos exposure. Today, one of the best available biomarkers is the soluble mesothelin-related protein (SMRP), also known as mesothelin. Recent studies have shown that mesothelin levels are influenced by individual genetic variability. This study aimed to investigate the influence of three mesothelin (MSLN) gene variants (SNPs) in the 5′-untranslated promoter region (5′-UTR), MSLN rs2235503 C > A, rs3764246 A > G, rs3764247 A > C, and one (rs1057147 G > A) in the 3′-untranslated region (3′-UTR) of the MSLN gene on plasma concentrations of mesothelin in 410 asbestos-exposed males without cancer and 43 males with prediagnostic MM (i.e., with MM diagnosed later on) from the prospective MoMar study, as well as 59 males with manifest MM from Germany. The mesothelin concentration differed significantly between the different groups (p < 0.0001), but not between the prediagnostic and manifest MM groups (p = 0.502). Five to eight mutations of the four SNP variants studied were associated with increased mesothelin concentrations (p = 0.001). The highest mesothelin concentrations were observed for homozygous variants of the three promotor SNPs in the 5′-UTR (p < 0.001), and the highest odds ratio for an elevated mesothelin concentration was observed for MSLN rs2235503 C > A. The four studied SNPs had a clear influence on the mesothelin concentration in plasma. Hence, the analysis of these SNPs may help to elucidate the diagnostic background of patients displaying increased mesothelin levels and might help to reduce false-positive results when using mesothelin for MM screening in high-risk groups. [ABSTRACT FROM AUTHOR]

We established reference intervals for serum concentrations of hormones from healthy pediatric subjects and investigated their associations with gender, body mass index (BMI), puberty and oral contraceptives (oC).

A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be different than raw images. Some data sets were of pediatric cases with pneumonia where COVID-19 chest X-rays are almost exclusively from adults, so lung size becomes a spurious feature that can be exploited. In this work, we have eliminated many confounding features by working with as close to raw data as possible. Still, deep learned models may leverage source specific confounders to differentiate COVID-19 from pneumonia preventing generalizing to new data sources (i.e. external sites). Our models have achieved an AUC of 1.00 on seen data sources but in the worst case only scored an AUC of 0.38 on unseen ones. This indicates that such models need further assessment/development before they can be broadly clinically deployed. An example of fine-tuning to improve performance at a new site is given.

Abstract Background Statistical adjustment is often considered to control confounding bias in observational studies, especially case–control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case–control studies to improve the validity of meta-analyses. Methods Three types of adjustment strategies were evaluated including insufficient adjustment (not all preset confounders were adjusted), full adjustment (all confounders were adjusted under the guidance of causal inference), and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case–control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results. Results For all scenarios with different strength of causal relations, combining ORs that were comprehensively adjusted for confounders would get the most precise effect estimation. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research. Conclusions Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case–control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods.

Abstract Background Myocardial native T1 and T2 relaxation time mapping are sensitive to pathological increase of myocardial water content (e.g. myocardial edema). However, the influence of physiological hydration changes as a possible confounder of relaxation time assessment has not been studied. The purpose of this study was to evaluate, whether changes in myocardial water content due to dehydration and hydration might alter myocardial relaxation times in healthy subjects. Methods A total of 36 cardiovascular magnetic resonance (CMR) scans were performed in 12 healthy subjects (5 men, 25.8 ± 3.2 years). Subjects underwent three successive CMR scans: (1) baseline scan, (2) dehydration scan after 12 h of fasting (no food or water), (3) hydration scan after hydration. CMR scans were performed for the assessment of myocardial native T1 and T2 relaxation times and cardiac function. For multiple comparisons, repeated measures ANOVA or the Friedman test was used. Results There was no change in systolic blood pressure or left ventricular ejection fraction between CMR scans (P > 0.05, respectively). T1 relaxation times were significantly reduced with dehydration (987 ± 27 ms [baseline] vs. 968 ± 29 ms [dehydration] vs. 986 ± 28 ms [hydration]; P = 0.006). Similar results were observed for T2 relaxation times (52.9 ± 1.8 ms [baseline] vs. 51.5 ± 2.0 ms [dehydration] vs. 52.2 ± 1.9 ms [hydration]; P = 0.020). Conclusions Dehydration may lead to significant alterations in relaxation times and thereby may influence precise, repeatable and comparable assessment of native T1 and T2 relaxation times. Hydration status should be recognized as new potential confounder of native T1 and T2 relaxation time assessment in clinical routine.

The problems of causal inference of models from empirical data (by independence-based methods) and some error mechanisms are examined. We demonstrate that the known rules for orienting edges of model can produce misleading results under presence of latent confounders. We propose corrections to the orientation rules aiming to successfully extend them for inference of models beyond the ancestral model class. The necessary assumptions justifying the inference of adequate causal relationships from data are suggested. [ABSTRACT FROM AUTHOR]

Various blood cell ratios exist which seem to have an impact on prognosis for resected gastric cancer patients. The aim of this systematic review was to investigate the prognostic role of blood cell ratios in patients with gastric cancer undergoing surgery in a curative attempt. A systematic literature search in MEDLINE (via PubMed), CENTRAL, and Web of Science was performed. Information on survival and cut-off values from all studies investigating any blood cell ratio in resected gastric cancer patients were extracted. Prognostic significance and optimal cut-off values were calculated by meta-analyses and a summary of the receiver operating characteristic. From 2831 articles, 65 studies investigated six different blood cell ratios (prognostic nutritional index (PNI), lymphocyte to monocyte ratio (LMR), systemic immune-inflammation index (SII), monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR)). There was a significant association for the PNI and NLR with overall survival and disease-free survival and for LMR and NLR with 5-year survival. The used cut-off values had high heterogeneity. The available literature is flawed by the use of different cut-off values hampering evidence-based patient treatment and counselling. This article provides optimal cut-off values recommendations for future research. [ABSTRACT FROM AUTHOR]

Causation and causal inference are of utmost importance in obstetrics and gynecology. In many clinical situations, causal reasoning is involved in etiological explanations, diagnostic considerations, and conversations about prognosis. In this paper, we offer an overview of the philosophical accounts of causation that may not be familiar to, but still be appreciated by, the busy clinician. In our discussion, we do not try to simplify what is a rather complex range of ideas. We begin with an introduction to some important basic ideas, followed by 2 sections on the metaphysical and epistemological aspects of causality, which offer a more detailed discussion of some of its specific philosophical facets, using examples from obstetrical and gynecologic research and practice along the way. We hope our discussion will help deepen the thinking and discourse about causation and causal inference in gynecology and obstetrics. [ABSTRACT FROM AUTHOR]

Third-variable effects, such as mediation and confounding, are core concepts in prevention science, providing the theoretical basis for investigating how risk factors affect behavior and how interventions change behavior. Another third variable, the collider, is not commonly considered but is also important for prevention science. This paper describes the importance of the collider effect as well as the similarities and differences between these three third-variable effects. The single mediator model in which the third variable (T) is a mediator of the independent variable (X) to dependent variable (Y) effect is used to demonstrate how to estimate each third-variable effect. We provide difference in coefficients and product of coefficients estimators of the effects and demonstrate how to calculate these values with real data. Suppression effects are defined for each type of third-variable effect. Future directions and implications of these results are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Der Urologe A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The point that adjustment for confounders do not always guarantee protection against spurious findings and type 1-errors has been made before. The present simulation study indicates that for traditional regression methods, this risk is accentuated by a large sample size, low reliability in the measurement of the confounder, and high reliability in the measurement of the predictor and the outcome. However, this risk might be attenuated by calculating the expected adjusted effect, or the required reliability in the measurement of the possible confounder, with equations presented in the present paper.

Training new medical odors presents challenges in procuring sufficient target samples, and suitably matched controls. Organizations are often forced to choose between using fewer samples and risking dogs learning individuals or using differently sourced samples. Even when aiming to standardize all aspects of collection, processing, storage and presentation, this risks there being subtle differences which dogs use to discriminate, leading to artificially high performance, not replicable when novel samples are presented. We describe lessons learnt during early training of dogs to detect prostate cancer from urine. Initially, six dogs were trained to discriminate between hospital-sourced target and externally-sourced controls believed to be processed and stored the same way. Dogs performed well: mean sensitivity 93.5% (92.2–94.5) and specificity 87.9% (78.2–91.9). When training progressed to include hospital-sourced controls, dogs greatly decreased in specificity 67.3% (43.2–83.3). Alerted to a potential issue, we carried out a methodical, investigation. We presented new strategically chosen samples to the dogs and conducted a logistic regression analysis to ascertain which factor most affected specificity. We discovered the two sets of samples varied in a critical aspect, hospital-processed samples were tested by dipping the urinalysis stick into the sample, whilst for externally sourced samples a small amount of urine was poured onto the stick. Dogs had learnt to distinguish target aided by the odor of this stick. This highlights the importance of considering every aspect of sample processing even when using urine, often believed to be less susceptible to contamination than media like breath.

Background: Statistical adjustment is often considered to control confounding bias in observational studies, especially case-control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case-control studies to improve the validity of meta-analyses.Methods: Three types of adjustment strategies were evaluated including insufficient adjustment (not all preset confounders were adjusted), full adjustment (all confounders were adjusted under the guidance of causal inference), and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case-control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results.Results: For all scenarios with different strength of causal relations, combining ORs that were comprehensively adjusted for confounders would get the most precise effect estimation. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research.Conclusions: Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case-control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods. [ABSTRACT FROM AUTHOR]

The point that adjustment for confounders do not always guarantee protection against spurious findings and type 1-errors has been made before. The present simulation study indicates that for traditional regression methods, this risk is accentuated by a large sample size, low reliability in the measurement of the confounder, and high reliability in the measurement of the predictor and the outcome. However, this risk might be attenuated by calculating the expected adjusted effect, or the required reliability in the measurement of the possible confounder, with equations presented in the present paper. [ABSTRACT FROM AUTHOR]