scholarly journals Identification of causal effects in case-control studies

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Bas B. L. Penning de Vries ◽  
Rolf H. H. Groenwold
Keyword(s):  
Causal Inference ◽  
Case Control ◽  
Causal Effects ◽  
Future Research ◽  
Causal Interpretation ◽  
Case Control Studies ◽  
Sampling Schemes ◽  
Intention To Treat ◽  
Study Results ◽  
Control Designs

Abstract Background Case-control designs are an important yet commonly misunderstood tool in the epidemiologist’s arsenal for causal inference. We reconsider classical concepts, assumptions and principles and explore when the results of case-control studies can be endowed a causal interpretation. Results We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Conclusion The modern epidemiologist’s arsenal for causal inference is well-suited to make transparent for case-control designs what assumptions are necessary or sufficient to endow the respective study results with a causal interpretation and, in turn, help resolve or prevent misunderstanding. Our approach may inform future research on different estimands, other variations of the case-control design or settings with additional complexities.

scholarly journals Power and sample size for multivariate logistic modeling of unmatched case-control studies

2017 ◽  
Vol 28 (3) ◽  
pp. 822-834
Author(s):  
Mitchell H Gail ◽  
Sebastien Haneuse
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Case Control ◽  
Simulation Methods ◽  
Case Control Studies ◽  
Control Data ◽  
Logistic Analysis ◽  
Sample Size Calculations ◽  
Control Designs ◽  
Univariate Analyses

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

scholarly journals Causal inference in case-control studies

2020 ◽  
Author(s):  
Sokbae (Simon) Lee ◽  
Sung Jae Jun
Keyword(s):  
Causal Inference ◽  
Case Control ◽  
Case Control Studies

The results obtained from studies of causation

2017 ◽  
Author(s):  
Mark Elwood
Keyword(s):  
Relative Risk ◽  
Intervention Studies ◽  
Case Control ◽  
Risk Difference ◽  
Attributable Risk ◽  
Number Needed To Treat ◽  
Case Control Studies ◽  
Sampling Schemes ◽  
Multiplicative Models ◽  
The Relationship

This chapter shows the format and derivation of results from studies. Cohort and intervention studies yield relative risk and risk difference, also known as attributable risk, and number needed to treat (NNT). Count and person-time methods are shown. Additive and multiplicative models for two or more exposures are shown. Case-control studies give primarily odds ratio; the relationship between this and relative risk is explained. Different sampling schemes for case-control studies include methods were a case can also be a control. Surveys yield results similar to cohort studies.

scholarly journals Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on meta-analyses of case–control studies

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruohua Yan ◽  
Tianyi Liu ◽  
Yaguang Peng ◽  
Xiaoxia Peng
Keyword(s):  
Causal Inference ◽  
Empirical Research ◽  
Case Control ◽  
Case Control Studies ◽  
Simulation Experiments ◽  
Confounding Bias ◽  
Statistical Adjustment ◽  
Adjustment Strategies ◽  
Effect Estimation ◽  
Meta Analyses

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.

scholarly journals Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on Meta-analyses of case-control studies

2020 ◽  
Author(s):  
Ruohua Yan ◽  
Tianyi Liu ◽  
Yaguang Peng ◽  
Xiaoxia Peng
Keyword(s):  
Causal Inference ◽  
Directed Acyclic Graph ◽  
Meta Analysis ◽  
Case Control ◽  
Case Control Studies ◽  
Confounding Bias ◽  
Statistical Adjustment ◽  
Adjustment Strategies ◽  
Effect Estimation ◽  
Meta Analyses

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 estimations 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 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. The strategies included fully adjustment of all preset confounders guided by causal inference, insufficiently adjustment of less confounders, and improperly adjustment of covariates other than confounders. Results For all scenarios with different strength of causal relations, combining ORs adjusted for confounders as far as possible would get the most precise effect estimation, regardless of the sampling approaches of case-control studies and the scale of Meta-analysis. 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. 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.

scholarly journals The Taboo Against Explicit Causal Inference in Nonexperimental Psychology

2020 ◽  
Vol 15 (5) ◽  
pp. 1243-1255 ◽  
Author(s):  
Michael P. Grosz ◽  
Julia M. Rohrer ◽  
Felix Thoemmes
Keyword(s):  
Data Analysis ◽  
Causal Inference ◽  
Study Design ◽  
Causal Reasoning ◽  
Causal Effects ◽  
Future Research ◽  
Subsequent Work ◽  
Causal Mechanisms ◽  
Causal Assumptions ◽  
Research Questions

Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policymaking. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects—the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policymakers.

Salmonella enterica Contamination of Market Fresh Tomatoes: A Review

2018 ◽  
Vol 81 (7) ◽  
pp. 1193-1213 ◽  
Author(s):  
JOSHUA B. GURTLER ◽  
NIA A. HARLEE ◽  
AMANDA M. SMELSER ◽  
KEITH R. SCHNEIDER
Keyword(s):  
Tomato Plant ◽  
Case Control ◽  
Future Research ◽  
Case Control Studies ◽  
Tomato Cultivar ◽  
Tomato Production ◽  
Potential Sources ◽  
Working Together ◽  
Survival And Growth ◽  
Sources Of Contamination

ABSTRACT Salmonella contamination associated with market fresh tomatoes has been problematic for the industry and consumers. A number of outbreaks have occurred, and dollar losses for the industry, including indirect collateral impact to agriculturally connected communities, have run into the hundreds of millions of dollars. This review covers these issues and an array of problems and potential solutions surrounding Salmonella contamination in tomatoes. Some other areas discussed include (i) the use of case-control studies and DNA fingerprinting to identify sources of contamination, (ii) the predilection for contamination based on Salmonella serovar and tomato cultivar, (iii) internalization, survival, and growth of Salmonella in or on tomatoes and the tomato plant, in biofilms, and in niches ancillary to tomato production and processing, (iv) the prevalence of Salmonella in tomatoes, especially in endogenous regions, and potential sources of contamination, and (v) effective and experimental means of decontaminating Salmonella from the surface and stem scar regions of the tomato. Future research should be directed in many of the areas discussed in this review, including determining and eliminating sources of contamination and targeting regions of the country where Salmonella is endemic and contamination is most likely to occur. Agriculturalists, horticulturalists, microbiologists, and epidemiologists may make the largest impact by working together to solve other unanswered questions regarding tomatoes and Salmonella contamination.

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