Effects In Case-control Studies Research Articles

Efficient estimation of indirect effects in case-control studies using a unified likelihood framework.

Mediation models are a set of statistical techniques that investigate the mechanisms that produce an observed relationship between an exposure variable and an outcome variable in order to deduce the extent to which the relationship is influenced by intermediate mediator variables. For a case-control study, the most common mediation analysis strategy employs a counterfactual framework that permits estimation of indirect and direct effects on the odds ratio scale for dichotomous outcomes, assuming either binary or continuous mediators. While this framework has become an important tool for mediation analysis, we demonstrate that we can embed this approach in a unified likelihood framework for mediation analysis in case-control studies that leverages more features of the data (in particular, the relationship between exposure and mediator) to improve efficiency of indirect effect estimates. One important feature of our likelihood approach is that it naturally incorporates cases within the exposure-mediator model to improve efficiency. Our approach does not require knowledge of disease prevalence and can model confounders and exposure-mediator interactions, and is straightforward to implement in standard statistical software. We illustrate our approach using both simulated data and real data from a case-control genetic study of lung cancer.

Identification of causal effects in case-control studies

BackgroundCase-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.ResultsWe 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.ConclusionThe 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.

Uses of pathogen detection data to estimate vaccine direct effects in case-control studies.

The fact that many pathogens can be carried or shed without causing symptoms complicates the interpretation of microbiological data when diagnosing certain infectious disease syndromes. Diagnostic criteria that attribute symptoms to a pathogen which is detectable, whether it is or is not the aetiological agent of disease, may lead to outcome misclassification in epidemiological studies. Case-control studies are commonly undertaken to estimate vaccine effectiveness (VE) and present an opportunity to compare pathogen detection among individuals with and without clinically relevant symptoms. Considering this study context, we present a mathematical framework yielding simple estimators for the direct effects of vaccination on various aspects of host susceptibility. These include protection against acquisition of the pathogen of interest and protection against progression of this pathogen to disease following acquisition. We assess the impact of test sensitivity on these estimators and extend our framework to identify a 'vaccine probe' estimator for pathogen-specific aetiological fractions. We also derive biases affecting VE estimates under the test-negative design, a special case enrolling only symptomatic persons. Our results provide strategies for estimating pathogen-specific VE in the absence of a diagnostic gold standard. These approaches can inform the design and analysis of studies addressing numerous pathogens and vaccines.

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis

The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets.

A Powerful Gene-Based Test Accommodating Common and Low-Frequency Variants to Detect Both Main Effects and Gene-Gene Interaction Effects in Case-Control Studies.

Next-generation sequencing (NGS) has been widely used in genetic association studies to identify both common and rare variants associated with complex diseases. Various statistical association tests have been developed to analyze NGS data; however, most focus on identifying the marginal effects of a set of genetic variants on the disease. Only a few association tests for NGS data analysis have considered the interaction effects between genes. We developed three powerful gene-based gene-gene interaction tests for testing both the main effects and the interaction effects of common, low-frequency, and common with low-frequency variant pairs between two genes (the IGOF tests) in case-control studies using NGS data. We performed a comprehensive simulation study to verify that the proposed tests had appropriate type I error rates and significantly higher power than did other interaction tests for analyzing NGS data. The tests were applied to a whole-exome sequencing dataset for autism spectrum disorder (ASD) and the significant results were evaluated in another independent ASD cohort. The IGOF tests were implemented in C++ and are available at http://igof.sourceforge.net.

A Bayesian Partitioning Model for the Detection of Multilocus Effects in Case-Control Studies

Background: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS. Methods: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease. Results: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes. Conclusion: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.

A double robust approach to causal effects in case-control studies.

In a recent issue of the Journal, VanderWeele and Vansteelandt (Am J Epidemiol. 2011;174(10):1197-1203) discussed an inverse probability weighting method for case-control studies that could be used to estimate an additive interaction effect, referred to as the "relative excess risk due to interaction." In this article, we reinforce the well-known disadvantages of inverse probability weighting and comment on the desirability of the described parameter. Further, we review an existing double robust estimator not considered by VanderWeele and Vansteelandt, the case-control-weighted targeted maximum likelihood estimator, which has improved properties in comparison with a previously described inverse-probability-weighted estimator. This targeted maximum likelihood estimator can be used to target various parameters of interest, and its implementation has been described previously for the risk difference, relative risk, and odds ratio.

Including known covariates can reduce power to detect genetic effects in case-control studies

Genome-wide association studies (GWAS) search for associations between genetic variants and disease status, typically via logistic regression. Often there are covariates, such as sex or well-established major genetic factors, that are known to affect disease susceptibility and are independent of tested genotypes at the population level. We show theoretically and with data from recent GWAS on multiple sclerosis, psoriasis and ankylosing spondylitis that inclusion of known covariates can substantially reduce power for the identification of associated variants when the disease prevalence is lower than a few percent. Whether the inclusion of such covariates reduces or increases power to detect genetic effects depends on various factors, including the prevalence of the disease studied. When the disease is common (prevalence of >20%), the inclusion of covariates typically increases power, whereas, for rarer diseases, it can often decrease power to detect new genetic associations.

Sample size requirements to detect the effect of a group of genetic variants in case-control studies

BackgroundBecause common diseases are caused by complex interactions among many genetic variants along with environmental risk factors, very large sample sizes are usually needed to detect such effects in case-control studies. Nevertheless, many genetic variants act in well defined biologic systems or metabolic pathways. Therefore, a reasonable first step may be to detect the effect of a group of genetic variants before assessing specific variants.MethodsWe present a simple method for determining approximate sample sizes required to detect the average joint effect of a group of genetic variants in a case-control study for multiplicative models.ResultsFor a range of reasonable numbers of genetic variants, the sample size requirements for the test statistic proposed here are generally not larger than those needed for assessing marginal effects of individual variants and actually decline with increasing number of genetic variants in many situations considered in the group.ConclusionWhen a significant effect of the group of genetic variants is detected, subsequent multiple tests could be conducted to detect which individual genetic variants and their combinations are associated with disease risk. When testing for an effect size in a group of genetic variants, one can use our global test described in this paper, because the sample size required to detect an effect size in the group is comparatively small. Our method could be viewed as a screening tool for assessing groups of genetic variants involved in pathogenesis and etiology of common complex human diseases.

A Simple Loglinear Model for Haplotype Effects in a Case-Control Study Involving Two Unphased Genotypes

Because haplotypes may parsimoniously summarize the effect of genes on disease, there is great interest in using haplotypes in case-control studies of unphased genotype data. Previous methods for investigating haplotypes effects in case-control studies have not allowed for both of the following two scenarios that could have a large impact on results (i) departures from Hardy-Weinberg equilibrium in controls as well as cases, and (ii) an interactive effect of haplotypes and environmental covariates on the probability of disease. A new method is proposed that generalizes the model of Epstein and Satten to incorporate both (i) and (ii). Computations are relatively simple involving a single loglinear design matrix for parameters modeling the distribution of haplotype frequencies in controls, parameters modeling the effect of haplotypes and covariate-haplotype interactions on disease, and nuisance parameters required for correct inference. Based on simulations with realistic sample sizes, the method is recommended with data from two genotypes, a recessive or dominant model linking haplotypes to disease, and estimates of haplotype effects among haplotypes with a frequency greater than 10%. The methodology is most useful with candidate genotype pairs or for searching through pairs of genotypes when scenarios (i) and (ii) are likely. An example without a covariate illustrates the importance of modeling a departure from Hardy-Weinberg equilibrium in controls.