2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies.
Ontology highlight
ABSTRACT:
SUBMITTER: Yi S
PROVIDER: S-EPMC8276451 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
Publications
2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies.
Yi Sangyoon S Zhang Xianyang X Yang Lu L Huang Jinyan J Liu Yuanhang Y Wang Chen C Schaid Daniel J DJ Chen Jun J
Genome biology 20210713 1
One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. ...[more]
Similar Datasets
Project description:MotivationGenomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction.ResultsThis study shows that the traditional approach is suboptimal and proposes a new two-dimensional false discovery rate control framework (2DFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2DFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2DFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. Promising finite sample performance is demonstrated via extensive simulations and real data applications.Availability and implementationR codes and vignettes are available at https://github.com/asmita112358/tdfdr.np.
| S-EPMC10539716 | biostudies-literature
Project description:BackgroundConfounder adjustment is critical for accurate causal inference in observational studies. However, the appropriateness of methods for confounder adjustment in studies investigating multiple risk factors, where the factors are not simply mutually confounded, is often overlooked. This study aims to summarise the methods for confounder adjustment and the related issues in studies investigating multiple risk factors.MethodsA methodological study was performed. We searched PubMed from January 2018 to March 2023 to identify cohort and case-control studies investigating multiple risk factors for three chronic diseases (cardiovascular disease, diabetes and dementia). Study selection and data extraction were conducted independently by two reviewers. The study objectives were grouped into two categories: widely exploring potential risk factors and examining specific risk factors. The methods for confounder adjustment were classified based on a summarisation of the included studies, identifying six categories: (1) each risk factor was adjusted for potential confounders separately (the recommended method); (2) all risk factors were mutually adjusted (i.e. including all factors in a multivariable model); (3) all risk factors were adjusted for the same confounders separately; (4) all risk factors were adjusted for the same confounders with some factors being mutually adjusted; (5) all risk factors were adjusted for the same confounders with mutual adjustment among them being unclear; and (6) unable to judge. All data were descriptively analysed.ResultsA total of 162 studies were included, with 88 (54.3%) exploring potential risk factors and 74 (45.7%) examining specific risk factors. The current status of confounder adjustment was unsatisfactory: only ten studies (6.2%) used the recommended method, all of which aimed at examining several specific risk factors; in contrast, mutual adjustment was adopted in over 70% of the studies. The remaining studies either adjusted for the same confounders across all risk factors, or unable to judge.ConclusionsThere is substantial variation in the methods for confounder adjustment among studies investigating multiple risk factors. Mutual adjustment was the most commonly adopted method, which might lead to overadjustment bias and misleading effect estimates. Future research should avoid indiscriminately including all risk factors in a multivariable model to prevent inappropriate adjustment.
| S-EPMC11881322 | biostudies-literature
Project description:We consider large-scale studies in which thousands of significance tests are performed simultaneously. In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable(s) of interest (e.g., treatment variable, phenotype) and the outcome. Over the past decade, many statistical methods have been proposed to adjust for the confounders in hypothesis testing. We unify these methods in the same framework, generalize them to include multiple primary variables and multiple nuisance variables, and analyze their statistical properties. In particular, we provide theoretical guarantees for RUV-4 [Gagnon-Bartsch, Jacob and Speed (2013)] and LEAPP [Ann. Appl. Stat. 6 (2012) 1664-1688], which correspond to two different identification conditions in the framework: the first requires a set of "negative controls" that are known a priori to follow the null distribution; the second requires the true nonnulls to be sparse. Two different estimators which are based on RUV-4 and LEAPP are then applied to these two scenarios. We show that if the confounding factors are strong, the resulting estimators can be asymptotically as powerful as the oracle estimator which observes the latent confounding factors. For hypothesis testing, we show the asymptotic z-tests based on the estimators can control the type I error. Numerical experiments show that the false discovery rate is also controlled by the Benjamini-Hochberg procedure when the sample size is reasonably large.
| S-EPMC6706069 | biostudies-literature
Project description:BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.ResultsOur simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.ConclusionsTight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.
| S-EPMC11423519 | biostudies-literature
Project description:Many studies in arthroplasty research are based on nonrandomized, retrospective, registry-based cohorts. In these types of studies, patients belonging to different treatment or exposure groups often differ with respect to patient characteristics, medical histories, surgical indications, or other factors. Consequently, comparisons of nonrandomized groups are often subject to treatment selection bias and confounding. Propensity scores can be used to balance cohort characteristics, thus helping to minimize potential bias and confounding. This article explains how propensity scores are created and describes multiple ways in which they can be applied in the analysis of nonrandomized studies. Please visit the following (https://www.youtube.com/watch?v=sqgxl_nZWS4&t=3s) for a video that explains the highlights of the paper in practical terms.
| S-EPMC10023476 | biostudies-literature
Project description:In environmental and nutritional epidemiology and in many other fields, there is increasing interest in estimating the effect of simultaneous exposure to several agents (e.g., multiple nutrients, pesticides, or air pollutants) on a health outcome. We consider estimating the effect of a multivariate exposure that includes several continuous agents and their interactions-on an outcome, when the true confounding variables are an unknown subset of a potentially large (relative to sample size) set of measured covariates. Our approach is rooted in the ideas of Bayesian model averaging: the exposure effect is estimated as a weighted average of the estimated exposure effects obtained under several linear regression models that include different sets of the potential confounders. We introduce a data-driven prior that assigns to the likely confounders a higher probability of being included into the regression model. We show that our approach can also be formulated as a penalized likelihood formulation with an interpretable tuning parameter. Through a simulation study, we demonstrate that the proposed approach identifies parsimonious models that are fully adjusted for observed confounding and estimates the multivariate exposure effect with smaller mean squared error compared to several alternatives. We apply the method to an Environmental Wide Association Study using National Heath and Nutrition Examination Survey to estimate the effect of mixtures of nutrients and pesticides on lipid levels.
| S-EPMC6518418 | biostudies-literature
Project description:Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g., positivity). The disease risk score (DRS) is an alternative confounding score that can relax some of these assumptions. Like the propensity score, the DRS summarizes multiple confounders into a single score, on which conditioning by matching allows the estimation of causal effects. However, matching relies on arbitrary choices for pruning out data (e.g., matching ratio, algorithm, and caliper width) and may be computationally demanding. Alternatively, weighting methods, common in propensity score analysis, are easy to implement and may entail fewer choices, yet none have been developed for the DRS. Here we present 2 weighting approaches: One derives directly from inverse probability weighting; the other, named target distribution weighting, relates to importance sampling. We empirically show that inverse probability weighting and target distribution weighting display performance comparable to matching techniques in terms of bias but outperform them in terms of efficiency (mean squared error) and computational speed (up to >870 times faster in an illustrative study). We illustrate implementation of the methods in 2 case studies where we investigate placebo treatments for multiple sclerosis and administration of aspirin in stroke patients.
| S-EPMC10840080 | biostudies-literature
Project description:BackgroundObservational studies of medical interventions or risk factors are potentially biased by unmeasured confounding. In this paper we propose a Bayesian approach by defining an informative prior for the confounder-outcome relation, to reduce bias due to unmeasured confounding. This approach was motivated by the phenomenon that the presence of unmeasured confounding may be reflected in observed confounder-outcome relations being unexpected in terms of direction or magnitude.MethodsThe approach was tested using simulation studies and was illustrated in an empirical example of the relation between LDL cholesterol levels and systolic blood pressure. In simulated data, a comparison of the estimated exposure-outcome relation was made between two frequentist multivariable linear regression models and three Bayesian multivariable linear regression models, which varied in the precision of the prior distributions. Simulated data contained information on a continuous exposure, a continuous outcome, and two continuous confounders (one considered measured one unmeasured), under various scenarios.ResultsIn various scenarios the proposed Bayesian analysis with an correctly specified informative prior for the confounder-outcome relation substantially reduced bias due to unmeasured confounding and was less biased than the frequentist model with covariate adjustment for one of the two confounding variables. Also, in general the MSE was smaller for the Bayesian model with informative prior, compared to the other models.ConclusionsAs incorporating (informative) prior information for the confounder-outcome relation may reduce the bias due to unmeasured confounding, we consider this approach one of many possible sensitivity analyses of unmeasured confounding.
| S-EPMC6303957 | biostudies-literature
Project description:With high-dimensional data, variable-by-variable statistical testing is often used to select variables whose behavior differs across conditions. Such an approach requires adjustment for multiple testing, which can result in low statistical power. A two-stage approach that first filters variables by a criterion independent of the test statistic, and then only tests variables which pass the filter, can provide higher power. We show that use of some filter/test statistics pairs presented in the literature may, however, lead to loss of type I error control. We describe other pairs which avoid this problem. In an application to microarray data, we found that gene-by-gene filtering by overall variance followed by a t-test increased the number of discoveries by 50%. We also show that this particular statistic pair induces a lower bound on fold-change among the set of discoveries. Independent filtering-using filter/test pairs that are independent under the null hypothesis but correlated under the alternative-is a general approach that can substantially increase the efficiency of experiments.
| S-EPMC2906865 | biostudies-literature
Project description:Background"Single-arm trials" with external comparators that contrast outcomes in those on experimental therapy to real-world patients have been used to evaluate efficacy and safety of experimental drugs in rare and severe diseases. Regulatory agencies are considering expanding the role these studies can play; guidance thus far has explicitly considered outcome misclassification with little discussion of misclassification of confounding variables.ObjectivesThis work uses causal diagrams to illustrate how adjustment for a misclassified confounder can result in estimates farther from the truth than ignoring it completely. This theory is augmented with quantitative examples using plausible values for misclassification of smoking in real-world pharmaceutical claims data. A tool is also provided for calculating bias of adjusted estimates with specific input parameters.ResultsWhen confounder misclassification is similar in both data sources, adjustment generally brings estimates closer to the truth. When it is not, adjustment can generate estimates that are considerably farther from the truth than the crude. While all nonrandomized studies are subject to this potential bias, single-arm studies are particularly vulnerable due to perfect alignment of confounder measurement and treatment group. This is most problematic when the prevalence of the confounder does not differ between data sources and misclassification does, but can occur even with strong confounder-data source associations.DiscussionResearchers should consider differential confounder misclassification when designing protocols for these types of studies. Subsample validation of confounders, followed by imputation or other bias correction methods, may be a key tool for combining trial and real-world data going forward.
| S-EPMC7665993 | biostudies-literature