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Inference for correlated effect sizes using multiple univariate meta-analyses.

ABSTRACT: Multivariate meta-analysis, which involves jointly analyzing multiple and correlated outcomes from separate studies, has received a great deal of attention. One reason to prefer the multivariate approach is its ability to account for the dependence between multiple estimates from the same study. However, nearly all the existing methods for analyzing multivariate meta-analytic data require the knowledge of the within-study correlations, which are usually unavailable in practice. We propose a simple non-iterative method that can be used for the analysis of multivariate meta-analysis datasets, that has no convergence problems, and does not require the use of within-study correlations. Our approach uses standard univariate methods for the marginal effects but also provides valid joint inference for multiple parameters. The proposed method can directly handle missing outcomes under missing completely at random assumption. Simulation studies show that the proposed method provides unbiased estimates, well-estimated standard errors, and confidence intervals with good coverage probability. Furthermore, the proposed method is found to maintain high relative efficiency compared with conventional multivariate meta-analyses where the within-study correlations are known. We illustrate the proposed method through two real meta-analyses where functions of the estimated effects are of interest.

SUBMITTER: Chen Y

PROVIDER: S-EPMC4821787 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The goal of expression quantitative trait loci (eQTL) studies is to identify the genetic variants that influence the expression levels of the genes in an organism. High throughput technology has made such studies possible: in a given tissue sample, it enables us to quantify the expression levels of approximately 20 000 genes and to record the alleles present at millions of genetic polymorphisms. While obtaining this data is relatively cheap once a specimen is at hand, obtaining human tissue remains a costly endeavor: eQTL studies continue to be based on relatively small sample sizes, with this limitation particularly serious for tissues as brain, liver, etc.-often the organs of most immediate medical relevance. Given the high-dimensional nature of these datasets and the large number of hypotheses tested, the scientific community has adopted early on multiplicity adjustment procedures. These testing procedures primarily control the false discoveries rate for the identification of genetic variants with influence on the expression levels. In contrast, a problem that has not received much attention to date is that of providing estimates of the effect sizes associated with these variants, in a way that accounts for the considerable amount of selection. Yet, given the difficulty of procuring additional samples, this challenge is of practical importance. We illustrate in this work how the recently developed conditional inference approach can be deployed to obtain confidence intervals for the eQTL effect sizes with reliable coverage. The procedure we propose is based on a randomized hierarchical strategy with a 2-fold contribution: (1) it reflects the selection steps typically adopted in state of the art investigations and (2) it introduces the use of randomness instead of data-splitting to maximize the use of available data. Analysis of the GTEx Liver dataset (v6) suggests that naively obtained confidence intervals would likely not cover the true values of effect sizes and that the number of local genetic polymorphisms influencing the expression level of genes might be underestimated.

Project description:ObjectivesTo systematically review and meta-analyse the evidence for effect modification by refractory status and number of treatment lines in relapsed/refractory multiple myeloma (RRMM); and to assess whether effect modification is likely to invalidate network meta-analyses (NMA) that assume negligible modification.DesignSystematic review, meta-analysis and simulation.Data sourcesWe systematically searched the literature (e.g., OVID Medline) to identify eligible publications in February 2020 and regularly updated the search until January 2022. We also contacted project stakeholders (including industry) ELIGIBILITY CRITERIA: Phase 2 and 3 randomised controlled trials reporting stratified estimates for comparisons with at least one of a prespecified set of treatments relevant for use in Norwegian RRMM patients.OutcomesWe used meta-analysis to estimate relative HRs (RHRs) for overall survival (OS) and progression-free survival (PFS) with respect to refractory status and number of treatment lines. We used the estimated RHRs in simulations to estimate the percentage of NMA results expected to differ significantly in the presence versus absence of effect modification.ResultsAmong the 42 included publications, stratified estimates were published by and extracted from up to 18 (43%) publications and on as many as 8364 patients. Within-study evidence for effect modification is very weak (p>0.05 for 47 of 49 sets of stratified estimates). The largest RHR estimated was 1.32 (95% CI 1.18 to 1.49) for the modifying effect of refractory status on HR for PFS. Simulations suggest that, in the worst case, this would result in only 4.48% (95% CI 4.42% to 4.54%) of NMA estimates differing statistically significantly in the presence versus absence of effect modification.ConclusionsBased on the available evidence, effect modification appears to be sufficiently small that it can be neglected in adequately performed NMAs. NMAs can probably be relied on to provide estimates of HRs for OS and PFS in RRMM, subject to caveats discussed herein.

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Inference for correlated effect sizes using multiple univariate meta-analyses.

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