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An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.

ABSTRACT: For analysis of the main effects of SNPs, meta-analysis of summary results from individual studies has been shown to provide comparable results as "mega-analysis" that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene-environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of gene-environment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable P-values. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Mega-analysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that meta-analysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating meta-versus mega-analyses for interactions.

SUBMITTER: Sung YJ

PROVIDER: S-EPMC4332385 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.

Sung Yun Ju YJ Schwander Karen K Arnett Donna K DK Kardia Sharon L R SL Rankinen Tuomo T Bouchard Claude C Boerwinkle Eric E Hunt Steven C SC Rao Dabeeru C DC

Genetic epidemiology 20140409 4

For analysis of the main effects of SNPs, meta-analysis of summary results from individual studies has been shown to provide comparable results as "mega-analysis" that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene-environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals ...[more]

PMID: 24719363

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Project description:BackgroundExposure to faecal contamination is believed to be associated with child diarrhoea and possibly stunting; however, few studies have explicitly measured the association between faecal contamination and health. We aimed to assess individual participant data (IPD) across multiple trials and observational studies to quantify the relationship for common faecal-oral transmission pathways.MethodsWe did a systematic review and meta-analysis of IPD from studies identified in an electronic search of PubMed, Web of Science, and Embase on May 21, 2018. The search was done in English, but full texts published in French, Portuguese, and Spanish were also reviewed. Eligible studies quantified (1) household-level faecal indicator bacteria concentrations along common faecal-oral transmission pathways of drinking water, soil, or food, on children's hands or fomites, or fly densities in food preparation areas; and (2) individual-level diarrhoea or linear growth measures for children younger than 5 years in low-income and middle-income countries. For the diarrhoea analysis, all definitions of diarrhoea were eligible but studies were excluded if they used a recall period longer than 7 days. For the linear growth analysis (using height-for-age Z scores [HAZ]), cross-sectional studies were excluded, because of the absence of longitudinal environmental contamination data measured before the growth outcomes. We used multilevel generalised mixed-effects models to estimate the odds ratio (OR) for diarrhoea and the difference in HAZ scores for individual studies associated with a 1-log10 higher measure of faecal contamination. Estimates from each study were combined under a random-effects meta-analysis framework. The study protocol was pre-registered with PROSPERO (CRD42018102114).FindingsFrom 72 eligible studies, we included IPD for 20 studies in the meta-analyses, totalling 54 225 diarrhoea or linear growth observations matched to faecal indicator bacteria concentrations in drinking water, and a further 35 010 observations with faecal contamination data for the other transmission pathways. Child diarrhoea was associated with 1-log10 higher faecal indicator bacteria concentrations in drinking water (OR 1·09, 95% CI 1·04 to 1·13; p=0·0002, I2=34%, 95% CI 0 to 62) and on children's hands (1·11, 1·02 to 1·22; p=0·021, I2=0%, 0 to 71). Lower HAZ scores were associated with 1-log10 higher median faecal indicator bacteria concentrations in drinking water (HAZ -0·04, 95% CI -0·06 to -0·01; p=0·0054; I2=19%, 95% CI 0 to 63) and on fomites (-0·06, -0·12 to 0·00; p=0·044, I2=57%, 0 to 90).InterpretationAlthough summary measures from individual studies often report little or no effect of measured faecal contamination on child health, this multi-study IPD analysis indicates that household faecal indicator bacteria concentrations are associated with important adverse health outcomes in young children. Improved direct measures of exposure and enteric pathogens could help to better characterise the relationship and inform intervention design in future studies.FundingNone.

Project description:BackgroundIndividual participant data (IPD) meta-analyses that obtain "raw" data from studies rather than summary data typically adopt a "two-stage" approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of "one-stage" approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare "two-stage" and "one-stage" models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way.Methods and findingsWe included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model.ConclusionsFor these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

Project description:BackgroundIt remains unclear when standard systematic reviews and meta-analyses that rely on published aggregate data (AD) can provide robust clinical conclusions. We aimed to compare the results from a large cohort of systematic reviews and meta-analyses based on individual participant data (IPD) with meta-analyses of published AD, to establish when the latter are most likely to be reliable and when the IPD approach might be required.Methods and findingsWe used 18 cancer systematic reviews that included IPD meta-analyses: all of those completed and published by the Meta-analysis Group of the MRC Clinical Trials Unit from 1991 to 2010. We extracted or estimated hazard ratios (HRs) and standard errors (SEs) for survival from trial reports and compared these with IPD equivalents at both the trial and meta-analysis level. We also extracted or estimated the number of events. We used paired t tests to assess whether HRs and SEs from published AD differed on average from those from IPD. We assessed agreement, and whether this was associated with trial or meta-analysis characteristics, using the approach of Bland and Altman. The 18 systematic reviews comprised 238 unique trials or trial comparisons, including 37,082 participants. A HR and SE could be generated for 127 trials, representing 53% of the trials and approximately 79% of eligible participants. On average, trial HRs derived from published AD were slightly more in favour of the research interventions than those from IPD (HRAD to HRIPD ratio = 0.95, p = 0.007), but the limits of agreement show that for individual trials, the HRs could deviate substantially. These limits narrowed with an increasing number of participants (p < 0.001) or a greater number (p < 0.001) or proportion (p < 0.001) of events in the AD. On average, meta-analysis HRs from published AD slightly tended to favour the research interventions whether based on fixed-effect (HRAD to HRIPD ratio = 0.97, p = 0.088) or random-effects (HRAD to HRIPD ratio = 0.96, p = 0.044) models, but the limits of agreement show that for individual meta-analyses, agreement was much more variable. These limits tended to narrow with an increasing number (p = 0.077) or proportion of events (p = 0.11) in the AD. However, even when the information size of the AD was large, individual meta-analysis HRs could still differ from their IPD equivalents by a relative 10% in favour of the research intervention to 5% in favour of control. We utilised the results to construct a decision tree for assessing whether an AD meta-analysis includes sufficient information, and when estimates of effects are most likely to be reliable. A lack of power at the meta-analysis level may have prevented us identifying additional factors associated with the reliability of AD meta-analyses, and we cannot be sure that our results are generalisable to all outcomes and effect measures.ConclusionsIn this study we found that HRs from published AD were most likely to agree with those from IPD when the information size was large. Based on these findings, we provide guidance for determining systematically when standard AD meta-analysis will likely generate robust clinical conclusions, and when the IPD approach will add considerable value.

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An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.

Publications

An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.

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