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Multivariate analysis of the population representativeness of related clinical studies.

ABSTRACT: To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

SUBMITTER: He Z

PROVIDER: S-EPMC4837055 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Multivariate analysis of the population representativeness of related clinical studies.

He Zhe Z Ryan Patrick P Hoxha Julia J Wang Shuang S Carini Simona S Sim Ida I Weng Chunhua C

Journal of biomedical informatics 20160125

<h4>Objective</h4>To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.<h4>Methods</h4>We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importan ...[more]

PMID: 26820188

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Project description:ImportanceSystematic differences between patients included in randomized clinical trials (RCTs) and the general patient population may influence the generalizability of RCT findings. Comprehensive national registries of patients with end-stage kidney disease who are undergoing dialysis provide a unique opportunity to compare trial and real-world patient cohorts.ObjectiveTo determine if participants in large, multicenter dialysis trials were similar to the general population undergoing dialysis in terms of age, comorbidities, and mortality rate.Data sourcesMEDLINE, PubMed, and the Cochrane Central Register of Controlled Trials were systematically searched on January 6, 2017, for studies published from January 1, 2007, to December 31, 2016. Data sources were published manuscripts, supplementary material, and trial registration information. Data on the general population undergoing dialysis were derived from the US Renal Data System (USRDS). Data were analyzed from March 17 to July 22, 2018.Study selectionRandomized clinical trials enrolling only participants undergoing dialysis for end-stage kidney disease with 100 or more adult participants from 2 or more sites.Data extraction and synthesisAbstract screening and data extraction were performed independently by 2 researchers. Data were pooled using a random-effects model.Main outcomes and measuresThe primary outcome was difference in mean age between the RCT and USRDS populations. Secondary outcomes included differences in mortality rate and comorbidities.ResultsThe search identified 186 RCTs, enrolling 79 104 participants. Compared with the 2011 USRDS population, RCT participants were younger (mean age, 58.9 years; 95% CI, 58.3-59.5 years vs 61.2 years; P < .001), more likely to be male (58.9%; 95% CI, 57.6%-60.1% vs 55.7%; P < .001), and have coronary artery disease (26.9%; 95% CI, 22.2%-31.7% vs 17.7%; P < .001) and less likely to have diabetes (40.2%; 95% CI, 36.7%-43.6% vs 44.2%; P = .03) or heart failure (19.6%; 95% CI, 15.1%-24.0% vs 29.8%; P < .001). The mortality rate per 100 patient-years during trial participation was less than half that of the USRDS population (8.9; 95% CI, 7.8-10.0 vs 18.6; P < .001). The differences in age, mortality, and coronary artery disease remained when studies recruiting only from the United States were considered. Diabetes was more common in RCT participants from the United States than in the registry population.Conclusions and relevanceParticipants in large, multicenter RCTs of patients with end-stage kidney disease undergoing dialysis are younger, have a different pattern of comorbidities, and have a lower mortality rate than the general population of patients undergoing dialysis. This finding has implications for the generalization of trial results to the broader patient population and for future trial design.

Project description:In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance.

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Multivariate analysis of the population representativeness of related clinical studies.

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Multivariate analysis of the population representativeness of related clinical studies.

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