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Required sample size and nonreplicability thresholds for heterogeneous genetic associations.

ABSTRACT: Many gene-disease associations proposed to date have not been consistently replicated across different populations. Nonreplication often reflects false positives in the original claims. However, occasionally, nonreplication may be due to heterogeneity due to biases or even genuine diversity of the genetic effects in different populations. Here, we propose methods for estimating the required sample size to replicate an association across many studies with different amounts of between-study heterogeneity, when data are summarized through metaanalysis. We demonstrate thresholds of between-study heterogeneity (tau(0)(2)) above which one cannot reach adequate power to replicate a proposed association at a specified level of statistical significance when k studies are performed (regardless of how large these studies are). Based on empirical evidence from 91 proposed gene-disease associations (50 on candidate genes and 41 from genome-wide association efforts), the observed between-study heterogeneity is often close to or even surpasses nonreplicability thresholds. With more modest between-study heterogeneity, the required sample size increases considerably compared with when no between-study heterogeneity exists. Increases are steep as tau(0)(2) is approached. Therefore, some true associations may not be practically possible to replicate with consistency, no matter how large studies are conducted. Efforts should be made to minimize between-study heterogeneity in targeted genetic effects.

SUBMITTER: Moonesinghe R

PROVIDER: S-EPMC2206585 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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Required sample size and nonreplicability thresholds for heterogeneous genetic associations.

Moonesinghe Ramal R Khoury Muin J MJ Liu Tiebin T Ioannidis John P A JP

Proceedings of the National Academy of Sciences of the United States of America 20080103 2

Many gene-disease associations proposed to date have not been consistently replicated across different populations. Nonreplication often reflects false positives in the original claims. However, occasionally, nonreplication may be due to heterogeneity due to biases or even genuine diversity of the genetic effects in different populations. Here, we propose methods for estimating the required sample size to replicate an association across many studies with different amounts of between-study hetero ...[more]

PMID: 18174335

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Project description:Risk-prediction models for health outcomes are used in practice as part of clinical decision-making, and it is essential that their performance be externally validated. An important aspect in the design of a validation study is choosing an adequate sample size. In this paper, we investigate the sample size requirements for validation studies with binary outcomes to estimate measures of predictive performance (C-statistic for discrimination and calibration slope and calibration in the large). We aim for sufficient precision in the estimated measures. In addition, we investigate the sample size to achieve sufficient power to detect a difference from a target value. Under normality assumptions on the distribution of the linear predictor, we obtain simple estimators for sample size calculations based on the measures above. Simulation studies show that the estimators perform well for common values of the C-statistic and outcome prevalence when the linear predictor is marginally Normal. Their performance deteriorates only slightly when the normality assumptions are violated. We also propose estimators which do not require normality assumptions but require specification of the marginal distribution of the linear predictor and require the use of numerical integration. These estimators were also seen to perform very well under marginal normality. Our sample size equations require a specified standard error (SE) and the anticipated C-statistic and outcome prevalence. The sample size requirement varies according to the prognostic strength of the model, outcome prevalence, choice of the performance measure and study objective. For example, to achieve an SE < 0.025 for the C-statistic, 60-170 events are required if the true C-statistic and outcome prevalence are between 0.64-0.85 and 0.05-0.3, respectively. For the calibration slope and calibration in the large, achieving SE < 0.15 would require 40-280 and 50-100 events, respectively. Our estimators may also be used for survival outcomes when the proportion of censored observations is high.

Dataset Information

Required sample size and nonreplicability thresholds for heterogeneous genetic associations.

Publications

Required sample size and nonreplicability thresholds for heterogeneous genetic associations.

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