Dataset Information

A mixture model approach in gene-gene and gene-environmental interactions for binary phenotypes.

ABSTRACT: In translational research, a genetic association study of a binary outcome has a twofold aim: test whether genetic/environmental variables or their combinations are associated with a clinical phenotype, and determine how those combinations are grouped to predict the phenotype (i.e., which combinations have a similarly distributed phenotype, and which ones have differently distributed phenotypes). The second part of this aim has high clinical appeal, because it can directly facilitate clinical decisions. Although traditional logistic regression can detect gene-gene or gene-environmental interaction effects on binary phenotypes, they cannot decisively determine how genotype combinations are grouped to predict the phenotype. Our proposed mixture model approach is valuable in this context. It concurrently detects main and interaction effects of genetic and environmental variables through a likelihood ratio test (LRT) and conducts phenotype cluster analysis based on genetic and environmental variable combinations. The theoretical distribution of the proposed mixture model's likelihood ratio test is robust not only to small sample size but also to unequal sample size in various genotype and environmental subgroups. Hypothesis testing through a likelihood ratio test results in a fast algorithm for p-value calculations. Extensive simulation studies demonstrate that mixture model, overall test in logistic regression, and Monte Carlo based logic regression constantly possess the best power to detect multi-way gene/environmental combinations. The mixture model approach has the highest recovery probability to recover the true partition in the simulation studies. Its applications are exemplified in interim data analyses for two cancer studies.

SUBMITTER: Li L

PROVIDER: S-EPMC2790052 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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A mixture model approach in gene-gene and gene-environmental interactions for binary phenotypes.

Li Lang L Yu Menggang M Jason Robarge D RD Shen Changyu C Azzouz Faouzi F McLeod Howard L HL Borges-Gonzales Silvana S Nguyen Anne A Skaar Todd T Desta Zeruesenay Z Sweeney Christopher J CJ Flockhart David A DA

Journal of biopharmaceutical statistics 20080101 6

In translational research, a genetic association study of a binary outcome has a twofold aim: test whether genetic/environmental variables or their combinations are associated with a clinical phenotype, and determine how those combinations are grouped to predict the phenotype (i.e., which combinations have a similarly distributed phenotype, and which ones have differently distributed phenotypes). The second part of this aim has high clinical appeal, because it can directly facilitate clinical de ...[more]

PMID: 18991114

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Project description:BackgroundTrials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The performance of the one-stage and the two-stage approaches has been documented extensively in the literature. However, little is known about how these approaches behave in the presence of missing outcome data (MOD), which are ubiquitous in clinical trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis using Bayesian methods to handle MOD appropriately.MethodsWe used 29 published networks to empirically compare the two approaches concerning the relative treatment effects of several competing interventions and the between-trial variance (?2), while considering the extent and level of balance of MOD in the included trials. We additionally conducted a simulation study to compare the competing approaches regarding the bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and ?2 in the presence of moderate and large MOD.ResultsThe empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. For these networks, the empirical results revealed slightly higher ?2 estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and ?2 when compared with the two-stage approach for large MOD.ConclusionsDue to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

Dataset Information

A mixture model approach in gene-gene and gene-environmental interactions for binary phenotypes.

Publications

A mixture model approach in gene-gene and gene-environmental interactions for binary phenotypes.

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