Resampling-based Multiple Testing Methods with Covariate Adjustment: Application to Investigation of Antiretroviral Drug Susceptibility

Victor DeGruttola
Department of Biostatistics
Harvard University

Wednesday, April 23rd
3:30pm
MoosT 5-125
Minneapolis Campus

Abstract:
Identification of patterns of genetic mutations that are associated with clinical resistance to specific antiretroviral drugs in HIV-infected patients requires adjustment for potential confounders, such as the number of active drugs in a patient's regimen other than the one of interest. A variety of methods (e.g. regression trees, neural networks, support vector regression, least squares regression, least angle regression) are available for fitting high dimensional models, that are especially useful for prediction. Our goal focuses on the discovery of important patterns of mutations associated with resistance to a specific drug, after robust adjustment for the impact of covariates. Motivated by this problem, we investigated resampling-based methods to test equal mean response across multiple groups defined by HIV genotype, after adjustment for covariates. We consider construction of test statistics and their null distributions under two types of model: parametric and semiparametric. The covariate function (e.g., linear or quadratic) is explictly specified in the parametric but not in the semiparametric approach. The parametric approach is more precise when models are correctly specified, but suffers from bias when they are not; the semiparametric approach is more robust to model misspecification, but may be less efficient. To help preserve Type I error while also improving power in both approaches, we propose resampling approaches based on matching of observations with similar covariate values. Matching reduces the impact of model misspecification as well as imprecision in estimation. These methods are evaluated via simulation studies and applied to a data set that combines results from a variety of clinical studies of salvage regimens. Our focus is on relating HIV genotype to viralogical response to abacavir after adjustment for the number of active antiretroviral drugs (excluding abacavir) in the patient's regimen. Illustrative data were provided by the Forum for HIV Collaborative Research, which collected baseline genotype, treatment history, and virological response on over 1300 patients from a range of clinical research studies in North America and Europe. These methods are extended to consider the identification of single nucleotide polymorphisms (SNPs) associated with toxicities related to antiretroviral drugs; an additional challenge in this research arises from fact that the genotype is unphased.

A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html