B1218
Title: Identifying treatment-sensitive subgroups based on multiple covariates and longitudinal measurements
Authors: Yingwei Peng - Queen\'s University (Canada) [presenting]
Abstract: Identifying a subgroup of patients who may be sensitive to a specific treatment is an important step towards personalized medicine. We consider the effect of a treatment on longitudinal measurements, which may be continuous or categorical, such as quality of life scores assessed over the duration of a clinical trial. We assume multiple baseline covariates, such as age and expression levels of genes, are available and propose a generalized single-index linear threshold model to simultaneously identify the treatment-sensitive subgroup and assess the treatment-by-subgroup interaction. Because the model involves an indicator function with unknown parameters, conventional procedures are difficult to apply for the inferences of parameters in the model. We define smoothed generalized estimating equations and propose an inference procedure based on these equations with an efficient spectral algorithm employed to find their solutions. The proposed procedure is evaluated through simulation studies and application to the analysis of data from a randomized clinical trial in advanced pancreatic cancer.
