BST 290 Seminar Series
Tuesday, March 10th, 2015, at 4:10pm, Location: MSB 1147 (Colloquium Room)
Refreshments at 3:30pm in MSB 4110
Speaker: Lihong Qi (Department of Public Health Sciences, UC Davis)
Title: A Comparison of Multiple Imputation via Chained Equations and General Location Model for Accelerated Failure Time Models with Missing Covariates
Abstract: Missing covariates are common in biomedical studies with survival outcomes. Multiple imputation is a practical strategy for handling this problem with various approaches and software packages available. We compare two important approaches: multiple imputation by chained equation (MICE) and multiple imputation via a general location model (GLM) for accelerated failure time (AFT) models with missing covariates. Through a comprehensive simulation study, we investigate the performance of the two approaches and their robustness toward violation of the GLM assumptions and model misspecifications including misspecifications of the covariance structure and of the joint distribution of continuous covariates. Simulation results show that MICE can be sensitive to model misspecifications and may generate biased results with inflated standard errors while GLM can still yield estimates with reasonable biases and coverages in these situations. MICE is flexible to use but lack of a clear theoretical rationale and suffers from potential incompatibility of the conditional regression models used in imputation. In contrast, GLM is theoretically sound and can be rather robust toward model misspecifications and violations of GLM assumptions. Therefore, we believe that GLM shows the potential for being a competitive and attractive tool for tackling the analysis of AFT models with missing covariates.