NSF Award
1007535
Linear models analysis is one of the most appealing statistical methods for its directly interpretable results. The accelerated failure time (AFT) and censored quantile regression (QR) model serve counterparts of the classical linear and uncensored QR model for censored data, and complement the Cox-proportional hazards model. Censored QR, in particular, enriches linear models analysis for censored data by allowing non-constant covariate effects across the distribution of event times. Other regression methods unduly constrain the covariate effects to be constant and fail to provide consistent results. In contrast censored QR allows the treatment effect to be negative for more severe cases (with shorter event-free survival times) but positive in other cases. The AFT and censored QR model are, however, under-utilized as flexible and general methods for estimation, variable selection and inference do not exist. This investigation includes developing (A) flexible estimation methods that work under less stringent conditions than those for existing methods, (B) methods for variable selection, including high dimensional data, and (C) general empirical likelihood(EL) methods parallel to uncensored case. In addition, the general ideas of the proposed research and method developed are applicable to truncation or other censoring types, although they are developed under random right censoring mechanism.<br/><br/>Improving statistical models for predicting medical outcomes is always an important part of statistical research. Thanks to recent advancement in high throughput technologies, a vast amount of potentially useful information, including patient's gene profile, is available and anticipated to lead to much improved prediction. The proposed study investigates novel methods to incorporate those data in building a better statistical model to more accurately predict a patient survival. The type of models to be investigated are also more sophisticated: instead of predicting only an "average" person's survival, they allow prediction for "top 10%, or "bottom 10%", while allowing the survivals can be very differently impacted by the gene profile.
