Analysis of cohort studies with multivariate and partially observed disease classification data

Analysis of cohort studies with multivariate and partially observed disease classification data.

Complex diseases like cancers can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effe...

Supplementary materials : Analysis of Cohort Studies with Multivariate , Partially Observed , Disease Classification Data

Analysis of Cohort Studies with Multivariate, Partially Observed, Disease Classification Data

Complex diseases like cancer can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effec...

Analysis of Multivariate Disease Classification Data in the Presence of Partially Missing Disease Traits

In modern cancer epidemiology, diseases are classified based on pathologic and molecular traits, and different combinations of these traits give rise to many disease subtypes. The effect of predictor variables can be measured by fitting a polytomous logistic model to such data. The differences (heterogeneity) among the relative risk parameters associated with subtypes are of great interest to b...

Classification of Partially Observed Data with Association Trees

Classification methods have troubles with missing data. Even CART, which was designed to deal with missing data, performs poorly when run with over 90% of the predictors unobserved. We use the Apriori algorithm to fit decision trees by converting the continuous predictors to categorical variables, bypassing the missing data problem by treating missing data as absent items. We demonstrate our me...