The proportional odds cumulative incidence model for competing risks

Cumulative incidence estimates in the presence of competing risks.

When competing risks are present, the appropriate estimate of the failure probabilities is the cumulative incidence. stcompet creates new variables containing the estimate of this function, its standard error, and ln(− ln) transformed confidence bounds. Two examples are presented to illustrate the use of the new command and some key features of the cumulative incidence.

Cumulative incidence in competing risks data and competing risks regression analysis.

Competing risks occur commonly in medical research. For example, both treatment-related mortality and disease recurrence are important outcomes of interest and well-known competing risks in cancer research. In the analysis of competing risks data, methods of standard survival analysis such as the Kaplan-Meier method for estimation of cumulative incidence, the log-rank test for comparison of cum...

Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks.

The cumulative incidence is the probability of failure from the cause of interest over a certain time period in the presence of other risks. A semiparametric regression model proposed by Fine and Gray (1999) has become the method of choice for formulating the effects of covariates on the cumulative incidence. Its estimation, however, requires modeling of the censoring distribution and is not st...

Variable selection for proportional odds model.

In this paper we study the problem of variable selection for the proportional odds model, which is a useful alternative to the proportional hazards model and might be appropriate when the proportional hazards assumption is not satisfied. We propose to fit the proportional odds model by maximizing the marginal likelihood subject to a shrinkage-type penalty, which encourages sparse solutions and ...

Model Diagnostics for Proportional and Partial Proportional Odds Models