Addressing missing covariates for the regression analysis of competing risks: Prognostic modelling for triaging patients diagnosed with prostate cancer
| Autor: | Gabriel Escarela, Juan Ruiz-de-Chavez, Alberto Castillo-Morales |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Male
Risk Statistics and Probability Epidemiology 030232 urology & nephrology Bivariate analysis 01 natural sciences Copula (probability theory) Cohort Studies 010104 statistics & probability 03 medical and health sciences Prostate cancer 0302 clinical medicine Health Information Management Statistics Covariate medicine Econometrics Humans 0101 mathematics Aged Cause of death Likelihood Functions business.industry Prostatic Neoplasms Regression analysis Middle Aged Prognosis Missing data medicine.disease Mixture model National Cancer Institute (U.S.) United States Regression Analysis Triage business |
| Zdroj: | Statistical Methods in Medical Research. 25:1579-1595 |
| ISSN: | 1477-0334 0962-2802 |
| DOI: | 10.1177/0962280213492406 |
| Popis: | Competing risks arise in medical research when subjects are exposed to various types or causes of death. Data from large cohort studies usually exhibit subsets of regressors that are missing for some study subjects. Furthermore, such studies often give rise to censored data. In this article, a carefully formulated likelihood-based technique for the regression analysis of right-censored competing risks data when two of the covariates are discrete and partially missing is developed. The approach envisaged here comprises two models: one describes the covariate effects on both long-term incidence and conditional latencies for each cause of death, whilst the other deals with the observation process by which the covariates are missing. The former is formulated with a well-established mixture model and the latter is characterised by copula-based bivariate probability functions for both the missing covariates and the missing data mechanism. The resulting formulation lends itself to the empirical assessment of non-ignorability by performing sensitivity analyses using models with and without a non-ignorable component. The methods are illustrated on a 20-year follow-up involving a prostate cancer cohort from the National Cancer Institutes Surveillance, Epidemiology, and End Results program. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|