Efficient estimation of semiparametric transformation models for the cumulative incidence of competing risks

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 79; no. 2; pp. 573 - 587
• Main Authors: Mao, Lu, Lin, D.Y.
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons Ltd 01.03.2017; Oxford University Press
• Subjects: Algorithms; Bone marrow; bone marrow transplant; Censoring; Consistency; equations; Estimating techniques; Failure; Incidence; Mathematical models; Maximum likelihood estimators; Maximum likelihood method; Non‐parametric maximum likelihood estimation; Probability; Profile likelihood; Proportional hazards; Regression; regression analysis; Risk; Semiparametric efficiency; Statistical methods; Statistics; Studies; Survival analysis; Transformations; Transplants & implants; Semiparametric efficiency; Profile likelihood; Survival analysis; Proportional hazards; Censoring; Nonparametric maximum likelihood estimation

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 has become the method of choice for formulating the effects of covariates on the cumulative inciden...