Regression survival analysis with an assumed copula for dependent censoring: a sensitivity analysis approach.
نویسندگان
چکیده
SUMMARY In clinical studies, when censoring is caused by competing risks or patient withdrawal, there is always a concern about the validity of treatment effect estimates that are obtained under the assumption of independent censoring. Because dependent censoring is nonidentifiable without additional information, the best we can do is a sensitivity analysis to assess the changes of parameter estimates under different assumptions about the association between failure and censoring. This analysis is especially useful when knowledge about such association is available through literature review or expert opinions. In a regression analysis setting, the consequences of falsely assuming independent censoring on parameter estimates are not clear. Neither the direction nor the magnitude of the potential bias can be easily predicted. We provide an approach to do sensitivity analysis for the widely used Cox proportional hazards models. The joint distribution of the failure and censoring times is assumed to be a function of their marginal distributions. This function is called a copula. Under this assumption, we propose an iteration algorithm to estimate the regression parameters and marginal survival functions. Simulation studies show that this algorithm works well. We apply the proposed sensitivity analysis approach to the data from an AIDS clinical trial in which 27% of the patients withdrew due to toxicity or at the request of the patient or investigator.
منابع مشابه
Regression modeling of semicompeting risks data.
Semicompeting risks data are often encountered in clinical trials with intermediate endpoints subject to dependent censoring from informative dropout. Unlike with competing risks data, dropout may not be dependently censored by the intermediate event. There has recently been increased attention to these data, in particular inferences about the marginal distribution of the intermediate event wit...
متن کاملGene selection for survival data under dependent censoring: A copula-based approach.
Dependent censoring arises in biomedical studies when the survival outcome of interest is censored by competing risks. In survival data with microarray gene expressions, gene selection based on the univariate Cox regression analyses has been used extensively in medical research, which however, is only valid under the independent censoring assumption. In this paper, we first consider a copula-ba...
متن کاملPartial Identification and Inference in Censored Quantile Regression: A Sensitivity Analysis
In this paper we characterize the identified set and construct asymptotically valid and non-conservative confidence sets for the quantile regression coeffi cient in a linear quantile regression model, where the dependent variable is subject to possibly dependent censoring. The underlying censoring mechanism is characterized by an Archimedean copula for the dependent variable and the censoring v...
متن کاملProperties of the marginal survival functions for dependent censored data under an assumed Archimedean copula
Given a dependent censored data (X, δ) = (min(T, C), I(T < C)) from an Archimedean copula model, we give general formulas for possible marginal survival functions of T and C. Based on our formulas, we can easily establish the relationship between all these survival functions and derive some useful identifiability results. Also based on our formulas, we propose a new estimator of the marginal su...
متن کاملNonparametric estimation of current status data with dependent censoring.
This paper discusses nonparametric estimation of a survival function when one observes only current status data (McKeown and Jewell, Lifetime Data Anal 16:215-230, 2010; Sun, The statistical analysis of interval-censored failure time data, 2006; Sun and Sun, Can J Stat 33:85-96, 2005). In this case, each subject is observed only once and the failure time of interest is observed to be either sma...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Biometrics
دوره 64 4 شماره
صفحات -
تاریخ انتشار 2008