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序贯治疗决策中最优动态治疗方案的稳健估计AI 解读

A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient's history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression…

利用辅助协变量提高对数秩检验的效率AI 解读

Under the assumption of proportional hazards, the log-rank test is optimal for testing the null hypothesis ⁠, where denotes the logarithm of the hazard ratio. However, if there are additional covariates that correlate with survival times, making use of their information will increase the efficiency of the log-rank test. We apply the theory of semiparametrics to characterize a class of regular and asymptotically linear estimators for when auxiliary covariates are incorporated…

函数型测量误差模型的局部有效半参数估计量AI 解读

A class of semiparametric estimators are proposed in the general setting of functional measurement error models. The estimators follow from estimating equations that are based on the semiparametric efficient score derived under a possibly incorrect distributional assumption for the unobserved ‘measured with error’ covariates. It is shown that such estimators are consistent and asymptotically normal even with misspecification and are efficient if computed under the truth. The…

临床试验监测中自适应设计的低效率问题AI 解读

Adaptive designs, which allow the sample size to be modified based on sequentially computed observed treatment differences, have been advocated recently for monitoring clinical trials. Although such methods have a great deal of appeal on the surface, we show that such methods are inefficient and that one can improve uniformly on such adaptive designs using standard group‐sequential tests based on the sequentially computed likelihood ratio test statistic.

失效原因缺失时检验生存分布治疗差异的多重插补方法AI 解读

We propose a method for comparing survival distributions when cause‐of‐failure information is missing for some individuals. We use multiple imputation to impute missing causes of failure, where the probability that a missing cause is that of interest may depend on auxiliary covariates, and combine log‐rank statistics computed from several ‘completed’ datasets into a test statistic that achieves asymptotically the nominal level. Simulations demonstrate the relevance of the…

存在纵向协变量测量误差的比例风险模型半参数估计AI 解读

A common objective in longitudinal studies is to characterise the relationship between a failure time process and time‐independent and time‐dependent covariates. Time‐dependent covariates are generally available as longitudinal data collected periodically during the course of the study. We assume that these data follow a linear mixed effects model with normal measurement error and that the hazard of failure depends both on the underlying random effects describing the…

现时状态数据分析的半参数比例优势回归模型AI 解读

An approach to covariate analysis of current status data is described. The method is applicable when the logit of the conditional probability of survival given the covariates is some increasing function of time plus a linear combination of the covariates. The approach is based on approximating the infinite-dimensional nuisance parameter, the baseline log-odds of failure, with a step function, and carrying out a maximum likelihood procedure. The resulting finite dimensional…

参数生存模型的序贯方法AI 解读

SUMMARY The asymptotic sequential distribution is derived for score tests and maximum likelihood estimates in parametric survival models in the presence of nuisance parameters where individuals enter over time and the data are subject to right censoring. It is shown that these sequential tests and estimates have an independent increments structure. This allows the use of standard group sequential methods for early stopping.