Two-stage estimation on adjustment for cross-over in oncology trials / 中国临床药理学与治疗学

AIM: To investigate the application of two-stage estimation (TSE) on adjustment for treatment switch in oncology trials. METHODS: The theory and implementation of TSE method was described, and was applied to adjust the data from a two-arm randomized controlled trial of anti-tumor drugs. The changes of survival curves and hazard ratio of two groups after adjustment for cross-over were evaluated. In addition, the results of two-stage estimation and rank preserving structural failure time model (RPSFT) were compared. RESULTS: After adjustment for cross-over using TSE methods, the results showed that the median survival time of control group was shorter than the original one, and the hazard ratio was lower than the observed value. Moreover, TSE method showed similar results to rank preserving structural failure time model. CONCLUSION: The TSE method is relatively simple to use, reliable and has a good practice property in cross-over analysis of oncology trials. At the same time, it is necessary to pay attention to its application scopes.

Subgroup identification based on an accelerated failure time model combined with adaptive elastic net / 南方医科大学学报

OBJECTIVE@#We propose a strategy for identifying subgroups with the treatment effect from the survival data of a randomized clinical trial based on accelerated failure time (AFT) model.@*METHODS@#We applied adaptive elastic net to the AFT model (designated as the penalized model) and identified the candidate covariates based on covariate-treatment interactions. To classify the patient subgroups, we utilized a likelihood-based change-point algorithm to determine the threshold cutoff point. A two-stage adaptive design was adopted to verify if the treatment effect existed within the identified subgroups.@*RESULTS@#The penalized model with the main effect of the covariates considerably outperformed the univariate model without the main effect for the trial data with a small sample size, a high censoring rate, a small subgroup size, or a sample size that did not exceed the number of covariates; in other scenarios, the latter model showed better performances. Compared with the traditional design, the adaptive design improved the power for detecting the treatment effect where subgroup effect exists with a well-controlled type Ⅰ error.@*CONCLUSIONS@#The penalized AFT model with the main effect of the covariates has advantages in subgroup identification from the survival data of clinical trials. Compared with the traditional design, the two-stage adaptive design has better performance in evaluation of the treatment effect when a subgroup effect exists.

Gene-Gene Interaction Analysis for the Accelerated Failure Time Model Using a Unified Model-Based Multifactor Dimensionality Reduction Method

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.