In this paper, we implement a micro-simulation (MS) model for colorectal cancer and report the process of the MS model applying to Japanese data. As an advantage of MS, it is possible to evaluate the effects of interventional cancer control program based on various scenario setting. Although there are many advanced MS projects in policy making in other countries, they are still on the way of developing in Japan. Then, we have tried to construct a colorectal cancer MS model in Japan. The purpose of this paper is to describe the process of colorectal cancer MS in Japan as a useful manual for future reference, and to introduce an on-going research project and future possibility of research extensions.
In multicenter clinical trials, the assessment for heterogeneity of various relevant factors across participating centers is a relevant issue because it can cause inconsistency of the treatment effects. Especially, outlying centers with extreme profiles can influence the overall conclusions of these trials. In this article, we propose quantitative methods to detect the outlying centers and to assess their influences in multicenter clinical trials. We proposed four effective methods based on (1) a studentized residual obtained by a leave-one-out analysis, (2) a model-based significance test to detect an outlying trial using a mean-shifted model, (3) a relative change measure for the variance estimate of the overall treatment effect estimator, and (4) a relative change measure for the heterogeneity variance estimate in a random-effects model. In addition, we provide parametric bootstrap algorithms to assess the statistical variability of their influential measures. We also demonstrate the practical effectiveness of these proposed methods via applications to two clinical trials for benign prostatic hyperplasia and cardiovascular heart disease.
The case-only approach is widely known as a resource-efficient approach to identify biomarkers that interact with a treatment. It measures stored baseline samples from trial participants who experience the outcome. The previously proposed case-only approach for binary outcomes is most commonly used in early-stage, retrospective profiling experiments for selecting biomarkers. In this article, we compare power and alpha error for assessment of interaction between a treatment and a baseline marker from the case-only approach to those from the full-cohort approach in which all the baseline samples are measured. We calculated power and alpha error based on a test for interaction in the case-only approach and then compared the power and alpha error of the test for interaction in the case-only approach and the full-cohort approach. We showed that the case-only approach can get 80% statistical power under the scenarios of the risk ratio=0.5, probability of outcome event=0.5-0.7, marker expression probability=0.3-0.7 and case sample size>300. We could get enough power in full-cohort approach in the RR=0.5-0.7, while the case-only approach gave less than 80% power. We revealed that the case-only approach can get 80% power and situations that the case-only approach or the full-cohort approach should be used.