Real-World Data Analysis of Pembrolizumab Monotherapy for NSCLC Using Japanese Postmarketing All-Case Surveillance Data.

Introduction: Pembrolizumab is a programmed death-ligand 1 inhibitor that was initially indicated for monotherapy in patients with advanced lung cancer. The Japanese Lung Cancer Society conducted an observational study on pembrolizumab using confirmative data obtained through postmarketing all-case surveillance (PMACS), which was performed by a pharmaceutical company under the Japanese law in 2017. Methods: This multicenter observational study was conducted by the Japanese Lung Cancer Society using PMACS data with the newly created central registration system regarding patients with NSCLC who received pembrolizumab monotherapy between February 1, 2017 and June 30, 2017; a new database was created by adding the clinical information regarding prognosis for 3 years after therapy to the existing data collected by PMACS. Results: A total of 300 patients from 43 facilities were enrolled in this study. The median overall survival and progression-free survival after pembrolizumab initiation were 558 and 188 days, respectively. Moreover, the 1- and 3-year survival rates were 58.9% and 33.7%, respectively. Results of multivariate analysis revealed performance status (p < 0.0001), histology (p = 0.0118), previous chemotherapy (p = 0.0007), programmed death-ligand 1 expression status (p = 0.0195), and previous steroid use (p = 0.0460) as significant factors that affected overall survival. The toxicity profile was similar to that previously reported. Conclusions: In this first attempt to use PMACS data, we successfully collected clinical information and found the real-world efficacy and safety of pembrolizumab.

Construction of a survival tree for dependent censoring.

In this study, we examined the problem of constructing a model for time-to-event data considering dependent censoring. Our goal was to construct a set of subgroups of covariate space, wherein each element had the same failure model considering the dependency of failure and censoring times. As such, a model was constructed based on the parametric form from the identifiability problem of censoring. We used the copula to represent the dependency between failure and censoring times. Under the assumption of parametric models for failure and censoring times and a copula function, which have unknown parameters, we proposed a method for constructing the tree-structured model through the test statistics. We subsequently evaluated the performance of the splitting rule and tree obtained using the proposed method and compared it with the general method that assumes independent censoring through simulation studies. We also present the analysis results for AIDS clinical trial research to show the utility of the method.

On Stratified Adjusted Tests by Binomial Trials.

To estimate or test the treatment effect in randomized clinical trials, it is important to adjust for the potential influence of covariates that are likely to affect the association between the treatment or control group and the response. If these covariates are known at the start of the trial, random assignment of the treatment within each stratum would be considered. On the other hand, if these covariates are not clear at the start of the trial, or if it is difficult to allocate the treatment within each stratum, completely randomized assignment of the treatment would be performed. In both sampling structures, the use of a stratified adjusted test is a useful way to evaluate the significance of the overall treatment effect by reducing the variance and/or bias of the result. If the trial has a binary endpoint, the Cochran and Mantel-Haenszel tests are generally used. These tests are constructed based on the assumption that the number of patients within a stratum is fixed. However, in practice, the stratum sizes are not fixed at the start of the trial in many situations, and are instead allowed to vary. Therefore, there is a risk that using these tests under such situations would result in an error in the estimated variation of the test statistics. To handle the problem, we propose new test statistics under both sampling structures based on multinomial distributions. Our proposed approach is based on the Cochran test, and the difference between the two tests tends to have similar values in the case of a large number of patients. When the total number of patients is small, our approach yields a more conservative result. Through simulation studies, we show that the new approach could correctly maintain the type I error better than the traditional approach.

A combination of TERT promoter mutation and MGMT methylation status predicts clinically relevant subgroups of newly diagnosed glioblastomas.

The prognostic impact of TERT mutations has been controversial in IDH-wild tumors, particularly in glioblastomas (GBM). The controversy may be attributable to presence of potential confounding factors such as MGMT methylation status or patients' treatment. This study aimed to evaluate the impact of TERT status on patient outcome in association with various factors in a large series of adult diffuse gliomas. We analyzed a total of 951 adult diffuse gliomas from two cohorts (Cohort 1, n = 758; Cohort 2, n = 193) for IDH1/2, 1p/19q, and TERT promoter status. The combined IDH/TERT classification divided Cohort 1 into four molecular groups with distinct outcomes. The overall survival (OS) was the shortest in IDH wild-type/TERT mutated groups, which mostly consisted of GBMs (P < 0.0001). To investigate the association between TERT mutations and MGMT methylation on survival of patients with GBM, samples from a combined cohort of 453 IDH-wild-type GBM cases treated with radiation and temozolomide were analyzed. A multivariate Cox regression model revealed that the interaction between TERT and MGMT was significant for OS (P = 0.0064). Compared with TERT mutant-MGMT unmethylated GBMs, the hazard ratio (HR) for OS incorporating the interaction was the lowest in the TERT mutant-MGMT methylated GBM (HR, 0.266), followed by the TERT wild-type-MGMT methylated (HR, 0.317) and the TERT wild-type-MGMT unmethylated GBMs (HR, 0.542). Thus, patients with TERT mutant-MGMT unmethylated GBM have the poorest prognosis. Our findings suggest that a combination of IDH, TERT, and MGMT refines the classification of grade II-IV diffuse gliomas.

Tree Based Method for Aggregate Survival Data Modeling.

In many scenarios, a patient in medical research is treated as a statistical unit. However, in some scenarios, we are interested in treating aggregate data as a statistical unit. In such situations, each set of aggregated data is considered to be a concept in a symbolic representation, and each concept has a hyperrectangle or multiple points in the variable space. To construct a tree-structured model from these aggregate survival data, we propose a new approach, where a datum can be included in several terminal nodes in a tree. By constructing a model under this condition, we expect to obtain a more flexible model while retaining the interpretive ease of a hierarchical structure. In this approach, the survival function of concepts that are partially included in a node is constructed using the Kaplan-Meier method, where the number of events and risks at each time point is replaced by the expectation value of the number of individual descriptions of concepts. We present an application of this proposed model using primary brain tumor patient data. As a result, we obtained a new interpretation of the data in comparison to the classical survival tree modeling methods.

A Bayesian equivalency test for two independent binomial proportions.

In clinical trials, it is often necessary to perform an equivalence study. The equivalence study requires actively denoting equivalence between two different drugs or treatments. Since it is not possible to assert equivalence that is not rejected by a superiority test, statistical methods known as equivalency tests have been suggested. These methods for equivalency tests are based on the frequency framework; however, there are few such methods in the Bayesian framework. Hence, this article proposes a new index that suggests the equivalency of binomial proportions, which is constructed based on the Bayesian framework. In this study, we provide two methods for calculating the index and compare the probabilities that have been calculated by these two calculation methods. Moreover, we apply this index to the results of actual clinical trials to demonstrate the utility of the index.

A comparative study on splitting criteria of a survival tree based on the Cox proportional model.

We treat the situations that the effect of covariates on hazard is differed in subgroups of patients. To handle this situation, we can consider the hybrid model of the Cox model and tree-structured model. Through simulation studies, we compared several splitting criteria for constructing this hybrid model. As a result, the criterion using the degree of the improvement in the negative maximum partial log-likelihood obtained by splitting showed a good performance for many situations. We also present the results obtained by applying this tree model in an actual medical research study to show its utility.

Comparison of splitting methods on survival tree.

We compare splitting methods for constructing survival trees that are used as a model of survival time based on covariates. A number of splitting criteria on the classification and regression tree (CART) have been proposed by various authors, and we compare nine criteria through simulations. Comparative studies have been restricted to criteria that suppose the survival model for each terminal node in the final tree as a non-parametric model. As the main results, the criteria using the exponential log-likelihood loss, log-rank test statistics, the deviance residual under the proportional hazard model, or square error of martingale residual are recommended when it appears that the data have constant hazard with the passage of time. On the other hand, when the data are thought to have decreasing hazard with passage of time, the criterion using the two-sample test statistic, or square error of deviance residual would be optimal. Moreover, when the data are thought to have increasing hazard with the passage of time, the criterion using the exponential log-likelihood loss, or impurity that combines observed times and the proportion of censored observations would be the best. We also present the results of an actual medical research to show the utility of survival trees.