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There is substantial upward deviation between actual and predicted drug sales in Japan. So long as drug sales predictions are used in drug price calculations, a flexible repricing system is needed to buffer unexpected pharmaceutical expenditures.

There is substantial upward deviation between actual and predicted drug sales in Japan. So long as drug sales predictions are used in drug price calculations, a flexible repricing system is needed to buffer unexpected pharmaceutical expenditures.

Since 2008, the US Food and Drug Administration (FDA) has required that drug manufacturers conduct postmarket cardiovascular outcomes trials (CVOTs) for approved type 2 diabetes mellitus (T2DM) drugs. The utility and impact of these studies in determining atherosclerotic cardiovascular risk was reviewed during an FDA Advisory Committee Meeting held on October 24, 2018. Drug manufacturers and patient advocates at this meeting contended that the FDA-required CVOT studies discouraged private sector investment into developing novel T2DM drugs. Here, we explore these contentions by reviewing private sector investment in T2DM drug development from 2000 through 2008, followed by a deductive analysis of how associated events-including the implementation of the CVOT requirement-may have precipitated any observed changes.

We collected and analyzed industry-sponsored interventional trials for T2DM initiated between January 1, 2000, and December 31, 2017, and compared observed trends with those seen across all trialsidering more efficient postmarket study structures to assess cardiovascular safety beyond mandatory CVOTs.

Two issues on clinical trials with multiple endpoints were surveyed (1) the terminology of multiple endpoints, relationship between rare events and endpoints, and differences in multiplicity adjustment between regions; and (2) the current practice on multiplicity adjustment and sample size calculation. This article provides a summary of the results of a survey on the first issue.

The survey was conducted among 63 members of the Japan Pharmaceutical Manufacturers Association from October to November 2017.

Thirty-five companies based in Japan and 12 companies based in other countries, 47 companies in total, responded to the survey. The terms co-primary endpoints, secondary endpoint, and composite endpoint were used in a variety of ways. An endpoint for a clinically most important event that is expected to occur rarely differed between regions. Although the Pharmaceuticals and Medical Devices Agency did not demand multiplicity adjustment, it was considered in clinical trials with multiple endpoints for approval in Japan.

The use of terminology differed from the definition in the Food and Drug Administration guidance and the European Medicines Agency guideline. There remain challenges on a clinically most important event that is expected to occur rarely and multiplicity adjustment in clinical trials with multiple endpoints.

The use of terminology differed from the definition in the Food and Drug Administration guidance and the European Medicines Agency guideline. There remain challenges on a clinically most important event that is expected to occur rarely and multiplicity adjustment in clinical trials with multiple endpoints.

The analysis of subgroups in clinical trials is essential to assess differences in treatment effects for distinct patient clusters, that is, to detect patients with greater treatment benefit or patients where the treatment seems to be ineffective.

The software application subscreen (R package) has been developed to analyze the population of clinical trials in minute detail. The aim was to efficiently calculate point estimates (eg, hazard ratios) for multiple subgroups to identify groups that potentially differ from the overall trial result. The approach intentionally avoids inferential statistics such as P values or confidence intervals but intends to encourage discussions enriched with external evidence (eg, from other studies) about the exploratory results, which can be accompanied by further statistical methods in subsequent analyses. The subscreen application was applied to 2 clinical study data sets and used in a simulation study to demonstrate its usefulness.

The visualization of numerous combinedespecially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.

In the process of research and development of a new treatment, clinical trials are conducted to evaluate its safety and efficacy. Key to streamlining the process is to utilize appropriate historical information on an outcome of a control treatment when designing and analyzing a clinical trial.

For the use of such historical control information, there exist a meta-analytic approach and power prior approach. In this article, we evaluate their performance with regard to the type I error (TIE) rate and power through a simulation study where we analyze the data on a binary outcome of an experimental treatment and a control treatment from a new small-scale trial, along with the corresponding data of the control treatment from multiple historical trials. The reason is that the difference in the performance between the 2 approaches has not been clear.

When historical trials were homogeneous, the power was higher in the power prior approach and the meta-analytic approach using a beta-binomial model with a less noninformative prior than the other approaches. However, when heterogeneous historical trials were mixed, the power was lower, or the TIE rates got inflated.

To make use of historical control data, if importance is attached to control of the TIE rate, the meta-analytic approach using a normal-normal hierarchical model may be preferable to the power prior approach, whereas if attached to improvement of the power, this preference be reversed. selleckchem Anyway, the best approach should be chosen by comparing the operational characteristics of the approaches.

To make use of historical control data, if importance is attached to control of the TIE rate, the meta-analytic approach using a normal-normal hierarchical model may be preferable to the power prior approach, whereas if attached to improvement of the power, this preference be reversed. Anyway, the best approach should be chosen by comparing the operational characteristics of the approaches.