elaborator: A Novel App for Insights into Laboratory Data of Clinical Trials.

In clinical studies there are huge numbers of laboratory parameters available that are measured at several visits for several treatment groups. The status quo for presenting laboratory data in clinical trials consists in generating large numbers of tables and data listings. Such tables and listings are required for submissions to health authorities. However, reviewing laboratory data presented in the form of tables and listings is a lengthy and tedious process. Thus, to enable efficient exploration of laboratory data we developed elaborator, a comprehensive and easy-to-use interactive browser-based application. The elaborator app comprises three analyses types for addressing different questions, for example about changes in laboratory values that frequently occur, treatment-related changes and changes beyond the normal ranges. In this way, the app can be used by study teams for identifying safety signals in a clinical trial as well as for generating hypotheses that are further inspected with detailed analyses and possibly data from other sources. The elaborator app is implemented in the statistical software R. The R package elaborator can be obtained from https://cran.r-project.org/package=elaborator . Patients' laboratory data need to be extracted from the clinical database and pre-processed locally for feeding into the app. For exploring data by means of the elaborator, the user needs some familiarity with R but no programming knowledge is required.

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

BACKGROUND: 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. METHODS: 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. RESULTS: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials. CONCLUSION: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

BACKGROUND: 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. METHODS: 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. RESULTS: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials. CONCLUSION: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.