Aggregate Safety Assessment Planning for the Drug Development Life-Cycle.

The Program Safety Analysis Plan (PSAP) was proposed previously as a tool to proactively plan for integrated analyses of product safety data. Building on the PSAP and taking into consideration the evolving regulatory landscape, the Drug Information Association-American Statistical Association (DIA-ASA) Interdisciplinary Safety Evaluation scientific working group herein proposes the Aggregate Safety Assessment Plan (ASAP) process. The ASAP evolves over a product's life-cycle and promotes interdisciplinary, systematic safety planning as well as ongoing data review and characterization of the emerging product safety profile. Objectives include alignment on the safety topics of interest, identification of safety knowledge gaps, planning for aggregate safety evaluation of the clinical trial data and preparing for safety communications. The ASAP seeks to tailor the analyses for a drug development program while standardizing the analyses across studies within the program. The document is intended to be modular and flexible in nature, depending on the program complexity, phase of development and existing sponsor processes. Implementation of the ASAP process will facilitate early safety signal detection, improve characterization of product risks, harmonize safety messaging, and inform program decision-making.

Meta-analysis of clinical trial safety data in a drug development program: answers to frequently asked questions.

BACKGROUND: Meta-analyses of clinical trial safety data have risen in importance beyond regulatory submissions. During drug development, sponsors need to recognize safety signals early and adjust the development program accordingly, so as to facilitate the assessment of causality. Once a product is marketed, sponsors add postapproval clinical trial data to the body of information to help understand existing safety concerns or those that arise from other postapproval data sources, such as spontaneous reports. PURPOSE: This article focuses on common questions encountered when designing and performing a meta-analysis of clinical trial safety data. Although far from an exhaustive set of questions, they touch on some basic and often misunderstood features of conducting such meta-analyses. METHODS: The authors reviewed the current literature and used their combined experience with regulatory and other uses of meta-analysis to answer common questions that arise when performing meta-analyses of safety data. RESULTS: We addressed the following topics: choice of studies to pool, effects of the method of ascertainment, use of patient-level data compared to trial-level data, the need (or not) for multiplicity adjustments, heterogeneity of effects and sources of it, and choice of fixed effects versus random effects. LIMITATIONS: The list of topics is not exhaustive and the opinions offered represent only our perspective; we recognize that there may be other valid perspectives. CONCLUSIONS: Meta-analysis can be a valuable tool for evaluating safety questions, but a number of methodological choices need to be made in designing and conducting any meta-analysis. This article provides advice on some of the more commonly encountered choices.

Recommendations for safety planning, data collection, evaluation and reporting during drug, biologic and vaccine development: a report of the safety planning, evaluation, and reporting team.

BACKGROUND: The Safety Planning, Evaluation and Reporting Team (SPERT) was formed in 2006 by the Pharmaceutical Research and Manufacturers of America. PURPOSE: SPERT's goal was to propose a pharmaceutical industry standard for safety planning, data collection, evaluation, and reporting, beginning with planning first-in-human studies and continuing through the planning of the post-product-approval period. METHODS: SPERT's recommendations are based on our review of relevant literature and on consensus reached in our discussions. RESULTS: An important recommendation is that sponsors create a Program Safety Analysis Plan early in development. We also give recommendations for the planning of repeated, cumulative meta-analyses of the safety data obtained from the studies conducted within the development program. These include clear definitions of adverse events of special interest and standardization of many aspects of data collection and study design. We describe a 3-tier system for signal detection and analysis of adverse events and highlight proposals for reducing "false positive" safety findings. We recommend that sponsors review the aggregated safety data on a regular and ongoing basis throughout the development program, rather than waiting until the time of submission. LIMITATIONS: We recognize that there may be other valid approaches. CONCLUSIONS: The proactive approach we advocate has the potential to benefit patients and health care providers by providing more comprehensive safety information at the time of new product marketing and beyond.

Missing item imputation for quality-of-life instruments with application to asthma quality-of-life questionnaires.

There has been increasing use of quality-of-life (QoL) instruments in drug development. Missing item values often occur in QoL data. A common approach to solve this problem is to impute the missing values before scoring. Several imputation procedures, such as imputing with the most correlated item and imputing with a row/column model or an item response model, have been proposed. We examine these procedures using data from two clinical trials, in which the original asthma quality-of-life questionnaire (AQLQ) and the miniAQLQ were used. We propose two modifications to existing procedures: truncating the imputed values to eliminate outliers and using the proportional odds model as the item response model for imputation. We also propose a novel imputation method based on a semi-parametric beta regression so that the imputed value is always in the correct range and illustrate how this approach can easily be implemented in commonly used statistical software. To compare these approaches, we deleted 5% of item values in the data according to three different missingness mechanisms, imputed them using these approaches and compared the imputed values with the true values. Our comparison showed that the row/column-model-based imputation with truncation generally performed better, whereas our new approach had better performance under a number scenarios.