ICH E9 Statistical Principles for Clinical Trials was issued in 1998. In October 2014, an addendum to ICH E9 was proposed relating to estimands and sensitivity analyses. In preparation for the release of the addendum, Statisticians in the Pharmaceutical Industry held a 1-day expert group meeting in February 2015. Topics debated included definition, development, implementation, education and communication challenges associated with estimands and sensitivity analyses. The topic of estimands is an important and relatively new one in clinical development. A clear message from the meeting was that estimands bridge the gap between study objectives and statistical methods. When defining estimands, an iterative process linking trial objectives, estimands, trial design, statistical and sensitivity analysis needs to be established. Each objective should have at least one distinct estimand, supported by sensitivity analyses. Because clinical trials are multi-faceted and expensive, it is unrealistic to restrict a study to a single objective and associated estimand. The actual set of estimands and sensitivity analyses for a study will depend on the study objectives, the disease setting and the needs of the various stakeholders. Copyright © 2016 John Wiley & Sons, Ltd.
A key paper in modelling patient recruitment in multi-centre clinical trials is that of Anisimov and Fedorov. They assume that the distribution of the number of patients in a given centre in a completed trial follows a Poisson distribution. In a second stage, the unknown parameter is assumed to come from a Gamma distribution. As is well known, the overall Gamma-Poisson mixture is a negative binomial. For forecasting time to completion, however, it is not the frequency domain that is important, but the time domain and that of Anisimov and Fedorov have also illustrated clearly the links between the two and the way in which a negative binomial in one corresponds to a type VI Pearson distribution in the other. They have also shown how one may use this to forecast time to completion in a trial in progress. However, it is not just necessary to forecast time to completion for trials in progress but also for trials that have yet to start. This suggests that what would be useful would be to add a higher level of the hierarchy: over all trials. We present one possible approach to doing this using an orthogonal parameterization of the Gamma distribution with parameters on the real line. The two parameters are modelled separately. This is illustrated using data from 18 trials. We make suggestions as to how this method could be applied in practice.
The International Conference on Harmonization (ICH) E9 guideline 'Statistical principles for clinical trials' was adopted by the Committee for Proprietary Medicinal Products in March 1998, and consequently is operational in Europe. It has also been adopted in the U.S.A. and Japan. The aim of this paper is to relate the problems encountered during a recent regulatory submission to those discussed in the ICH E9 guideline. Statistical principles discussed in the guideline, but not comprehensively addressed when the clinical development programme was initiated in the mid-1990s, will be reviewed. The impact of each issue on the approvability of the dossier will be discussed, together with recommendations on how to avoid such problems with other ongoing clinical development programmes.
SummaryThe International Council for Harmonisation (ICH) guideline E9 Statistical Principles for Clinical Trials (1) was issued in 1998. In October 2014, an addendum to ICH E9 was proposed on statistical principles relating to estimands and sensitivity analyses. The final version of the addendum to ICH E9 (R1) (2) was issued in November 2019. This virtual edition of Pharmaceutical Statistics takes a closer look at some of the progress that has been made since 2018 when implementing the estimand framework within clinical research. The articles discussed in this virtual issue are not new, but a compilation from previous issues. This specific article will act as a refresher for those not familiar with the topic and discuss the ABCs of estimands and their proposed deployment for improving the quality of clinical research. An overview of the more recent Pharmaceutical Statistics articles on estimands will be provided, signifying areas where progress have been made. The articles should be considered as contributions to the ongoing discussions rather than the final word. Finally, a personal perspective on the estimand success story and remaining challenges with proposed solutions will be discussed.
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