Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials

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Abstract

The goal of clinical trial research is to deliver safe and efficacious new treatments to patients in need in a timely and cost-effective manner. There is precedent in using historical control data to reduce the number of concurrent control subjects required in developing medicines for rare diseases and other areas of unmet need. The purpose of this paper is to provide a review for a regulatory and industry audience of the current state of relevant statistical methods, and of the uptake of these approaches and the opportunities for broader use of historical data in confirmatory clinical trials. General principles to consider when incorporating historical control data in a new trial are presented. Bayesian and frequentist approaches are outlined including how the operating characteristics for such a trial can be obtained. Finally, examples of approved new treatments that incorporated historical controls in their confirmatory trials are presented.

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The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.

Peter C Austin (2015)

The propensity score is a balancing score: conditional on the propensity score, treated and untreated subjects have the same distribution of observed baseline characteristics. Four methods of using the propensity score have been described in the literature: stratification on the propensity score, propensity score matching, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. However, the relative ability of these methods to reduce systematic differences between treated and untreated subjects has not been examined. The authors used an empirical case study and Monte Carlo simulations to examine the relative ability of the 4 methods to balance baseline covariates between treated and untreated subjects. They used standardized differences in the propensity score matched sample and in the weighted sample. For stratification on the propensity score, within-quintile standardized differences were computed comparing the distribution of baseline covariates between treated and untreated subjects within the same quintile of the propensity score. These quintile-specific standardized differences were then averaged across the quintiles. For covariate adjustment, the authors used the weighted conditional standardized absolute difference to compare balance between treated and untreated subjects. In both the empirical case study and in the Monte Carlo simulations, they found that matching on the propensity score and weighting using the inverse probability of treatment eliminated a greater degree of the systematic differences between treated and untreated subjects compared with the other 2 methods. In the Monte Carlo simulations, propensity score matching tended to have either comparable or marginally superior performance compared with propensity-score weighting.

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Power prior distributions for regression models

Ming-Hui Chen, Joseph Ibrahim (2000)

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Robust meta-analytic-predictive priors in clinical trials with historical control information.

Heinz Schmidli, Sandro Gsteiger, Satrajit Roychoudhury … (2014)

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

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Author and article information

Journal

Title: Therapeutic Innovation & Regulatory Science

Abbreviated Title: Drug Inf J

Publisher: SAGE Publications

ISSN (Print): 2168-4790

ISSN (Electronic): 2168-4804

Publication date Created: January 22 2018

Publication date Created: September 2018

Publication date (Electronic): June 18 2018

Publication date (Print): September 2018

Volume: 52

Issue: 5

Pages: 546-559

Affiliations

[1 ]Clinical Statistics, GlaxoSmithKline, Collegeville, PA, USA

[2 ]Centre for Excellence in Statistical Innovation, UCB, UK

[3 ]Statistical Science and Programming, Allergan, Irvine, CA, USA

[4 ]Advanced Biostatistics and Data Analytics Centre of Excellence, GlaxoSmithKline, Uxbridge, Middlesex, UK

[5 ]Statistical Methodology & Consulting, Novartis Pharma AG, Basel

[6 ]Data Science Biostatistics, Astellas, Leiden, the Netherlands

[7 ]Independent Consultant, Newmarket, UK

[8 ]Biostatistics, Worldwide Research & Development, Pfizer, Cambridge, MA, USA

[9 ]Global Statistical Science, Eli Lilly and Company, Indianapolis, IN, USA

[10 ]R&D Data Centre of Excellence, GlaxoSmithKline, Collegeville, PA, USA

Article

DOI: 10.1177/2168479018778282

PubMed ID: 29909645

SO-VID: 8f521728-b35b-4477-af2c-ca1d2c0e9e0e

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http://journals.sagepub.com/page/policies/text-and-data-mining-license

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Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials : Review of Methods and Opportunities

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Most cited references 40

The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies.

Power prior distributions for regression models

Robust meta-analytic-predictive priors in clinical trials with historical control information.

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