Details

Autor(en) / Beteiligte

• Soikkeli, Fanni, MSc
• Hashim, Mahmoud, MPH
• Ouwens, Mario, PhD
• Postma, Maarten, PhD
• Heeg, Bart, PhD

Titel

Extrapolating Survival Data Using Historical Trial–Based a Priori Distributions

Ist Teil von

• Value in health, 2019-09, Vol.22 (9), p.1012-1017

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Applied Social Sciences Index & Abstracts (ASSIA)

Beschreibungen/Notizen

• AbstractObjectivesTo show how clinical trial data can be extrapolated using historical trial data–based a priori distributions. MethodsExtrapolations based on 30-month pivotal multiple myeloma trial data were compared with 75-month data from the same trial. The 30-month data represent a typical decision-making scenario where early results from a clinical trial are extrapolated. Mature historical trial data with the same comparator as in the pivotal trial were incorporated in 2 stages. First, the parametric distribution selection was based on the historical trial data. Second, the shape parameter estimate of the historical trial was used to define an informative a priori distribution for the shape of the 30-month pivotal trial data. The method was compared with standard approaches, fitting parametric distributions to the 30-month data with noninformative prior. The predicted survival of each method was compared with the observed survival (ΔAUC) in the 75-month trial data. ResultsThe Weibull had the best fit to the historical trial and the log-normal to the 30-month pivotal trial data. The ΔAUC of the Weibull with informative priors was considerably smaller compared with the standard Weibull. Also, the predicted median survival based on the Weibull with informative priors was more accurate (melphalan and prednisone [MP] 40 months, and bortezomib [V] combined with MP [VMP] 62 months) than based on the standard Weibull (MP 45 months and VMP 72 months) when compared with the observed median (MP 41.3 months and VMP 56.4 months). ConclusionsExtrapolation of clinical trial data is improved by using historical trial data–based informative a priori distributions.