Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation

Shu Yang; Chenyin Gao; Donglin Zeng; Xiaofei Wang

doi:10.1093/jrsssb/qkad017

Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation

J R Stat Soc Series B Stat Methodol. 2023 Apr 6;85(3):575-596. doi: 10.1093/jrsssb/qkad017. eCollection 2023 Jul.

Authors

Shu Yang¹, Chenyin Gao¹, Donglin Zeng², Xiaofei Wang³

Affiliations

¹ Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
² Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
³ Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

Abstract

We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternatives. We provide a data-adaptive procedure to select the test threshold that promises the smallest mean square error and an elastic confidence interval with a good finite-sample coverage property.

Keywords: counterfactual outcome; least favourable confidence interval; non-regularity; pre-test estimator; precision medicine; semiparametric efficiency.

Abstract

Grants and funding