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Volume 23
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Population-Adjusted Survival: A Real-World, Epidemiologic Approach for Analyzing Time-to-Event Clinical Trial Data

by
Jimmy T. Efird
1,2,*,
Genevieve N. Dupuis
3,
Yuk Ming Choi
4,
Cynthia Hau
1,5,
Sonia T. Anand
1,
Michael J. Davenport
1,
Maria Androsenko
1,
Kaitlin J. Cassidy
1,
Robert A. Lew
1,6 and
Hongsheng Wu
1,7
1
Cooperative Studies Program Coordinating Center, VA Boston, Lafayette City Center, 2 Avenue de Lafayette, Boston, MA 02111, USA
2
School of Medicine, Case Western Reserve University, Cleveland, OH 44206, USA
3
Center for Biostatistics in AIDS Research, Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA 02118, USA
4
Provider Services, Signify Health, 4055 (S-700) Valley View Ln, Dallas, TX 75244, USA
5
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
6
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
7
Division of Mathematics, Analytics, Science, and Technology, Babson College, Wellesley, MA 02457, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2026, 23(9), 1162; https://doi.org/10.3390/ijerph23091162
Submission received: 1 July 2026 / Revised: 17 August 2026 / Accepted: 26 August 2026 / Published: 6 September 2026
(This article belongs to the Special Issue Advances in Biostatistics for Cardiovascular and Cancer Research)
Versions Notes

Abstract

Results of a clinical trial may misrepresent the target population. Inappropriate inclusion/exclusion criteria, selective recruitment, and differential participation may restrict the number of patients who consent to join the study. Frequently, they differ from the real-world population in terms of key outcome-related characteristics. Such biased selection may make the therapeutic agent seem more or less effective within a certain stratum. While block randomization and an intent-to-treat design can help to minimize confounding within the trial sample, differences may still exist with respect to the distribution of these variables in the population. To mitigate misleading conclusions, the current manuscript illustrates how to ‘population-adjust’ survival estimates in a clinical trial using the Beckett–Bayes expansion method. In contrast to other population adjustment methods, this novel approach involves substituting the probability of belonging to a specific sample stratum with the corresponding population data (i.e., population borrowing). As a regression-free technique, the method is not susceptible to model misspecification. A large clinical trial focusing on cardiovascular-related deaths in patients with hypertension is used as an example to illustrate the method. The proposed framework is promising and easy to implement when reliable population-level information is available.
Keywords: Beckett–Bayes expansion; cardiovascular events; external validity; hypertension; population-adjusted survival; randomized clinical trials Beckett–Bayes expansion; cardiovascular events; external validity; hypertension; population-adjusted survival; randomized clinical trials

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MDPI and ACS Style

Efird, J.T.; Dupuis, G.N.; Choi, Y.M.; Hau, C.; Anand, S.T.; Davenport, M.J.; Androsenko, M.; Cassidy, K.J.; Lew, R.A.; Wu, H. Population-Adjusted Survival: A Real-World, Epidemiologic Approach for Analyzing Time-to-Event Clinical Trial Data. Int. J. Environ. Res. Public Health 2026, 23, 1162. https://doi.org/10.3390/ijerph23091162

AMA Style

Efird JT, Dupuis GN, Choi YM, Hau C, Anand ST, Davenport MJ, Androsenko M, Cassidy KJ, Lew RA, Wu H. Population-Adjusted Survival: A Real-World, Epidemiologic Approach for Analyzing Time-to-Event Clinical Trial Data. International Journal of Environmental Research and Public Health. 2026; 23(9):1162. https://doi.org/10.3390/ijerph23091162

Chicago/Turabian Style

Efird, Jimmy T., Genevieve N. Dupuis, Yuk Ming Choi, Cynthia Hau, Sonia T. Anand, Michael J. Davenport, Maria Androsenko, Kaitlin J. Cassidy, Robert A. Lew, and Hongsheng Wu. 2026. "Population-Adjusted Survival: A Real-World, Epidemiologic Approach for Analyzing Time-to-Event Clinical Trial Data" International Journal of Environmental Research and Public Health 23, no. 9: 1162. https://doi.org/10.3390/ijerph23091162

APA Style

Efird, J. T., Dupuis, G. N., Choi, Y. M., Hau, C., Anand, S. T., Davenport, M. J., Androsenko, M., Cassidy, K. J., Lew, R. A., & Wu, H. (2026). Population-Adjusted Survival: A Real-World, Epidemiologic Approach for Analyzing Time-to-Event Clinical Trial Data. International Journal of Environmental Research and Public Health, 23(9), 1162. https://doi.org/10.3390/ijerph23091162

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