A hybrid prior Bayesian method for combining domestic real-world data and overseas data in global drug development

Abstract

Background

Hybrid clinical trial design integrates traditional randomized controlled trials (RCTs) with real-world data (RWD), aiming to enhance trial efficiency through dynamic incorporation of external data (External trial data and RWD). However, existing methods, such as the Meta-Analytic Predictive (MAP) Prior, exhibit serious limitations in controlling data heterogeneity, adjusting baseline discrepancies, and optimizing dynamic borrowing proportions. These limitations often introduce external bias or compromise evidence reliability, hindering their application in complex analyses like bridging trials and multi-regional clinical trials (MRCTs).

Objective

This study proposes a novel hybrid Bayesian framework, EQPS Robust MAP (rMAP), to address heterogeneity and bias in multi-source data integration. Its feasibility and robustness are validated through systematic simulations and retrospective case analyses, using two independent datasets to evaluate the effect of Risankizumab in patients with moderate-to-severe plaque psoriasis.

Design and Methods

The EQPS-rMAP method operates in three stages: (1) Eliminating baseline covariate discrepancies through propensity score stratification; (2) constructing stratum-specific MAP priors to dynamically adjust weights for external data; and (3) introducing equivalence probability weights to quantify data conflict risks. The study evaluates the method's performance across six simulated analyses (heterogeneity differences, baseline shifts, etc.), comparing it with traditional methods (MAP, PSMAP, Empirical Bayes MAP) in terms of estimation bias, type I error control, and sample size requirements. Real-world case analyses further validate its applicability.

Results

Simulations demonstrate that EQPS-rMAP maintains estimation robustness under considerable heterogeneity while reducing sample size demands and enhancing trial efficiency. Case analyses confirm its ability to control external bias while preserving high estimation accuracy compared to conventional approaches.

Conclusion

The EQPS-rMAP method provides empirical evidence for the feasibility of hybrid clinical designs. Its methodological advancements—resolving baseline and heterogeneity conflicts through adaptive mechanisms—offer broader applicability for integrating external and RWD across diverse analyses, including bridging trials, MRCTs, and post-marketing studies.

Keywords

Hybrid clinical trial design Bayesian real world data external trial data propensity score meta-analytic approaches

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References

U.S. Food and Drug Administration. Complex innovative trial design meeting program. 2022, https://www.fda.gov/drugs/development-approval-process-drugs/complex-innovative-trial-design-meeting-program .

U.S. Food and Drug Administration. ACT EU multi-annual workplan 2022–2026. 2022, https://www.ema.europa.eu/en/documents/other/act-eu-multi-annual-workplan-2022-2026_en.pdf .

International Conference on Harmonisation. Guidance on ethnic factors in the acceptability of foreign clinical data; availability—FDA. Fed Regist 1998; 63: 31790–31796.

Liu

Bai

, et al. Statistical methods for bridging studies in new drug clinical trials. Chin J Health Stat 2015; 32: 345–349.

Shao

Chow

. Reproducibility probability in clinical trials. Stat Med 2002; 21: 1727–1742.

Chow

Shao

. Assessing sensitivity and similarity in bridging studies. J Biopharm Stat 2002; 12: 385–400.

Liu

Chow

. Bridging studies in clinical development. J Biopharm Stat 2002; 12: 359–367.

Lan

Soo

Siu

, et al. The use of weighted Z-tests in medical research. J Biopharm Stat 2005; 15: 625–639.

Tsou

Tsong

Liu

, et al. Weighted evidence approach of bridging study. J Biopharm Stat 2012; 22: 952–965.

10.

Dong

Guo

Tsong

. A note on two approaches of testing bridging evidence to a new region. J Biopharm Stat 2012; 22: 966–976.

11.

Hsiao

Liu

. A two-stage design for bridging studies. J Biopharm Stat 2007; 15: 75–83.

12.

Hsiao

Liu

. A group sequential approach to evaluation of bridging studies. J Biopharm Stat 2003; 13: 793–801.

13.

Hsiao

Hsu

Tsou

, et al. Use of prior information for Bayesian evaluation of bridging studies. J Biopharm Stat 2007; 17: 109–121.

14.

Ibrahim

Chen

. Power prior distributions for regression models. Stat Sci 2000; 15: 46–60.

15.

Gandhi

Mukherjee

Biswas

. A Bayesian approach for inference from a bridging study with binary outcomes. J Biopharm Stat 2012; 22: 935–951.

16.

Duan

Smith

. Evaluating water quality using power priors to incorporate historical information. Environmetrics 2006; 17: 95–106.

17.

Hobbs

Sargent

Carlin

. Commensurate priors for incorporating historical information in clinical trials using general and generalized linear models. Bayesian Anal 2012; 7: 639–674.

18.

Schmidli

Gsteiger

Roychoudhury

, et al. Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics 2014; 70: 1023–1032.

19.

Hupf

Bunn

Lin

, et al. Bayesian Semiparametric meta-analytic-predictive prior for historical control borrowing in clinical trials. Stat Med 2021; 40: 3385–3399.

20.

Chen

Scott

. Addressing prior-data conflict with empirical meta-analytic-predictive priors in clinical studies with historical information. J Biopharm Stat 2016; 26: 1056–1066.

21.

Zhang

Shen

, et al. Adaptively leveraging external data with robust meta-analytical-predictive prior using empirical Bayes. Pharm Stat 2023; 22: 846–860.

22.

Neuenschwander

Capkun-Niggli

Branson

, et al. Summarizing historical information on controls in clinical trials. Clin Trials 2010; 7: 5–18.

23.

Rosenbaum

Rubin

. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41–55.

24.

Bennett

White

Best

, et al. A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods. Pharm Stat 2021; 20: 462–484.

25.

Lee

Lessler

Stuart

. Improving propensity score weighting using machine learning. Stat Med 2010; 29: 337–346.

26.

Setoguchi

Schneeweiss

Brookhart

, et al. Evaluating uses of data mining techniques in propensity score estimation: A simulation study. Pharmacoepidemiol Drug Saf 2008; 17: 546–555.

27.

McCaffrey

Ridgeway

Morral

. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods 2004; 9: 403–425.

28.

Rosenbaum

Rubin

. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984; 79: 516–524.

29.

Cochran

. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 1968; 24: 295–313.

30.

Gustafson

Hossain

Macnab

. Conservative prior distributions for variance parameters in hierarchical models. Can J Stat 2006; 34: 377–390.

31.

Dalal

Hall

. Approximating priors by mixtures of natural conjugate priors. J R Stat Soc B: Stat Methodol 1983; 45: 278–286.

32.

Wang

Chen

, et al. Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies. J Biopharm Stat 2019; 29: 731–748.

33.

Liu

Bunn

Hupf

, et al. Propensity-score-based meta-analytic predictive prior for incorporating real-world and historical data. Stat Med 2021; 40: 4794–4808.

34.

Inman

Bradley

. The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities. Commun Stat – Theory Methods 1989; 18: 3851–3874.

35.

Thompson

. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 1933; 25: 285–294.

36.

Psioda

Ibrahim

. Bayesian Clinical trial design using historical data that inform the treatment effect. Biostatistics 2019; 20: 400–415.

37.

Gooderham

Pinter

Ferris

, et al. Long-term, durable, absolute Psoriasis Area and Severity Index and health-related quality of life improvements with risankizumab treatment: a post hoc integrated analysis. J Eur Acad Dermatol Venereol 2022; 36: 855–865.

38.

Borroni

Malagoli

Gargiulo

, et al. Real-life effectiveness and safety of Risankizumab in moderate-to-severe plaque psoriasis: a 40-week multicentric retrospective study. Acta Derm-Venereol 2021; 101: adv00605.

39.

U.S. Food and Drug Administration. Using Bayesian statistical approaches to evaluate drug products. 2023, https://www.fda.gov/drugs/cder-small-business-industry-assistance-sbia/using-bayesian-statistical-approaches-advance-our-ability-evaluate-drug-products .

40.

U.S. Food and Drug Administration. Advancing the use of complex innovative designs in clinical trials: From pilot to practice. 2024, https://www.fda.gov/news-events/advancing-use-complex-innovative-designs-clinical-trials-pilot-practice-03052024 .

41.

Shenoy

. Multi-regional clinical trials and global drug development. Perspect Clin Res 2016; 7: 62–67.

42.

Liu

Hsueh

Hsiao

. A Bayesian noninferiority approach to evaluation of bridging studies. J Biopharm Stat 2004; 14: 291–300.

43.

U.S. Food and Drug Administration. Rare diseases: Common issues in drug development – Draft guidance for industry. 2019, https://www.fda.gov/media/119757/download .

44.

Drazen

Harrington

McMurray

JJV

, et al. Adaptive designs for clinical trials. N Engl J Med 2016; 375: 65–74.