Combining Real-World and Randomized Control Trial Data Using Data-Adaptive Weighting via the On-Trial Score

Harton, Joanna; Segal, Brian D.; Mamtani, Ronac; Mitra, Nandita; Hubbard, Rebecca A.

doi:10.1080/19466315.2022.2071982

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…22 If there is no unmeasured confounding factors, the PS analysis has the theoretical guarantee to remove bias from SC, which leads to the ideal situation of bias elimination, power increase, and Type I error control. 23 However, the assumption of unmeasured confounding is often unlikely to hold or difficult to verify in practical situations. Additionally, if this assumption holds, then there is little need to use the augmented RCT with hybrid control: a single arm trial with SC as the sole control group would be adequate.…”

Section: Statistical Methods To Combine Data From Synthetic Control A...mentioning

confidence: 99%

“…More recently, propensity score (PS) analysis has been used to balance covariates between SC and RCT data 22 . If there is no unmeasured confounding factors, the PS analysis has the theoretical guarantee to remove bias from SC, which leads to the ideal situation of bias elimination, power increase, and Type I error control 23 . However, the assumption of unmeasured confounding is often unlikely to hold or difficult to verify in practical situations.…”

Section: Introductionmentioning

confidence: 99%

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Confounding adjustment in the analysis of augmented randomized controlled trial with hybrid control arm

Jemielita

2023

Statistics in Medicine

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The augmented randomized controlled trial (RCT) with hybrid control arm includes a randomized treatment group (RT), a smaller randomized control group (RC), and a large synthetic control (SC) group from real-world data. This kind of trial is useful when there is logistics and ethics hurdle to conduct a fully powered RCT with equal allocation, or when it is necessary to increase the power of the RCT by incorporating real-world data. A difficulty in the analysis of augmented RCT is that the SC and RC may be systematically different in the distribution of observed and unmeasured confounding factors, causing bias when the two control groups are analyzed together as hybrid controls. We propose to use propensity score (PS) analysis to balance the observed confounders between SC and RC. The possible bias caused by unmeasured confounders can be estimated and tested by analyzing propensity score adjusted outcomes from SC and RC. We also propose a partial bias correction (PBC) procedure to reduce bias from unmeasured confounding. Extensive simulation studies show that the proposed PS + PBC procedures can improve the efficiency and statistical power by effectively incorporating the SC into the RCT data analysis, while still control the estimation bias and Type I error inflation that might arise from unmeasured confounding. We illustrate the proposed statistical procedures with data from an

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Section: Statistical Methods To Combine Data From Synthetic Control A...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Confounding adjustment in the analysis of augmented randomized controlled trial with hybrid control arm

Jemielita

2023

Statistics in Medicine

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Testing for treatment effect twice using internal and external controls in clinical trials

Yi,

Zhang,

et al. 2023

Journal of Causal Inference

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Leveraging external controls – relevant individual patient data under control from external trials or real-world data – has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given access to novel treatments. However, due to lack of randomization, RCT patients and external controls may differ with respect to covariates that may or may not have been measured. Hence, after controlling for measured covariates, for instance by matching, testing for treatment effect using external controls may still be subject to unmeasured biases. In this article, we propose a sensitivity analysis approach to quantify the magnitude of unmeasured bias that would be needed to alter the study conclusion that presumed no unmeasured biases are introduced by employing external controls. Whether leveraging external controls increases power or not depends on the interplay between sample sizes and the magnitude of treatment effect and unmeasured biases, which may be difficult to anticipate. This motivates a combined testing procedure that performs two highly correlated analyses, one with and one without external controls, with a small correction for multiple testing using the joint distribution of the two test statistics. The combined test provides a new method of sensitivity analysis designed for data fusion problems, which anchors at the unbiased analysis based on RCT only and spends a small proportion of the type I error to also test using the external controls. In this way, if leveraging external controls increases power, the power gain compared to the analysis based on RCT only can be substantial; if not, the power loss is small. The proposed method is evaluated in theory and power calculations, and applied to a real trial.

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Combining Real-World and Randomized Control Trial Data Using Data-Adaptive Weighting via the On-Trial Score

Cited by 2 publications

References 27 publications

Confounding adjustment in the analysis of augmented randomized controlled trial with hybrid control arm

Confounding adjustment in the analysis of augmented randomized controlled trial with hybrid control arm

Testing for treatment effect twice using internal and external controls in clinical trials

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