arXiv subjects

Riddhiman Saha

Publications and source records attributed to Riddhiman Saha.

3 recordsLinked to original sources

arXiv2026

Estimating Treatment Effects with a Single Arm Clinical Trial and Data Summaries from an External Control Group: is it Possible?

Randomized controlled trials are crucial for assessing new treatments, but they are often infeasible due to ethical or practical constraints. In these cases, single-arm trials combined with external control data constitute an alternative approach for estimating treatment effects. However, individual patient-level data on external controls are frequently inaccessible due to privacy and economic concerns. Therefore, investigators rely on data summaries published by previous studies of the control therapy. We propose a method for inference on the treatment effect of an experimental therapy using patient-level data from a single-arm trial and a few published data summaries from an external control group. We introduce and study the Interval of Potential Effects (IPE), described as the range of plausible levels of the treatment effect that are compatible with the patient-level data from the experimental treatment and the available summaries on the control group. The width of the IPE quantifies the uncertainty on the treatment effect attributable to the lack of individual patient data from the control group. We demonstrate, through simulations and a case study, how the IPE indicates whether the available summaries are sufficient for accurate estimation of the treatment effect. We also illustrate that IPEs can support decision-making in the early phases of development of new treatments.

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arXiv2026

Q-approximation of operating characteristics of clinical trial designs

Designing clinical trials requires evaluating multiple operating characteristics (OCs), such as the likelihood of an early stopping decision, the probability of detecting a treatment effect, and the Type I error rate. In most cases, these evaluations are based on computationally intensive Monte Carlo simulations. As the complexity of clinical trials and the use of adaptive designs increase, the computational burden can quickly become prohibitive. We introduce a strategy for rapidly approximating OCs, called the Q-approximation. Our approach is based on quadratic approximations of the log-likelihood and asymptotic arguments. The main idea is to replace simulation of full trial datasets with simulation of the approximate likelihood functions that determine the trial's interim and final decisions. The Q-approximation approach can be applied to any trial design that uses data analysis methods coherent with the likelihood principle, including multistage designs with early stopping, adaptively randomized designs, and designs that leverage external data. We illustrate the approach with several examples and show that it provides an accurate approximation of important OCs while reducing the computation time compared to Monte Carlo simulations. In particular, in our experiments, the standard Monte Carlo approximation of OCs requires 150 to 1,900 times greater computing budget than Q-approximations to achieve comparable levels of accuracy. By enabling fast OC evaluations, Q-approximations can support the broader use of innovative trial designs in both applied trial planning and methodological development.

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arXiv2025

Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data

Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. In particular, subgroup analyses of early-stage trials often influence the design and eligibility criteria of subsequent confirmatory trials and ultimately influence which subpopulations will receive the treatment after regulatory approval. However, subgroup analyses are often complicated by small sample sizes, which leads to substantial uncertainty about subgroup-specific treatment effects. We explore the use of external control (EC) data to augment RCT subgroup analyses. We define and discuss harmonized estimators of subpopulation-specific treatment effects that leverage EC data. Our approach can be used to modify any subgroup-specific treatment effect estimates that are obtained by combining RCT and EC data, such as linear regression. We alter these subgroup-specific estimates to make them coherent with a robust estimate of the average effect in the randomized population based only on RCT data. The weighted average of the resulting subgroup-specific harmonized estimates matches the RCT-only estimate of the overall effect in the randomized population. We discuss the proposed harmonized estimators through analytic results and simulations, and investigate standard performance metrics. The method is illustrated with a case study in oncology.

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