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Ray Lin

Publications and source records attributed to Ray Lin.

4 recordsLinked to original sources

Leveraging External Controls for Treatment Switching in Randomized Controlled Trials: A Weighted Causal Inference Framework for Overall Survival

In many oncology clinical trials where overall survival is a key endpoint, patients are permitted to switch from the control arm to the experimental treatment arm or other suitable therapies. Switching can occur for various reasons, including disease progression. This violates the causal guarantees of randomized treatment assignment, resulting in biased treatment effect estimates. Existing methods often require strong assumptions, complicated model specifications, or both. In this paper, we propose a general framework that incorporates external controls to account for treatment switching in randomized controlled trials. Leveraging the synthetic control method and balancing weights from observational causal inference, we propose several estimators that use multiple imputation and time-varying weights to adjust for treatment switching. We also discuss approaches to selecting the risk set of external controls to impute from. Through extensive simulation studies, we show that our proposed methods lead to meaningful statistical improvements relative to standard adjustment methods that utilize external controls in naive ways or those that do not utilize external controls at all. We then demonstrate the utility of our external control-based approaches with two phase III oncology trials.

stat.ME

Comprehensive Formal Verification of Observational Correctness for the CHERIoT-Ibex Processor

The CHERI architecture equips conventional RISC ISAs with significant architectural extensions that provide a hardware-enforced mechanism for memory protection and software compartmentalisation. Architectural capabilities replace conventional integer pointers with memory addresses bound to permissions constraining their use. We present the first comprehensive formal verification of a capability extended RISC-V processor with internally 'compressed' capabilities - a concise encoding of capabilities with some resemblance to floating point number representations. The reference model for RTL correctness is a minor variant of the full and definitive ISA description written in the Sail ISA specification language. This is made accessible to formal verification tools by a prototype flow for translation of Sail into SystemVerilog. Our verification demonstrates a methodology for establishing that the processor always produces a stream of interactions with memory that is identical to that specified in Sail, when started in the same initial state. We additionally establish liveness. This abstract, microarchitecture-independent observational correctness property provides a comprehensive and clear assurance of functional correctness for the CHERIoT-Ibex processor's observable interactions with memory.

cs.AR

Balancing events, not patients, maximizes power of the logrank test: and other insights on unequal randomization in survival trials

We revisit the question of what randomization ratio (RR) maximizes power of the logrank test in event-driven survival trials under proportional hazards (PH). By comparing three approximations of the logrank test (Schoenfeld, Freedman, Rubinstein) to empirical simulations, we find that the RR that maximizes power is the RR that balances number of events across treatment arms at the end of the trial. This contradicts the common misconception implied by Schoenfeld's approximation that 1:1 randomization maximizes power. Besides power, we consider other factors that might influence the choice of RR (accrual, trial duration, sample size, etc.). We perform simulations to better understand how unequal randomization might impact these factors in practice. Altogether, we derive 6 insights to guide statisticians in the design of survival trials considering unequal randomization.

stat.ME

A meta-analytic framework to adjust for bias in external control studies

While randomized controlled trials (RCTs) are the gold standard for estimating treatment effects in medical research, there is increasing use of and interest in using real-world data for drug development. One such use case is the construction of external control arms for evaluation of efficacy in single-arm trials, particularly in cases where randomization is either infeasible or unethical. However, it is well known that treated patients in non-randomized studies may not be comparable to control patients -- on either measured or unmeasured variables -- and that the underlying population differences between the two groups may result in biased treatment effect estimates as well as increased variability in estimation. To address these challenges for analyses of time-to-event outcomes, we developed a meta-analytic framework that uses historical reference studies to adjust a log hazard ratio estimate in a new external control study for its additional bias and variability. The set of historical studies is formed by constructing external control arms for historical RCTs, and a meta-analysis compares the trial controls to the external control arms. Importantly, a prospective external control study can be performed independently of the meta-analysis using standard causal inference techniques for observational data. We illustrate our approach with a simulation study and an empirical example based on reference studies for advanced non-small cell lung cancer. In our empirical analysis, external control patients had lower survival than trial controls (hazard ratio: 0.907), but our methodology is able to correct for this bias. An implementation of our approach is available in the R package ecmeta.

stat.ME