arXiv subjects

Jonathan J. Chipman

Publications and source records attributed to Jonathan J. Chipman.

3 recordsLinked to original sources

arXiv2025

Randomization Restrictions: Their Impact on Type I Error When Experimenting with Finite Populations

Participants in clinical trials are often viewed as a unique, finite population. Yet, statistical analyses often assume that participants were randomly sampled from a larger population. Under Complete Randomization, Randomization-Based Inference (RBI; a finite population inference) and Analysis of Variance (ANOVA; a random sampling inference) provide asymptotically equivalent difference-in-means tests. However, sequentially-enrolling trials typically employ restricted randomization schemes, such as block or Maximum Tolerable Imbalance (MTI) designs, to reduce the chance of chronological treatment imbalances. The impact of these restrictions on RBI and ANOVA concordance is not well understood. With real-world frames of reference, such as rare and ultra-rare diseases, we review full versus random sampling of finite populations and empirically evaluate finite population Type I error when using ANOVA following randomization restrictions. Randomization restrictions strongly impacted ANOVA Type I error, even for trials with 1,000 participants. Properly adjusting for restrictions corrected Type I error. We corrected for block randomization, yet leave open how to correct for MTI designs. More directly, RBI accounts for randomization restrictions while ensuring correct finite population Type I error. Novel contributions are: 1) deepening the understanding and correction of RBI and ANOVA concordance under block and MTI restrictions and 2) using finite populations to estimate the convergence of Type I error to a nominal rate. We discuss the challenge of specifying an estimand's population and reconciling with sampled trial participants.

stat.ME↗

arXiv2023

Rematching on-the-fly: sequential matched randomization and a case for covariate-adjusted randomization

Covariate-adjusted randomization (CAR) can reduce the risk of covariate imbalance and, when accounted for in analysis, increase the power of a trial. Despite CAR advances, stratified randomization remains the most common CAR method. Matched Randomization (MR) randomizes treatment assignment within optimally identified matched pairs based on covariates and a distance matrix. When participants enroll sequentially, Sequentially Matched Randomization (SMR) randomizes within matches found "on-the-fly" to meet a pre-specified matching threshold. However, pre-specifying the ideal threshold can be challenging and SMR yields less-optimal matches than MR. We extend SMR to allow multiple participants to be randomized simultaneously, to use a dynamic threshold, and to allow matches to break and rematch if a better match later enrolls (Sequential Rematched Randomization; SRR). In simplified settings and a real-world application, we assess whether these extensions improve covariate balance, estimator/study efficiency, and optimality of matches. We investigate whether adjusting for more covariates can be detrimental upon covariate balance and efficiency as is the case of traditional stratified randomization. As secondary objectives, we use the case study to assess how SMR schemes compare side-by-side with common and related CAR schemes and whether adjusting for covariates in the design can be as powerful as adjusting for covariates in a parametric model. We find each SMR extension, individually and collectively, to improve covariate balance, estimator efficiency, study power, and quality of matches. We provide a case-study where CAR schemes with randomization-based inference can be as and more powerful than Non-CAR schemes with parametric adjustment for covariates.

stat.ME↗

arXiv2022

Sequential monitoring using the Second Generation P-Value with Type I error controlled by monitoring frequency

Many adaptive monitoring schemes adjust the required evidence toward a hypothesis to control Type I error. This shifts focus away from determining scientific relevance with an uncompromised degree of evidence. We propose sequentially monitoring the Second Generation P-Value (SGPV) on repeated intervals until establishing evidence for scientific relevance (SeqSGPV). SeqSGPV encompasses existing strategies to monitor Region of Practical Equivalence (ROPE) or Region of Equivalence (ROE) hypotheses. Hence, our focus is to formalize sequential SGPV monitoring; establish a novel set of scientific hypotheses, called PRISM, which is a ROE with a ROPE surrounding the null hypothesis; and use monitoring frequency and a novel affirmation step to control Type I error. Under immediate and delayed outcomes, we assess finite and limiting SeqSGPV operating characteristics when monitoring PRISM, ROPE, and null-bound ROE hypotheses. In extensive simulations, SeqSGPV PRISM monitoring reduced wait time for fully sequential monitoring, average sample size, and reversals of null hypothesis conclusions under the null. With real-world data, we design a SeqSGPV-monitored randomized trial. SeqSGPV is method-agnostic and easy to implement. Adjusting monitoring frequency/affirmation and monitoring a one-sided PRISM synergistically control Type I error. PRISM monitoring and adjusting monitoring frequency to control Type I error may have application beyond SeqSGPV.

stat.ME↗