Statistical Methods in Medical Research最新文献

Multi-armed multi-stage designs evaluate experimental treatments using a control arm at interim analyses. Incorporating response-adaptive randomisation in these designs allows early stopping, faster treatment selection and more patients to be assigned to the more promising treatments. Existing frequentist multi-armed multi-stage designs demonstrate that the family-wise error rate is strongly controlled, but they may be too conservative and lack power when the experimental treatments are very different therapies rather than doses of the same drug. Moreover, the designs use a fixed allocation ratio. In this article, Fisher's least significant difference method extended to group-sequential response-adaptive designs is investigated. It is shown mathematically that the information time continues after dropping inferior arms, and hence the error-spending approach can be used to control the family-wise error rate. Two optimal allocations were considered. One ensures efficient estimation of the treatment effects and the other maximises the power subject to a fixed total sample size. Operating characteristics of the group-sequential response-adaptive design for normal and censored survival outcomes based on simulation and redesigning the NeoSphere trial were compared with those of a fixed-sample design. Results show that the adaptive design attains efficient and ethical advantages, and that the family-wise error rate is well controlled.

In clinical trials, response-adaptive randomization (RAR) has gained increasing attention due to its ability to assign more patients to better-performing treatments. Consequently, several RAR methods have been proposed in recent years. Among them, the efficient response adaptive randomization design (ERADE), proposed by Hu et al. (2009), stands out as an optimal approach, with the asymptotic variance of the allocation proportion achieving the Cramér-Rao lower bound, demonstrating its statistical efficiency. However, the original ERADE is limited to trials with only two treatment arms. Given the growing prevalence of multi-arm trials in modern clinical development, the original ERADE design no longer meets all practical needs. In this paper, we extend ERADE for use in multi-arm clinical trials, proposing the multi-arm ERADE algorithm. We establish the asymptotic properties of this generalized design and demonstrate its effectiveness in finite sample settings through simulations and a real-world trial redesign.

The prudent use of covariates to enhance the efficiency and ethics of clinical trials has garnered significant attention, particularly following the FDA's 2023 guidance on adjusting for covariates. This article introduces a Bayesian covariate-adjusted response-adaptive design aimed at distinguishing between prognostic and predictive covariates during randomization and analysis. The proposed design allocates more patients to the superior treatment based on predictive covariates while maintaining balance across prognostic covariate levels, without sacrificing the power to detect treatment effects. Predictive covariates, which identify patients more likely to benefit from a treatment, and prognostic covariates, which predict overall clinical outcomes, are crucial for personalized medicine and ethical rigor in clinical trials. The Bayesian covariate-adjusted response-adaptive design leverages these covariates to enhance precision and ensure balanced comparison groups, addressing patient heterogeneity and improving treatment efficacy. Our approach builds on the foundation of response-adaptive randomization designs, incorporating Bayesian methodologies to manage the complexities of adaptive designs and control the Type I error rate. Comprehensive numerical studies demonstrate the advantages of our design in achieving ethical, efficient, and balancing goals.

Randomized controlled trials (RCTs) are pivotal for evaluating the efficacy of medical treatments and interventions, serving as a cornerstone in clinical research. In addition to randomization, achieving balances among multiple targets, such as statistical validity, efficiency, and ethical considerations, is also a central issue in RCTs. The doubly-adaptive biased coin design (DBCD) is notable for its high flexibility and efficiency in achieving any predetermined optimal allocation ratio and reducing variance for a given target allocation. However, DBCD does not account for abundant covariates that may be correlated with responses, which could further enhance trial efficiency. To address this limitation, this article explores the use of covariates in the analysis stage and evaluates the benefits of nonlinear covariate adjustment for estimating treatment effects. We propose a general framework to capture the intricate relationship between subjects' covariates and responses, supported by rigorous theoretical derivation and empirical validation via simulation study. Additionally, we introduce the use of sample splitting techniques for machine learning methods under DBCD, demonstrating the effectiveness of the corresponding estimators in high-dimensional cases. This paper aims to advance both the theoretical research and practical application of DBCD, thereby achieving more accurate and ethical clinical trials.

Introduction: Adaptive designs (ADs) offer clinical trials flexibility to modify design aspects based on accumulating interim data. Response adaptive randomisation (RAR) adjusts treatment allocation according to interim results, favouring promising treatments. Despite scientific appeal, RAR adoption lags behind other ADs. Understanding methods and applications could provide insights and resources and reveal future research needs. This study examines RAR application, trial results and achieved benefits, reporting gaps, statistical tools and concerns, while highlighting examples of effective practices. Methods: RAR trials with comparative efficacy, effectiveness or safety objectives, classified at least phase I/II, were identified via statistical literature, trial registries, statistical resources and researcher-knowledge. Search spanned until October 2023, including results until February 2024. Analysis was descriptive and narrative. Results: From 652 articles/trials screened, 65 planned RAR trials (11 platform trials) were identified, beginning in 1985 and gradually increasing through to 2023. Most trials were in oncology (25%) and drug-treatments (80%), with 63% led by US teams. Predominantly Phase II (62%) and multi-arm (63%), 85% used Bayesian methods, testing superiority hypotheses (86%). Binary outcomes appeared in 55%, with a median observation of 56 days. Bayesian RAR algorithms were applied in 83%. However, 71% of all trials lacked clear details on statistical implementation. Subgroup-level RAR was seen in 23% of trials. Allocation was restricted in 51%, and 88% was included a burn-in period. Most trials (85%) planned RAR alongside other adaptations. Of trials with results, 92% used RAR, but over 50% inadequately reported allocation changes. A mean 22% reduction in sample size was seen, with none over-allocating to ineffective arms. Conclusion: RAR has shown benefits in conditions like sepsis, COVID-19 and cancer, enhancing effective treatment allocation and saving resources. However, complexity, costs and simulation need limit wider adoption. This review highlights RAR's benefits and suggests enhancing statistical tools to encourage wider adoption in clinical research.

The rapidly developing field of personalized medicine is giving the opportunity to treat patients with a specific regimen according to their individual demographic, biological, or genomic characteristics, known also as biomarkers. While binary biomarkers simplify subgroup selection, challenges arise in the presence of continuous ones, which are often categorized based on data-driven quantiles. In the context of binary response trials for treatment comparisons, this paper proposes a method for determining the optimal cutoff of a continuous predictive biomarker to discriminate between sensitive and insensitive patients, based on their relative risk. We derived the optimal design to estimate such a cutoff, which requires a set of equality constraints that involve the unknown model parameters and the patients' biomarker values and are not directly attainable. To implement the optimal design, a novel covariate-adjusted response-adaptive randomization is introduced, aimed at sequentially minimizing the Euclidean distance between the current allocation and the optimum. An extensive simulation study shows the performance of the proposed approach in terms of estimation efficiency and variance of the estimated cutoff. Finally, we show the potential severe ethical impact of adopting the data-dependent median to identify the subpopulations.

Linear models are extensively used in the analysis of clinical trials. However, required model assumptions (e.g. homoscedasticity) may not be satisfied in practice, resulting in low power of treatment-covariate interaction tests. Various interaction tests have been proposed to improve the efficiency of detecting differences in treatment-covariate interactions. Aiming to fundamentally improve the power of treatment-covariate interaction tests, for heteroscedasticity of treatment responses, we develop a model-based optimal randomization procedure, referred to as model-based Neyman allocation (MNA) in this article. The derived limiting allocation proportion indicates that the procedure MNA is a generalization of response-adaptive randomization targeting Neyman allocation (RAR-NA). In theory, we demonstrate that the procedure MNA can maximize the power of treatment-covariate interaction tests. The issue of sample size estimation is also addressed. Simulation studies show, in the framework of the heteroscedastic linear model, compared with Pocock and Simon's minimization method and RAR-NA, the procedure MNA has the greatest power of tests for both systematic effects and treatment-covariate interactions, even under model misspecification. Finally, the efficiency of the procedure MNA is illustrated by a hypothetical case study based on a real schizophrenia clinical trial.

Missing data is a widespread issue in clinical trials, but is particularly problematic for digital health interventions where disengagement is common and outcomes are likely to be missing not at random (MNAR). Trials that use response-adaptive designs need to handle missingness online and not simply at the end of the trial. We propose a novel online imputation strategy which allows previous imputations to be re-imputed given updated estimates of success probabilities. We additionally consider: (i) truncation of deterministic algorithms to prevent extreme realised treatment imbalance and (ii) changing the random component of semi-randomised algorithms. Through a simulation study based on a trial for a digital smoking cessation intervention, we illustrate how the strategy for handling missing responses can affect the exploration-exploitation tradeoff and the bias of the estimated success probabilities at the end of the trial. In the settings explored, we found that the exploration-exploitation tradeoff is affected particularly when arms have very different rates of missingness and we identified combinations of response-adaptive designs and missingness strategies that are particularly problematic. Further, we show that estimated success probabilities at the end of the trial can be biased not only due to optimistic sampling, but potentially also due to an MNAR missingness mechanism.

Multivariate imputation using chained equations is a popular algorithm for imputing missing data that entails specifying multivariable models through conditional distributions. Two standard imputation methods for imputing missing continuous variables are parametric imputation using a linear model and predictive mean matching. The default methods for imputing missing categorical variables are parametric imputation using multinomial logistic regression and ordinal logistic regression for imputing nominal and ordinal categorical variables, respectively. There is a paucity of research into the relative computational burden and the quality of statistical inferences when using predictive mean matching versus parametric imputation for imputing missing non-binary categorical variables. We used simulations to compare the performance of predictive mean matching with that of multinomial logistic regression and ordinal logistic regression for imputing categorical variables when the analysis model of scientific interest was a logistic or linear regression model. We varied the sample size (N = 500, 1000, 2500, and 5000), the rate of missing data (5%-50% in increments of 5%), and the number of levels of the categorical variable (3, 4, 5, and 6). In general, the performance of predictive mean matching compared very favorably to that of multinomial or ordinal logistic regression for imputing categorical variables when the analysis model was a logistic or linear regression model. This was true across a range of scenarios defined by sample size and the rate of missing data. Furthermore, the use of predictive mean matching was substantially faster, by a factor of 2-6. In conclusion, predictive mean matching can be used to impute categorical variables. The use of predictive mean matching to impute missing non-binary categorical variables substantially reduces computer processing time when conducting multiple imputation.

Cluster randomized trials are widely used in healthcare research for the evaluation of intervention strategies. Beyond estimating the average treatment effect, it is often of interest to assess whether the treatment effect varies across subgroups. While conventional methods based on tests of interaction terms between treatment and covariates can be used to detect treatment effect heterogeneity in cluster randomized trials, they typically rely on parametric assumptions that may not hold in practice. Adapting existing permutation tests from individually randomized trials, however, requires conceptual clarification and modification due to the multiple possible interpretations of treatment effect heterogeneity in the cluster randomized trial context. In this work, we develop variations of permutation tests and clarify key causal definitions in order to assess treatment effect heterogeneity in cluster randomized trials. Our procedure enables investigators to simultaneously test for effect modification across a large number of covariates, while maintaining nominal type I error rates and reasonable power in simulation studies. In the Pain Program for Active Coping and Training (PPACT) study, the proposed methods are able to detect treatment effect heterogeneity that was not identified by conventional methods assessing treatment-covariate interactions.