Statistics in Medicine最新文献

Effective decision-making plays a vital role throughout the drug development process, particularly when a proof-of-concept (POC) or phase II study has been completed. To determine whether to proceed to a larger-scale, confirmatory phase III study, assessing the uncertainty about the underlying treatment effect and the probability of success (POS) in the phase III study is of critical importance. In this paper, we proposed and investigated a Bayesian covariate-adjusted hierarchical modeling approach leveraging historical data with longitudinal outcome to quantitatively assess the POS of the confirmatory phase III trial. Although historical data borrowing methods are widely used and known for the advantages in alleviating recruitment and ethical challenges as well as improving trial operational efficiency, its application to predicting future trial POS with longitudinal outcome over multiple visits pose methodological challenges. This paper not only provided a comprehensive modeling approach but also demonstrated how the proposed model can be used in a Go/No-Go decision-making framework with a glaucoma eye care project example. For the approval of new drugs targeting glaucoma, regulatory agencies typically require a pivotal phase III trial to demonstrate noninferiority compared to a standard of care treatment. This may involve meeting both statistical and clinical margins across multiple visits simultaneously. Simulations were performed to evaluate the key factors that affect the operating characteristics, such as between-trial heterogeneity, subject-level variance and between-visit correlation. The proposed decision-making framework can also be applied to studies in other therapeutical areas with similar settings.

Microbiome analysis is the process of identifying the composition and function of a community of microorganisms in a particular location, which is essential in understanding human and environmental health. Properly quantifying microbial composition, however, remains challenging and relies on statistical modeling of either the raw taxonomic abundances or the relative abundances. Relative abundance measures are commonly preferred over the absolute abundances for microbiome analysis because absolute abundance values are dependent on the sequencing depth and sequencing method. Despite this, literature on modeling relative abundance by meaningful probability distribution, followed by subsequent statistical inferences, is limited. In this work, the Dirichlet distribution is proposed to model the relative abundances of taxa directly without the use of any further transformation (e.g., additive log-ratio transform, isometric log-ratio transform). In a comprehensive simulation study, we have compared biases and standard errors of two methods of moments estimators (MMEs) and the maximum likelihood estimator (MLE) of the Dirichlet distribution. comparison of these estimators is done over three cases of differing sample size and dimension: (i) Small dimension and small sample size; (ii) small dimension and large sample size; (iii) large dimension with both small and large sample sizes. As expected, the MLE shows the overall best performance because there is no loss of information since this estimator is based on the (minimal) sufficient statistics. We then explore the asymptotic properties of the MLE utilizing the Fisher information alongside our simulation results. We demonstrate the applicability of Dirichlet modeling methodology with four real world microbiome datasets and show how the estimated mean relative abundances obtained from the Dirichlet MLE (DMLE) differ from those obtained by a commonly used method, that is-Bayesian Dirichlet-multinomial estimator (BDME), which works with absolute abundances. For all the four datasets, the DMLE results are comparable to the BDME results while requiring much less computational time for both single uses and for large simulations.

It is crucial to design Phase II cancer clinical trials that balance the efficiency of treatment selection with clinical practicality. Sargent and Goldberg proposed a frequentist design that allows decision-making even when the primary endpoint is ambiguous. However, frequentist approaches rely on fixed thresholds and long-run frequency properties, which can limit flexibility in practical applications. In contrast, the Bayesian decision rule, based on posterior probabilities, enables transparent decision-making by incorporating prior knowledge and updating beliefs with new data, addressing some of the inherent limitations of frequentist designs. In this study, we propose a novel Bayesian design, allowing the selection of the best-performing treatment. Specifically, concerning phase II clinical trials with a binary outcome, our decision rule employs posterior interval probability by integrating the joint distribution over all values, for which the 'success rate' of the best-performing treatment is greater than that of the others. This design can then determine which treatment should proceed to the next phase, given predefined decision thresholds. Furthermore, we propose two sample size determination methods to empower such treatment selection designs implemented in a Bayesian framework. Through simulation studies and real-data applications, we demonstrate how this approach can overcome challenges related to sample size constraints in randomised trials. In addition, we present a user-friendly R Shiny application, enabling clinicians to conduct Bayesian designs. Both our methodology and the software application can advance the design and analysis of clinical trials for evaluating cancer treatments.

Cluster randomized controlled trials where groups (or clusters) of individuals, rather than single individuals, are randomized are especially useful when individual-level randomization is not feasible or when interventions are naturally delivered at the group level. Balanced randomization in the cluster randomized trial setting can pose logistical challenges and strain resources if subjects are randomized to a non-optimal arm. We propose a Bayesian response-adaptive randomization design for cluster randomized controlled trials based on Thompson sampling, which dynamically allocates clusters to the most efficacious treatment arm based on the interim posterior distributions of treatment effects using Markov chain Monte Carlo sampling. Our design also incorporates early stopping rules for efficacy and futility determined by prespecified posterior probability thresholds. The performance of the proposed design is evaluated across various operating characteristics under multiple settings, including varying intra-cluster correlation coefficients, cluster sizes, and effect sizes. Our adaptive approach is also compared with a standard, parallel two-arm cluster randomized controlled clinical trial design, highlighting improvements in both ethical considerations and efficiency. From our simulation studies based on an HIV behavioral trial, we demonstrate these improvements by preferentially assigning more clusters to the more efficacious intervention while maintaining robust statistical power and controlling false positive rates.

Phase II clinical trials play a pivotal role in drug development by screening a large number of drug candidates to identify those with promising preliminary efficacy for phase III testing. Trial designs that enable efficient decision-making with small sample sizes and early futility stopping while controlling for type I and type II errors in hypothesis testing, such as Simon's two-stage design, are preferred. Randomized multi-arm trials are increasingly used in phase II settings to overcome the limitations associated with using historical controls as the reference. However, how to effectively balance efficiency and accurate decision-making continues to be an important research topic. A notable development in phase II randomized design methodology is the Bayesian pick-the-winner (BPW) design proposed by Chen et al. [1]. Despite multiple appealing features, this method cannot easily control for overall type I and type II errors for winner selection. Here, we introduce an improved randomized two-stage Bayesian pick-the-winner (IBPW) design that formalizes the winner-selection based hypothesis testing, optimizes sample sizes and decision cut-offs by strictly controlling the type I and type II errors under a set of flexible hypotheses for winner-selection across two treatment arms. Simulation studies demonstrate that our new design offers improved operating characteristics for winner selection while retaining the desirable features of the BPW design.

In recent years, two parallel research trends have emerged in machine learning, yet their intersections remain largely unexplored. On one hand, there has been a significant increase in literature focused on Individual Treatment Effect (ITE) modeling, particularly targeting the Conditional Average Treatment Effect (CATE) using meta-learner techniques. These approaches often aim to identify causal effects from observational data. On the other hand, the field of Explainable Machine Learning (XML) has gained traction, with various approaches developed to explain complex models and make their predictions more interpretable. A prominent technique in this area is Shapley Additive Explanations (SHAP), which has become mainstream in data science for analyzing supervised learning models. However, there has been limited exploration of SHAP's application in identifying predictive biomarkers through CATE models, a crucial aspect in pharmaceutical precision medicine. We address inherent challenges associated with the SHAP concept in multi-stage CATE strategies and introduce a surrogate estimation approach that is agnostic to the choice of CATE strategy, effectively reducing computational burdens in high-dimensional data. Using this approach, we conduct simulation benchmarking to evaluate the ability to accurately identify biomarkers using SHAP values derived from various CATE meta-learners and Causal Forest.

This research is motivated by integrated epidemiological and blood biomarker studies, investigating the relationship between long-term adherence to a Mediterranean diet and cardiometabolic health, with plasma metabolomes as potential mediators. Analyzing causal mediation in high-dimensional omics data presents challenges, including complex dependencies among mediators and the need for advanced regularization or Bayesian techniques to ensure stable and interpretable estimation and selection of indirect effects. To this end, we propose a novel Bayesian framework to identify active pathways and estimate indirect effects in high-dimensional mediation analysis. Central to our method is the introduction of a set of priors for the selection indicators in the mediator and outcome models. A Markov random field prior leverages mediator correlations, enhancing power in detecting mediated effects. Sequential subsetting priors encourage simultaneous selection of relevant mediators and their indirect effects, ensuring a more coherent and efficient variable selection framework. Comprehensive simulation studies demonstrate that the proposed method provides superior power in detecting active mediating pathways. We further illustrate the practical utility of the method by applying it to metabolome data from two sub-studies within the Health Professionals Follow-up Study and Nurses' Health Study II, highlighting its effectiveness in a real-data setting.

Generalizing causal findings, such as the average treatment effect (ATE), from a source to a target population is a critical topic in biomedical research. Differences in the distributions of treatment effect modifiers between these populations, known as covariate shift, can lead to varying ATEs. Chen et al. [1] introduced a weighting method to estimate the target ATE using only summary-level information from a target sample while accounting for the possible covariate shifts. However, the asymptotic variance of the estimate was shown to depend on individual-level data from the target sample, hindering statistical inference. In this article, we propose a resampling-based perturbation method for confidence interval construction for the estimated target ATE, utilizing additional summary-level information. We demonstrate the effectiveness of our approach through simulation and real data settings when only summary-level information is available.

For a chronic disease, besides the treatment induction effect, it is also important to demonstrate the maintenance effect of long-term treatment use. To fulfill these and other objectives for a clinical study, we often apply one of three designs: the active treatment lead-in followed by randomized maintenance design, the randomized induction followed by re-randomized withdrawal maintenance design and the treat-through design (FDA 2022). Separately, a two-stage sequential parallel comparison design (SPCD) is frequently used in therapeutic areas where placebo has a large effect. In this paper, we use a SPCD for a clinical trial with a binary endpoint for induction, maintenance, long-term and other treatment effect assessments. This SPCD can actually be treated as a hybrid of the above three designs and has some additional advantages. For example, compared to the re-randomized withdrawal maintenance design, the SPCD does not need a re-randomization to simplify trial operation and it also provides controlled data for formal long-term efficacy and safety analyses. To fully utilize all available data of the two stages for an overall treatment effect evaluation, a weighted combination test is considered with the incorporation of correlations of the components. Further, a multiple imputation approach is applied to handle missing not at random data. Simulations are conducted to evaluate the performances of the methods and a data example is employed to illustrate the applications of the methods.

Statistical inference in multicenter clinical trials is often compromised when relying on asymptotic normal approximations, particularly in designs characterized by a small number of centers or severe imbalance in patient enrollment. Such deviations from asymptotic assumptions frequently result in unreliable p-values and a breakdown of error control. To resolve this, we introduce a high-precision saddlepoint approximation framework for aggregate permutation tests within hierarchically structured data. The theoretical core of our approach is the derivation of a multilevel nested cumulant generating function that explicitly models the trial hierarchy, analytically integrating patient-level linear rank statistics with the stochastic aggregation process across centers. A significant innovation of this work is the extension to the bivariate setting to address co-primary endpoints, providing a robust inferential solution for mixed continuous (efficacy) and discrete (safety) outcomes where standard multivariate normality is unattainable. The resulting framework yields simulation-free, highly accurate tail probabilities even in finite-sample regimes. Extensive simulation studies confirm that our method maintains strict Type I error control in scenarios where asymptotic methods exhibit substantial inflation. Furthermore, an application to the multicenter diabetes prevention program trial demonstrates the method's practical utility: it correctly identifies a significant cardiovascular risk factor that standard approximations failed to detect, thereby preventing a critical Type II error and ensuring valid clinical conclusions.