Statistics in Medicine最新文献

Genomics and other studies encounter many features and a selection of essential features with high accuracy is desired. In recent years, there has been a significant advancement in the use of Bayesian inference for variable (or feature) selection. However, there needs to be more practical information regarding their implementation and assessment of their relative performance. Our goal in this paper is to perform a comparative analysis of approaches, mainly from different Bayesian genres that apply to genomic analysis. In particular, we are examining how well shrinkage, global-local, and mixture priors, SUSIE, and a simple two-step procedure-namely, RFSFS, which we propose-perform in terms of several metrics: FDR, FNR, F-score, and mean squared prediction error under various simulation scenarios. There is no single method that outperforms others uniformly across all scenarios and in terms of variable selection and prediction performance metrics. So, we order the methods based on the average ranking across different scenarios. We found LASSO, spike-and-slab prior with normal slab (SN), and RFSFS are the most competitive methods for FDR and F-score when features are uncorrelated. When features are correlated, SN, SuSIE, and RFSFS are the most competitive methods for FDR whereas LASSO has an edge over SuSIE in terms of F-score. For illustration, we have applied these methods to analyzed The Cancer Genome Atlas Program (TCGA) renal cell carcinoma (RCC) data and have offered methodological direction.

Medical research often involves the study of composite endpoints that combine multiple clinical events to assess the efficacy of treatments. When constructing composite endpoints, it is a common practice to analyze the time to the first event. However, this approach overlooks outcomes that occur after the first event, resulting in information loss. Furthermore, the terminal event can not only be of interest but also, be a competing risk for other types of outcomes. While existing semi-parametric regression models can be used to analyze both fatal (terminal) and non-fatal composite events, potential nonlinear covariate effects on the logarithm of the rate function have not been addressed. To address this important issue, we introduce random forests for composite endpoints (Rforce) consisting of non-fatal composite events and terminal events. Rforce utilizes generalized estimating equations to build trees and handles the dependent censoring due to the terminal events with the concept of pseudo-at-risk duration. Simulation studies and real data analysis are conducted to demonstrate the performance of Rforce.

In this paper, we investigate the selection of minimal and efficient covariate adjustment sets for the imputation-based regression calibration method, which corrects for bias due to continuous exposure measurement error. We use directed acyclic graphs to illustrate how subject-matter knowledge aids in selecting these sets. For unbiased measurement error correction, researchers must collect, in both main and validation studies, (I) common causes of both the true exposure and the outcome, and (II) common causes of both measurement error and the outcome. For regression calibration under linear models, at minimum, covariate set (I) must be adjusted for in both the measurement error model (MEM) and the outcome model, while set (II) should be adjusted for in at least the MEM. Adjusting for non-risk factors that are correlates of true exposure or measurement error within the MEM alone improves efficiency. We apply this covariate selection approach to the Health Professionals Follow-up Study, assessing fiber intake's effect on cardiovascular disease. We also highlight potential pitfalls in data-driven MEM building that ignores structural assumptions. Additionally, we extend existing estimators to allow for effect modification. Finally, we caution against using regression calibration to estimate the effect of true nutritional intake through calibrating biomarkers.

Network meta-analysis of randomized controlled trials is traditionally conducted on a single outcome measured at one time point. However, many trials also feature a secondary outcome and both outcomes may have been reported at multiple time points. Existing network meta-analysis methods for synthesizing continuous outcome data from such trials focus on either the longitudinal data aspect or the multiple outcomes aspect, but not on both simultaneously. In this paper, we present two Bayesian network meta-analysis models that account for the correlation of outcome measurements over time using Gaussian random walks with drift. The first model is suitable for a single continuous outcome measured at multiple time points, while the second model extends the first model to allow incorporation of a second outcome through cointegration of random walks. A simulation study to evaluate several statistical properties of these models is conducted. The results indicate that both proposed models produce unbiased estimates of relative treatment effect and drift parameters, as well as reasonable coverage. Furthermore, in some scenarios, using the cointegration model yields small gains in precision over using the single outcome model. Based on various performance measures, both proposed models also outperform an existing random walk network meta-analysis model previously used by investigators to synthesize osteoarthritis trials data. The proposed models are illustrated with an application to trials evaluating treatments for knee and hip osteoarthritis. Both models are useful additions to existing tools available to investigators undertaking a network meta-analysis of continuous outcome data at multiple time points.

In pharmacoepidemiology, safety and effectiveness are frequently evaluated using readily available administrative and electronic health records data. In these settings, detailed confounder data are often not available in all data sources and therefore missing on a subset of individuals. Multiple imputation (MI) and inverse-probability weighting (IPW) are go-to analytical methods to handle missing data and are dominant in the biomedical literature. Doubly-robust methods, which are consistent under fewer assumptions, can be more efficient with respect to mean-squared error. We discuss two practical-to-implement doubly-robust estimators, generalized raking and inverse probability-weighted targeted maximum likelihood estimation (TMLE), which are both currently under-utilized in biomedical studies. We compare their performance to IPW and MI in a detailed numerical study for a variety of synthetic data-generating and missingness scenarios, including scenarios with rare outcomes and a high missingness proportion. Further, we consider plasmode simulation studies that emulate the complex data structure of a large electronic health records cohort in order to compare anti-depressant therapies in a rare-outcome setting where a key confounder is prone to more than 50% missingness. We provide guidance on selecting a missing data analysis approach, based on which methods excelled with respect to the bias-variance trade-off across the different scenarios studied.

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.

Multi-cancer testing with localization aims to detect signals from any of a set of targeted cancer types and predict the cancer signal origin from a single biological sample. Such tests have the potential to aid clinical decisions and significantly improve health outcomes. When used for multi-cancer screening in an asymptomatic population, these tests are referred to as multi-cancer early detection (MCED) tests. MCED testing has not yet achieved regulatory approval, reimbursement or broad clinical adoption. Some major reasons for this are that the clinical benefits and harms are not well understood, including the risk of unnecessary work-ups and false reassurance from a negative test that could reduce uptake of standard-of-care screening. Part of this uncertainty stems from the use of clinically obtuse metrics to assess the test's clinical validity. Traditionally, performance of MCED tests has been quantified using aggregate measures, disregarding the joint distribution of cancer type, stage (both at intended-use incidence rates) and predicted cancer signal origin, thereby obscuring biological variability and underlying differences in the test's behavior and limiting insight into true effectiveness. Clinically informative evaluation of an MCED test's performance requires metrics that are specific to cancer type, stage and predicted cancer origin at expected incidence rates in the intended-use population. In the context of a case-control sampling design, this paper derives analytical methods that allow for unbiased estimation of cancer-specific intrinsic accuracy, predicted cancer signal origin-specific predictive value and the marginal test classification distribution, each with corresponding valid confidence interval formulae. A simulation study is presented that evaluates performance of the proposed methodology and provides guidance for implementation. An application to a published MCED test dataset is given. The derived statistical analysis framework in general allows for estimation and inference for pointed metrics of a multi-category test that enables precisely informed decision-making, supports optimized trial designs across classical, digital, AI-driven, and hybrid stratified diagnostic screening platforms, and facilitates informed healthcare decisions by clinicians, policymakers, regulators, scientists, and patients.

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.