Statistical Methods in Medical Research最新文献

G-formula is a popular approach for estimating the effects of time-varying treatments or exposures from longitudinal data. G-formula is typically implemented using Monte-Carlo simulation, with non-parametric bootstrapping used for inference. In longitudinal data settings missing data are a common issue, which are often handled using multiple imputation, but it is unclear how G-formula and multiple imputation should be combined. We show how G-formula can be implemented using Bayesian multiple imputation methods for synthetic data, and that by doing so, we can impute missing data and simulate the counterfactuals of interest within a single coherent approach. We describe how this can be achieved using standard multiple imputation software and explore its performance using a simulation study and an application from cystic fibrosis.

Pilot trials are often conducted in advance of definitive trials to assess their feasibility and to inform their design. Although pilot trials typically collect primary endpoint data, preliminary tests of effectiveness have been discouraged given their typically low power. Power could be increased at the cost of a higher type I error rate, but there is little methodological guidance on how to determine the optimal balance between these operating characteristics. We consider a Bayesian decision-theoretic approach to this problem, introducing a utility function and defining an optimal pilot and definitive trial programme as that which maximises expected utility. We base utility on changes in average primary outcome, the cost of sampling, treatment costs, and the decision-maker's attitude to risk. We apply this approach to re-design OK-Diabetes, a pilot trial of a complex intervention with a continuous primary outcome with known standard deviation. We then examine how optimal programme characteristics vary with the parameters of the utility function. We find that the conventional approach of not testing for effectiveness in pilot trials can be considerably sub-optimal.

In the context of precision medicine, covariate-adjusted response-adaptive (CARA) randomization has garnered much attention from both academia and industry due to its benefits in providing ethical and tailored treatment assignments based on patients' profiles while still preserving favorable statistical properties. Recent years have seen substantial progress in inference for various adaptive experimental designs. In particular, research has focused on two important perspectives: how to obtain robust inference in the presence of model misspecification, and what the smallest variance, i.e., the efficiency bound, an estimator can achieve. Notably, Armstrong (2022) derived the asymptotic efficiency bound for any randomization procedure that assigns treatments depending on covariates and accrued responses, thus including CARA, among others. However, to the best of our knowledge, no existing literature has addressed whether and how this bound can be achieved under CARA. In this paper, by connecting two strands of adaptive randomization literature, namely robust inference and efficiency bound, we provide a definitive answer in an important practical scenario where only discrete covariates are observed and used for stratification. We consider a special type of CARA, i.e., a stratified version of doubly-adaptive biased coin design and prove that the stratified difference-in-means estimator achieves Armstrong (2022)'s efficiency bound, with possible ethical constraints on treatment assignments. Our work provides new insights and demonstrates the potential for more research on CARA designs that maximize efficiency while adhering to ethical considerations. Future studies could explore achieving the asymptotic efficiency bound for CARA with continuous covariates, which remains an open question.

This study addresses a critical gap in the design of clinical trials that use grouped sequential designs for one-sample or paired ordinal categorical outcomes. Single-arm experiments, such as those using the modified Rankin Scale in stroke trials, underscore the necessity of our work. We present a novel method for applying the Wilcoxon signed-rank test to grouped sequences in these contexts. Our approach provides a practical and theoretical framework for assessing treatment effects, detailing variance formulas and demonstrating the asymptotic normality of the U-statistic. Through simulation studies and real data analysis, we validate the empirical Type I error rates and power. Additionally, we include a comprehensive flowchart to guide researchers in determining the required sample size to achieve specified power levels while controlling Type I error rates, thereby enhancing the design process of sequential trials.

In this article, we develop nonparametric inference methods for comparing survival data across two samples, beneficial for clinical trials of novel cancer therapies where long-term survival is critical. These therapies, including immunotherapies and other advanced treatments, aim to establish durable effects. They often exhibit distinct survival patterns such as crossing or delayed separation and potentially leveling-off at the tails of survival curves, violating the proportional hazards assumption and rendering the hazard ratio inappropriate for measuring treatment effects. Our methodology uses the mixture cure framework to separately analyze cure rates of long-term survivors and the survival functions of susceptible individuals. We evaluated a nonparametric estimator for the susceptible survival function in a one-sample setting. Under sufficient follow-up, it is expressed as a location-scale-shift variant of the Kaplan-Meier estimator. It retains desirable features of the Kaplan-Meier estimator, including inverse-probability-censoring weighting, product-limit estimation, self-consistency, and nonparametric efficiency. Under insufficient follow-up, it can be adapted by incorporating a suitable cure rate estimator. In the two-sample setting, in addition to using the difference in cure rates to measure long-term effects, we propose a graphical estimand to compare relative treatment effects on susceptible subgroups. This process, inspired by Kendall's tau, compares the order of survival times among susceptible individuals. Large-sample properties of the proposed methods are derived for inference and their finite-sample properties are evaluated through simulations. The methodology is applied to analyze digitized data from the CheckMate 067 trial.

Simultaneously performing variable selection and inference in high-dimensional regression models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately select the most important predictors in a high-dimensional space, while controlling the false discovery rate (FDR) associated with the variable selection procedure. In this paper, we propose the joint adoption of the Mirror Statistic approach to FDR control, coupled with outcome randomisation to maximise the statistical power of the variable selection procedure, measured through the true positive rate. Through extensive simulations, we show how our proposed strategy allows us to combine the benefits of the two techniques. The Mirror Statistic is a flexible method to control FDR, which only requires mild model assumptions, but requires two sets of independent regression coefficient estimates, usually obtained after splitting the original dataset. Outcome randomisation is an alternative to data splitting that allows to generate two independent outcomes, which can then be used to estimate the coefficients that go into the construction of the Mirror Statistic. The combination of these two approaches provides increased testing power in a number of scenarios, such as highly correlated covariates and high percentages of active variables. Moreover, it is scalable to very high-dimensional problems, since the algorithm has a low memory footprint and only requires a single run on the full dataset, as opposed to iterative alternatives such as multiple data splitting.

There is growing use of shared-patient physician networks in health services research and practice, but minimal study of the consequences of decisions made in constructing them. To address this gap, we surveyed physician employees of a National Physician Organization (NPO) on their peer physician relationships. Using the physicians' survey nominations as ground truths, we evaluated the diagnostic accuracy of shared-patient edge-weights and the optimal construction of physician networks from sequences of patient-physician encounters. To further improve diagnostic accuracy, we optimized network construction with respect to the within-dyad difference and summation of edge-strength (two orthogonal measures), optimally combining them to form a final edge-weight. To achieve these goals, we develop statistical procedures to quantify the extent that directionality and other features of referral paths yield edge-weights with improved diagnostic properties. We also develop network models of the survey nominations incorporating directed (edge) and undirected (dyadic) shared-patient network measures as edge and dyad attributes to demonstrate that the measurement of the network as a whole is improved. Finally, we estimate the association of the physicians' centrality in the NPO shared-patient network (a sociocentric feature that cannot be evaluated for the partially-measured survey-based network) with their beliefs regarding physician peer-influence.

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.

A novel mixture cure frailty model is introduced for handling censored survival data. Mixture cure models are preferable when the existence of a cured fraction among patients can be assumed. However, such models are heavily underexplored: frailty structures within cure models remain largely undeveloped, and furthermore, most existing methods do not work for high-dimensional datasets, when the number of predictors is significantly larger than the number of observations. In this study, we introduce a novel extension of the Weibull mixture cure model that incorporates a frailty component, employed to model an underlying latent population heterogeneity with respect to the outcome risk. Additionally, high-dimensional covariates are integrated into both the cure rate and survival part of the model, providing a comprehensive approach to employ the model in the context of high-dimensional omics data. We also perform variable selection via an adaptive elastic-net penalization, and propose a novel approach to inference using the expectation-maximization (EM) algorithm. Extensive simulation studies are conducted across various scenarios to demonstrate the performance of the model, and results indicate that our proposed method outperforms competitor models. We apply the novel approach to analyze RNAseq gene expression data from bulk breast cancer patients included in The Cancer Genome Atlas (TCGA) database. A set of prognostic biomarkers is then derived from selected genes, and subsequently validated via both functional enrichment analysis and comparison to the existing biological literature. Finally, a prognostic risk score index based on the identified biomarkers is proposed and validated by exploring the patients' survival.

Recently, targeted and immunotherapy cancer treatments have motivated dose-finding based on efficacy-toxicity trade-offs rather than toxicity alone. The EffTox and utility-based Bayesian optimal interval (U-BOIN) dose-finding designs were developed in response to this need, but may be sensitive to elicited subjective design parameters that reflect the trade-off between efficacy and toxicity. To ease elicitation and reduce subjectivity, we propose dose desirability criteria that only depend on a preferential ordering of the joint efficacy-toxicity outcomes. We propose two novel order-based criteria and compare them with utility-based and contour-based criteria when paired with the design framework and probability models of EffTox and U-BOIN. The proposed dose desirability criteria simplify implementation and improve robustness to the elicited subjective design parameters and perform similarly in simulation studies to the established EffTox and U-BOIN designs when the ordering of the joint outcomes is equivalent. We also propose an alternative dose admissibility criteria based on the joint efficacy and toxicity profile of a dose rather than its marginal toxicity and efficacy profile. We argue that this alternative joint criterion is more consistent with defining dose desirability in terms of efficacy-toxicity trade-offs than the standard marginal admissibility criteria. The proposed methods enhance the usability and robustness of dose-finding designs that account for efficacy-toxicity trade-offs to identify the optimal biological dose.