Statistical Methods in Medical Research最新文献

Multiple efficacy endpoints are investigated in clinical trials, and selecting the appropriate primary endpoints is key to the study's success. The global test is an analysis approach that can handle multiple endpoints without multiplicity adjustment. This test, which aggregates the statistics from multiple primary endpoints into a single statistic using weights for the statistical comparison, has been gaining increasing attention. A key consideration in the global test is determination of the weights. In this study, we propose a novel global rank test in which the weights for each endpoint are estimated based on the current study data to maximize the test statistic, and the permutation test is applied to control the type I error rate. Simulation studies conducted to compare the proposed test with other global tests show that the proposed test can control the type I error rate at the nominal level, regardless of the number of primary endpoints and correlations between endpoints. Additionally, the proposed test offers higher statistical powers when the efficacy is considerably different between endpoints or when endpoints are moderately correlated, such as when the correlation coefficient is greater than or equal to 0.5.

Cluster randomized trials commonly employ multiple endpoints. When a single summary of treatment effects across endpoints is of primary interest, global methods represent a common analysis strategy. However, specification of the required joint distribution is non-trivial, particularly when the endpoints have different scales. We develop rank-based interval estimators for a global treatment effect referred to here as the "global win probability, or the mean of multiple Wilcoxon Mann-Whitney probabilities, and interpreted as the probability that a treatment individual responds better than a control individual on average. Using endpoint-specific ranks among the combined sample and within each arm, each individual-level observation is converted to a "win fraction" which quantifies the proportion of wins experienced over every observation in the comparison arm. An individual's multiple observations are then replaced with a single "global win fraction" by averaging win fractions across endpoints. A linear mixed model is applied directly to the global win fractions to obtain point, variance, and interval estimates adjusted for clustering. Simulation demonstrates our approach performs well concerning confidence interval coverage and type I error, and methods are easily implemented using standard software. A case study using public data is provided with corresponding R and SAS code.

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.

Measurement error is a prevalent issue in high-dimensional generalized linear regression that existing regularization techniques may inadequately address. Most require estimating error distributions, which can be computationally prohibitive or unrealistic. We introduce an error distribution-free approach for variable selection called the Iterative Matrix Uncertainty Selector (IMUS). IMUS employs the matrix uncertainty selector framework for linear models, which is known for its selection consistency properties. It features an efficient iterative algorithm easily implemented for any generalized linear model within the exponential family. Empirically, we demonstrate that IMUS performs well in simulations and on three microarray gene expression datasets, achieving effective covariate selection with smoother convergence and clearer elbow criteria compared to other error distribution free methods. Notably, simulation studies in logistic and Poisson regression showed that IMUS exhibited smoother convergence and clearer elbow criteria, performing comparably to the Generalized Matrix Uncertainty Selector (GMUS) and Generalized Matrix Uncertainty Lasso (GMUL) in covariate selection. In many scenarios, IMUS had smaller estimation errors than GMUL and GMUS, measured by both the 1- and 2-norms. In applications to three microarray datasets with noisy measurements, GMUS faced convergence issues, while GMUL converged but lacked well-defined elbows for two datasets. In contrast, IMUS converged with well-defined elbows for all datasets, providing a potentially effective solution for high dimensional regression problems involving measurement errors.

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.

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.

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.