Statistical Methods in Medical Research最新文献

We study spline-based efficient estimation of frailty models for panel count data using a penalization technique. An easy-to-implement and computationally efficient two-stage iterative expectation-maximization algorithm is proposed for the analysis. A general quasi-likelihood estimation that does not specify the stochastic model of the underlying counting process is developed to provide flexibility for model fitting. A powerful score test is discussed to detect the presence of overdispersion in count data. The proposed methods are assessed via an extensive simulation and further illustrated by analyzing data from a non-melanoma skin cancer chemoprevention study.

In several clinical areas, traditional clinical trials often use a responder outcome, a composite endpoint that involves dichotomising a continuous measure. An augmented binary method that improves power while retaining the original responder endpoint has previously been proposed. The method leverages information from the undichotomised component to improve power. We extend this method for basket trials, which are gaining popularity in many clinical areas. For clinical areas where response outcomes are used, we propose the new augmented binary method for basket trials that enhances efficiency by borrowing information on the treatment effect between subtrials. The method is developed within a latent variable framework using a Bayesian hierarchical modelling approach. We investigate the properties of the proposed methodology by analysing point estimates and high-density intervals in various simulation scenarios, comparing them to the standard analysis for basket trials that assumes binary outcomes. Our method results in a reduction of 95% high-density interval of the posterior distribution of the log odds ratio and an increase in power when the treatment effect is consistent across subtrials. We illustrate our approach using real data from two clinical trials in rheumatology.

A parallel randomized trial is frequently used to investigate the treatment effectiveness as compared to the gold standard. In early phase trials, a group sequential design has the potential to reduce the expected sample size as compared to the traditional one-stage design, and protect participants when a new treatment is not as effective as expected. When the outcome is binary, a group sequential design based on exact binomial distribution is preferable as compared to the asymptotic limiting distribution. To improve the design efficiency, we propose to develop new parallel two-stage adaptive design and promising zone design allowing sample size adjustment in the second stage based on the outcome from the first stage. The conditional probability is guaranteed in the proposed designs when a trial proceeds to the second stage. All these designs control the type I error rate, but only the proposed two designs guarantee the conditional probability constraint. We used a real example from a completed cancer trial to illustrate the application of the proposed designs. The adaptive design substantially increases unconditional power but requires a large sample size as compared to the group sequential design. The promising zone design achieves a good balance between statistical power and the expected sample size.

Clinical prediction models are developed to estimate a patient's risk for a specific outcome, and machine learning is frequently employed to improve prediction accuracy. When the outcome is some event that happens over time, binary classifiers can predict the risk at specific time points if right-censoring is addressed by inverse-probability-of-censoring-weighting . Assessing prediction uncertainty is crucial for interpreting individual risks, but there is limited knowledge on how to consider inverse-probability-of-censoring-weighting when estimating this uncertainty. We propose an adjustment of the infinitesimal jackknife estimator for the standard error of predictions that incorporates inverse-probability-of-censoring-weighting. By using a nonparametric approach, it is broadly applicable, especially to machine learning classifiers. For a simple tractable example, we show that the proposed adjustment reveals unbiased standard error estimates. For other situations, we evaluate performance through simulation studies under both parametric models with inverse-probability-of-censoring-weighting-customized log-likelihood and machine learning with inverse-probability-of-censoring-weighting-customized loss function. We illustrate the methods by predicting post-transplant survival probabilities, using national kidney transplant registry data. Our findings show that the proposed estimator is useful for quantifying prediction uncertainty of inverse-probability-of-censoring-weighting classifiers. Applications to simulated and real data show that prediction uncertainty increases when employing binary classifiers on dichotomized data compared to predictions from survival models.

Individualized treatment rules leverage patient-level information to tailor treatments for individuals. Estimating these rules, with the goal of optimizing expected patient outcomes, typically relies on individual-level data to identify the variability in treatment effects across patient subgroups defined by different covariate combinations. To increase the statistical power for detecting treatment-covariate interactions and the generalizability of the findings, data from multisite studies are often used. However, sharing sensitive patient-level health data is sometimes restricted. Additionally, due to funding or time constraints, only a subset of available treatments can be included at each site, but an individualized treatment rule considering all treatments is desired. In this work, we adopt a two-stage Bayesian network meta-analysis approach to estimate individualized treatment rules for multiple treatments using multisite data without disclosing individual-level data beyond the sites. Simulation results demonstrate that our approach can provide consistent estimates of the parameters that fully characterize the optimal individualized treatment rule. We illustrate the method's application through an analysis of data from the Sequenced Treatment Alternatives to Relieve Depression study, the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care study, and the Research Evaluating the Value of Augmenting Medication with Psychotherapy study.

This paper delves into the realm of ordinal classification processes for multiple raters. A Probit hierarchical model is proposed linking rater's ordinal ratings with rater diagnostic skills (bias and magnifier) and patient latent disease severity, where patient latent disease severity is assumed to follow a latent class normal mixture distribution. This model specification provides closed-form expressions for both overall and individual rater receiver operator characteristic (ROC) curves and the area under these ROC curves (AUC). We further extend the model by incorporating covariate information and adding a regression layer for rater diagnostic skill parameters and/or for patient latent disease severity. The extended covariate models also offer closed-form solutions for covariate-specific ROCs and AUCs. These analytical tools greatly facilitate traditional diagnostic accuracy analysis. We demonstrate our methods thoroughly with a practical mammography example.

Survival analysis is a vital field in statistics with widespread applications. The short-term and long-term hazard ratio model is a novel semiparametric framework designed to handle crossing survival curves, encompassing the proportional hazards and proportional odds models as special cases. In this paper, we extend the short-term and long-term hazard ratio model to accommodate interval-censored and truncated data with covariates. The identifiability challenges arising from truncation are also discussed. We first prove that the nonparametric maximum likelihood estimation of the baseline survival function retains piecewise constant. Then an efficient iterative convex minorant algorithm, enhanced with a half-stepping strategy, is developed for computation. Additionally, we present a straightforward Wald test for hypothesis testing under a simplified yet commonly encountered practical scenario. Extensive simulation studies under diverse censoring and truncation scenarios demonstrate the robustness and accuracy in estimation of the proposed approach, particularly when traditional proportional hazards or proportional odds assumptions are violated. Applications to three real-world datasets further demonstrate the model's ability to capture varying covariate effects on survival probabilities across early and late stages, offering valuable insights for clinical practice and decision-making.

The Binary Emax model is widely employed in dose-response analysis during drug development, where missing data often pose significant challenges. Addressing nonignorable missing binary responses-where the likelihood of missing data is related to unobserved outcomes-is particularly important, yet existing methods often lead to biased estimates. This issue is compounded when using the regulatory-recommended ''imputing as treatment failure'' approach, known as non-responder imputation (NRI). Moreover, the problem of separation, where a predictor perfectly distinguishes between outcome classes, can further complicate likelihood maximization. In this paper, we introduce a penalized likelihood-based method that integrates a modified expectation-maximization (EM) algorithm in the spirit of Ibrahim and Lipsitz to effectively manage both nonignorable missing data and separation issues. Our approach applies a noninformative Jeffreys' prior to the likelihood, reducing bias in parameter estimation. Simulation studies demonstrate that our method outperforms existing methods, such as NRI, and the superiority is further supported by its application to data from a Phase II clinical trial. Additionally, we have developed an R package, ememax (https://github.com/Celaeno1017/ememax), to facilitate the implementation of the proposed method.

Predicting the risk of death for chronic patients is highly valuable for informed medical decision-making. This paper proposes a general framework for dynamic prediction of the risk of death of a patient given her hospitalization history. Predictions are based on a joint model for the death and hospitalization processes, thereby avoiding the potential bias arising from selection of survivors. The framework is valid for arbitrary models for the hospitalization process-it does not require independence of hospitalization times nor gap times. In particular, we study the prediction of the risk of death in a renewal model for hospitalizations-a common approach to recurrent event modeling. In the renewal model, the distribution of hospitalizations throughout the follow-up period impacts the risk of death. This result differs from the prediction of death when considering the Poisson model for the hospitalization process, previously studied, where only the number of hospitalizations matters. We apply our methodology to a prospective, observational cohort study of 512 patients treated for chronic obstructive pulmonary disease in one of six outpatient respiratory clinics run by the Respiratory Service of Galdakao University Hospital, with a median follow-up of 4.7 years. We find that more concentrated hospitalizations increase the risk of death and that the hazard ratio for death continuously increases as the number of hospitalizations increases during follow-up.

Causal inference on populations embedded in social networks poses technical challenges, since the typical no-interference assumption frequently does not hold. Existing methods developed in the context of network interference rely upon the assumption of no unmeasured confounding. However, when faced with multilevel network data, there may be a latent factor influencing both the exposure and the outcome at the cluster level. We propose a Bayesian inference approach that combines a joint mixed-effects model for the outcome and the exposure with direct standardisation to identify and estimate causal effects in the presence of network interference and unmeasured cluster confounding. In simulations, we compare our proposed method with linear mixed and fixed effects models and show that unbiased estimation is achieved using the joint model. Having derived valid tools for estimation, we examine the effect of home environment on adolescent school performance using data from the National Longitudinal Study of Adolescent Health.