Statistical Methods in Medical Research最新文献

Estimating causal effects in the presence of spillover among individuals within a social network poses challenges due to missing information. Spillover effects refer to the impact of an intervention on individuals not directly exposed themselves but connected to intervention recipients within the network. In network-based studies, outcomes may be missing due to study termination or participant dropout, termed censoring. We introduce an inverse probability censoring weighted estimator which extends the inverse probability weighted estimator for network-based observational studies to handle possible outcome censoring. We prove the consistency and asymptotic normality of the proposed estimator and derive a closed-form estimator for its asymptotic variance. Applying the inverse probability censoring weighted estimator, we assess spillover effects in a network-based study of a nonrandomized intervention with outcome censoring. A simulation study evaluates the finite-sample performance of the inverse probability censoring weighted estimator, demonstrating its effectiveness with sufficiently large sample sizes and number of connected subnetworks. We then employ the method to assess spillover effects of community alerts on self-reported human immunodeficiency virus risk behavior among people who inject drugs and their contacts in the Transmission Reduction Intervention Project (TRIP), from 2013 to 2015, Athens, Greece. Results suggest that community alerts may help reduce human immunodeficiency virus risk behavior for both the individuals who receive them and others in their network, possibly through shared information. In this study, we found that the risk of human immunodeficiency virus behavior was reduced by increasing the proportion of a participant's immediate contacts exposed to community alerts.

Selection bias is a common type of bias, and depending on the causal estimand of interest and the structure of the selection variable, it can be a threat to both external and internal validity. One way to quantify the maximum magnitude of potential selection bias is to calculate bounds for the causal estimand. Here, we consider previously proposed bounds for selection bias, which require the specification of certain sensitivity parameters. First, we show that the sensitivity parameters are variation independent. Second, we show that the bounds are sharp under certain conditions. Furthermore, we derive improved bounds that are based on the same sensitivity parameters. Depending on the causal estimand, these bounds require additional information regarding the selection probabilities. We illustrate the improved bounds in an empirical example where the effect of breakfast eating on overweight is estimated. Lastly, the performance of the bounds are investigated in a numerical experiment for sharp and non-sharp cases.

Mechanistic interaction concerns how exposures affect the outcome. When investigating mechanisms, synergism is the most mentioned type in the fields of genetic study and pharmacology. Synergism is defined under the framework of sufficient component cause model, which is difficult to be quantified directly. Sufficient cause interaction (SCI) is the only alternative metric to imply the existence of synergism. VanderWeele and Robins provided empirical tests for SCIs. However, this test only assesses the lower bound of SCIs rather than estimate SCIs directly due to the lack of the degree of freedom, which causes low power. To address this issue, in this study, we propose a novel method to estimate the probability of individual with SCI by introducing a new factor named quasi-instrumental variable, which is necessary for the background condition of SCI. We also develop a corresponding hypothesis test and show that it is more powerful than the existing empirical test. We demonstrate this method by applying it to estimate the synergistic effects between intestinal bacteria on the formation of Parkinson's disease.

In multicenter randomized trials, when effect modifiers have a different distribution across centers, comparisons between treatment groups that average (standardize) effects over centers may not apply to any of the populations underlying the individual centers. In the presence of such heterogeneity, interpreting the evidence produced by a multicenter trial in the context of the local population underlying each center may be necessary. Here, we identify center-specific effects under conditions that are largely supported by the study design and are weaker than those underlying popular methods for the analysis of multicenter studies, in the presence of associations between center membership and the outcome ("center-outcome associations" conditional on baseline covariates and treatment). We then consider an additional testable condition of "no center-outcome associations," given baseline covariates and treatment. We propose methods for estimating center-specific average treatment effects, when center-outcome associations are present and when they are absent. When center-outcome associations are absent, we discuss how the proposed methods are often more efficient and make weaker conditions than related transportability methods applied to multicenter trials. We evaluate the performance of the methods in a simulation study and illustrate their implementation using data from the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis trial.

Randomized controlled trials in oncology often allow control group participants to switch to experimental treatments, a practice that, while often ethically necessary, complicates the accurate estimation of long-term treatment effects. When switching rates are high or sample sizes are limited, commonly used methods for treatment switching adjustment (such as the rank-preserving structural failure time model, inverse probability of censoring weights, and two-stage estimation) may produce imprecise estimates. Real-world data can be used to develop an external control arm for the randomized controlled trial, although this approach ignores evidence from trial subjects who did not switch and ignores evidence from the data obtained prior to switching for those subjects who did. This article introduces "augmented two-stage estimation" (ATSE), a method that combines data from non-switching participants in a randomized controlled trial with an external dataset, forming a "hybrid non-switching arm". While aiming for more precise estimation, the augmented two-stage estimation requires strong assumptions. Namely, conditional on all the observed covariates: (1) a participant's decision to switch treatments must be independent of their post-progression survival, and (2) individuals from the randomized controlled trial and the external cohort must be exchangeable. With a simulation study, we evaluate the augmented two-stage estimation method's performance compared to two-stage estimation adjustment and an external control arm approach. Results indicate that performance is dependent on scenario characteristics, but when unconfounded external data are available, augmented two-stage estimation may result in less bias and improved precision compared to two-stage estimation and external control arm approaches. When external data are affected by unmeasured confounding, augmented two-stage estimation becomes prone to bias, but to a lesser extent compared to an external control arm approach.

Effect sizes typically vary among studies of the same intervention. In a random effects meta-analysis, this source of variation is taken into account, at least to some extent. However, when we have only one study, the heterogeneity remains hidden and unaccounted for. Treating the study-level effect as if it is the population-level effect leads to underestimation of the uncertainty. We propose an empirical Bayesian approach to address this problem. We start by estimating the distribution of the population-level effects and heterogeneity among 1635 meta-analyses from the Cochrane Database of Systematic Reviews. Using both synthetic data and cross-validation, we assess the consequences of using these estimated distributions as prior information for the analysis of single trials. We find that our Bayesian "meta-analyses of single studies" perform much better than naively assuming non-varying effects. The prior on the heterogeneity results in better quantification of the uncertainty. The prior on the treatment effect substantially reduces the mean squared error both for estimating the study-level and population-level effects. For the latter, this reduction is equivalent to doubling the sample size.

The identification of biomarkers for disease onset in longitudinal studies necessitates precise estimation of the association between longitudinal markers and survival outcomes. Currently, methods for estimating these associations in the context of left-truncated and clustered survival outcomes are lacking. In this study, we propose a novel model tailored to this scenario and develop several estimation methods: last observation carried forward, regression calibration, and a two-stage likelihood approach for joint modeling of longitudinal and survival processes. Simulation results indicate that the last observation carried forward method performs well only with a dense grid and no marker measurement error. For less dense grids and low measurement error, regression calibration approaches are preferred. Joint modeling approaches outperform calibration methods in the presence of measurement error, although they may suffer from numerical instability. In cases of numerical instability, calibration methods might be a good alternative. We applied these methodologies to the TwinsUK data to estimate the effect of bone mineral density (BMD) as a longitudinal marker on fracture incidence in 766 elderly females, 138 of whom experienced a fracture. The survival model included a shared gamma-distributed frailty to account for correlation between the times to fracture of twin pairs. Estimates obtained using calibration and joint modeling approaches indicated a larger BMD effect compared to the last observation carried forward method, likely due to the irregular BMD measurement process and minimal measurement error. Overall, our methods offer valuable tools for modeling the effect of a longitudinal marker on survival outcomes in complex designs.

Binary endpoints are used widely to evaluate treatment effects during clinical trials. Although clinical trials in many therapeutic areas evaluate a single binary endpoint as the primary endpoint, clinical trials in certain therapeutic areas require two co-primary binary endpoints to evaluate treatment benefit multi-dimensionally. We consider the situation in which evidence of effects on both co-primary endpoints is necessary to conclude that the intervention is effective, which differs from approaches by which significance on at least one endpoint is sufficient for trial success. When designing clinical trials with two co-primary binary endpoints, consideration of correlation between the endpoints can increase trial power and consequently reduce the required sample size, leading to improved efficiency. For clinical trials with two co-primary binary endpoints, methods for calculating power and sample size have been proposed, but they are based on approximations or require Monte Carlo integration. Alternatively, we propose methods for calculating the exact power and sample size in clinical trials with two co-primary binary endpoints. The proposed methods are useful for any statistical test for binary endpoints. Numerical investigation under various scenarios demonstrated that our proposed methods can incorporate consideration of the correlation between two co-primary binary endpoints in sample size calculation, thereby allowing the required sample size to be reduced. We demonstrate that the exact power for the required sample size calculated using our proposed method is approximately equal to target power. Finally, we present application of our proposed methods to a clinical trial of relapsing or refractory eosinophilic granulomatosis with polyangiitis.

This study describes and compares the performance of several semi-parametric statistical modeling approaches to dynamically classify subjects into two groups, based on an irregularly and sparsely sampled curve. The motivating example of this study is the diagnosis of a complication following cardiac surgery, based on repeated measures of a single cardiac biomarker where early detection enables prompt intervention by clinicians. We first simulate data to compare the dynamic predictive performance over time for growth charts, conditional growth charts, a varying-coefficient model, a generalized functional linear model and longitudinal discriminant analysis. Our results demonstrate that functional regression approaches that implicitly incorporate historic information through random effects, provide superior discriminative ability compared to approaches that do not take historic information into account or explicitly model historic information through autoregressive terms. Semi-parametric modeling approaches show a benefit in terms of dynamic discriminative ability compared to the clinical practice of using a fixed threshold on the raw measured value. Under high degrees of sparsity the functional regression approaches are less advantageous compared to varying-coefficient models or quantile regression. The class imbalance of the outcome affects the historic and non-historic approaches in equal measure, with lower event rates reducing performance. Finally, the functional regression and varying-coefficient model were applied to a real-world clinical dataset to demonstrate their performance and application.

In clinical trials, it is often of interest to know whether treatment works differently for some groups than others, known as heterogeneity of treatment effect. Such subgroup analysis is complicated to conduct because trials are typically not powered to find subgroups. Furthermore, it is difficult to identify characteristics of patients pertaining to such subgroups. In this article, we propose a semiparametric mixture model to identify subgroups with time-to-event outcomes. Specifically, we assume a proportional hazards model with subgroup-specific piecewise constant baseline hazards, where the subgroup-specific treatment effect is assumed to be the same within each subgroup. The probability of belonging to a certain subgroup is a function of patient prognostic factors. Adopting a Bayesian approach, classification uncertainty is taken into account. We demonstrate the utility of our approach via simulation and an application to data from a real clinical trial in HIV research.