Biometrical Journal最新文献

In modern clinical trials, there is immense pressure to use surrogate markers in place of an expensive or long-term primary outcome to make more timely decisions about treatment effectiveness. However, using a surrogate marker to test for a treatment effect can be difficult and controversial. Existing methods tend to either rely on fully parametric methods where strict assumptions are made about the relationship between the surrogate and the outcome, or assume the surrogate marker is valid for the entire study population. In this paper, we develop a fully nonparametric method for efficient testing using surrogate information (ETSI). Our approach is specifically designed for settings where there is heterogeneity in the utility of the surrogate marker, that is, the surrogate is valid for certain patient subgroups and not others. ETSI enables treatment effect estimation and hypothesis testing via kernel-based estimation for a setting where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully only measured in the remaining patients. In addition, we provide a framework for future study design with power and sample size estimates based on our proposed testing procedure. Throughout, we assume a continuous surrogate and a primary outcome that may be discrete or continuous. We demonstrate the performance of our methods via a simulation study and application to two distinct HIV clinical trials.

In observational studies, the propensity score plays a central role in estimating causal effects of interest. The inverse probability weighting (IPW) estimator is commonly used for this purpose. However, if the propensity score model is misspecified, the IPW estimator may produce biased estimates of causal effects. Previous studies have proposed some robust propensity score estimation procedures. However, these methods require considering parameters that dominate the uncertainty of sampling and treatment allocation. This study proposes a novel Bayesian estimating procedure that necessitates probabilistically deciding the parameter, rather than deterministically. Since the IPW estimator and propensity score estimator can be derived as solutions to certain loss functions, the general Bayesian paradigm, which does not require considering the full likelihood, can be applied. Therefore, our proposed method only requires the same level of assumptions as ordinary causal inference contexts. The proposed Bayesian method demonstrates equal or superior results compared to some previous methods in simulation experiments and is also applied to real data, namely the Whitehall dataset.

In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxing this assumption and conducting a sensitivity analysis, we introduce novel bounds and derive confidence intervals for the Average Potential Outcome (APO)—a standard metric for evaluating continuous-valued treatment or exposure effects. We demonstrate that these bounds are sharp under a continuous sensitivity model, in the sense that they give the smallest possible interval under this model, and propose a doubly robust version of our estimators. In a comparative analysis with another method from the literature, using both simulated and real data sets, we show that our approach not only yields sharper bounds but also achieves good coverage of the true APO, with significantly reduced computation times.

The problem of measurement error and misclassification in covariates is ubiquitous in nutritional epidemiology and some other research areas, which often leads to biased estimate and loss of power. However, addressing both measurement error and misclassification simultaneously in a single analysis is challenged and less actively studied, especially in regression model for survival data with censoring. The approximate maximum likelihood estimation (AMLE) has been proved to be an effective method to correct both measurement error and misclassification simultaneously in a logistic regression model. However, its impact on survival analysis models has not been studied. In this paper, we study biases caused by both measurement error and misclassification in covariates from a Weibull accelerated failure time model, and explore the use of AMLE and its asymptotic properties to correct these biases. Extensive simulation studies are conducted to evaluate the finite-sample performance of the resulting estimator. The proposed method is then applied to deal with measurement error and misclassification in some nutrients of interest from the EPIC-InterAct Study.

Upholding data privacy, especially in medical research, has become tantamount to facing difficulties in accessing individual-level patient data. Estimating mixed effects binary logistic regression models involving data from multiple data providers, like hospitals, thus becomes more challenging. Federated learning has emerged as an option to preserve the privacy of individual observations while still estimating a global model that can be interpreted on the individual level, but it usually involves iterative communication between the data providers and the data analyst. In this paper, we present a strategy to estimate a mixed effects binary logistic regression model that requires data providers to share summary statistics only once. It involves generating pseudo-data whose summary statistics match those of the actual data and using these in the model estimation process instead of the actual unavailable data. Our strategy is able to include multiple predictors, which can be a combination of continuous and categorical variables. Through simulation, we show that our approach estimates the true model at least as good as the one that requires the pooled individual observations. An illustrative example using real data is provided. Unlike typical federated learning algorithms, our approach eliminates infrastructure requirements and security issues while being communication efficient and while accounting for heterogeneity.