Search

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group‐sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group‐sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting. [ABSTRACT FROM AUTHOR]

A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular (CV) risk prediction is useful in prioritizing resources and guiding treatment decisions. Most risk prediction equations are constructed using data from cohort studies. However, these cohorts often suffer from lack of diversity or generalizability. Therefore, there is an increasing interest in using electronic health data (EHD) to construct CV risk models. Given the size and complexity of EHD, machine learning (ML) techniques are an appealing alternative to time-to-event regression. However, the nature of EHD is such that subjects have varying duration of follow-up and some event times may be right censored. However, many ML techniques do not accommodate censoring. Methods: We propose a simple and universal method that allows any ML technique to accommodate right-censored data by averaging predictions over a set of weighted bootstrap samples, where the sampling weights are computed using inverse probability of censoring weighting (IPCW). IPCW is a method that reweights the observed data to represent the underlying population by assigning more weight to observed subjects who were likely to be censored. After resampling from the fully observed data, we fit a model and produce predictions in each iteration. Lastly, we offset computational times by using an ensemble. We consider the problem of predicting 5-year CV risk using EHD from a large health system (N=87,348), where 50% of subjects are censored. We compare our method to other ways of handling censoring. Results: Our technique consistently improved calibration by about 70-90% than approaches which ignored censoring (see below). However, there were no differences in discrimination. In conclusion, we show that miscalibration due to censoring can affect real-world treatment decisions, as demonstrated using the 2013 ACC/AHA treatment recommendation for statins. In such a scenario, one does not aim to identify for example top 10% highest risk patients, but rather the actual magnitude of risk for a single patient. [ABSTRACT FROM AUTHOR]