1 citations · 1 across the 7 of their papers we have counts for
10 papers
From priors to performance: enhancing statistical efficiency with Bayesian dynamic borrowing
Xinxin Chen, Ezequiel Braga, Joseph G. Ibrahim +2
One of the main advantages of the Bayesian approach to statistical inference is the flexibility in incorporating information from various sources, from expert opinion to historical…
NP-LEAP: Nonparametric Latent Exchangeability Prior for Model-Lean Borrowing from Historical Data
Ethan M. Alt, Miheer Dewaskar, Jacob M. Maronge +2
Bayesian dynamic borrowing (BDB) methods leverage historical data to reduce treatment effect uncertainty, yet existing approaches rely on parametric outcome models susceptible to m…
Cure Rate Joint Model for Time-to-Event Data and Longitudinal Tumor Burden with Potential Change Points
Yixiang Qu, Ethan M. Alt, Weibin Zhong +3
In non-small cell lung cancer (NSCLC) clinical trials, tumor burden (TB) is a key longitudinal biomarker for assessing treatment effects. Typically, standard-of-care (SOC) therapie…
hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
Ethan M. Alt, Xinxin Chen, Luiz M. Carvalho +1
There has been increased interest in the use of historical data to formulate informative priors in regression models. While many such priors for incorporating historical data have…
Improved computational efficiency and stability when imputing censored covariates: Analytic and numerical approaches
Sarah C. Lotspeich, Ethan M. Alt
Imputation is a popular approach to handling censored, missing, and error-prone covariates -- all coarsened data types for which the true values are unknown. However, there are nua…
Jointly modeling time-to-event and longitudinal data with individual-specific change points: a case study in modeling tumor burden
Ethan M. Alt, Yixiang Qu, Emily Damone +3
In oncology clinical trials, tumor burden (TB) stands as a crucial longitudinal biomarker, reflecting the toll a tumor takes on a patient's prognosis. With certain treatments, the…
LEAP: The latent exchangeability prior for borrowing information from historical data
Ethan M. Alt, Xiuya Chang, Xun Jiang +4
It is becoming increasingly popular to elicit informative priors on the basis of historical data. Popular existing priors, including the power prior, commensurate prior, and robust…
A hierarchical prior for generalized linear models based on predictions for the mean response
Ethan M. Alt, Matthew A. Psioda, Joseph G. Ibrahim
There has been increased interest in using prior information in statistical analyses. For example, in rare diseases, it can be difficult to establish treatment efficacy based solel…
The scale transformed power prior for use with historical data from a different outcome model
Brady Nifong, Matthew A. Psioda, Joseph G. Ibrahim
We develop the scale transformed power prior for settings where historical and current data involve different data types, such as binary and continuous data, respectively. This sit…
Bayesian Multivariate Probability of Success Using Historical Data with Strict Control of Family-wise Error Rate
Ethan M. Alt, Matthew A. Psioda, Joseph G. Ibrahim
Given the cost and duration of phase III and phase IV clinical trials, the development of statistical methods for go/no-go decisions is vital. In this paper, we introduce a Bayesia…