5 citations · 8 across the 7 of their papers we have counts for
8 papers
Bayesian additive regression trees for evaluating treatment benefit predictors using observational data
Yuan Xia, Mohsen Sadatsafavi, Jeenat Mehareen +1
A treatment benefit predictor (TBP) is an algorithm that maps a patient's characteristics to their putative benefit from a given treatment, which can be used to inform treatment de…
Methods for adjusting for covariate measurement error in flexible modelling of functional form: results of a blinded, controlled neutral comparison simulation study
Mohammed Sedki, Aris Perperoglou, Anne C. M. Thiébaut +6
Covariate measurement error is pervasive in epidemiological research and distorts estimated exposure-outcome associations, yet correction methods have been studied almost exclusive…
Methods for adjusting for covariate measurement error in flexible modelling of functional form: designing a blinded, controlled neutral comparison simulation study
Anne C M Thiébaut, Aris Perperouglou, Mohammed Sedki +6
This article describes the design of a neutral comparison study in the context of empirical studies where the interest is in learning the functional relationship between a continuo…
Don't Disregard the Data for Lack of a Likelihood: Bayesian Synthetic Likelihood for Enhanced Multilevel Network Meta-Regression
Harlan Campbell, Charles C. Margossian, Jeroen P. Jansen +1
Multilevel network meta-regression (ML-NMR) enables population-adjusted indirect treatment comparisons by combining individual patient data (IPD) with aggregate data. When individu…
Bayesian sample size calculations for external validation studies of risk prediction models
Mohsen Sadatsafavi, Paul Gustafson, Solmaz Setayeshgar +2
Contemporary sample size calculations for external validation of risk prediction models require users to specify fixed values of assumed model performance metrics alongside target…
Expected value of sample information calculations for risk prediction model development
Abdollah Safari, Paul Gustafson, Mohsen Sadatsafavi
Risk prediction models are often advertised as deterministic functions that map covariates to predicted risks. However, they are typically trained using finite samples, and as such…
Evaluating Treatment Benefit Predictors using Observational Data: Contending with Identification and Confounding Bias
Yuan Xia, Mohsen Sadatsafavi, Paul Gustafson
A treatment benefit predictor (TBP) is a function that maps patient characteristics to an estimate of the treatment benefit for that patient. Such predictors support optimizing ind…
The expected value of sample information calculations for external validation of risk prediction models
Mohsen Sadatsafavi, Andrew J Vickers, Tae Yoon Lee +2
In designing external validation studies of clinical prediction models, contemporary sample size calculation methods are based on the frequentist inferential paradigm. One of the w…