1 citations · 1 across the 4 of their papers we have counts for
6 papers
Multiple imputation with missing data indicators
Lauren J Beesley, Irina Bondarenko, Michael R Elliott +3
Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multi…
Accounting for not-at-random missingness through imputation stacking
Lauren J Beesley, Jeremy M G Taylor
Not-at-random missingness presents a challenge in addressing missing data in many health research applications. In this paper, we propose a new approach to account for not-at-rando…
Using Multiple Imputation to Classify Potential Outcomes Subgroups
Yun Li, Irina Bondarenko, Michael R. Elliott +2
With medical tests becoming increasingly available, concerns about over-testing and over-treatment dramatically increase. Hence, it is important to understand the influence of test…
Analysis of time-to-event for observational studies: Guidance to the use of intensity models
Per Kragh Andersen, Maja Pohar Perme, Hans C van Houwelingen +6
This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models.…
A stacked approach for chained equations multiple imputation incorporating the substantive model
Lauren Beesley, Jeremy M G Taylor
Multiple imputation by chained equations (MICE) has emerged as a popular approach for handling missing data. A central challenge for applying MICE is determining how to incorporate…
Personalized Screening Intervals for Biomarkers using Joint Models for Longitudinal and Survival Data
Dimitris Rizopoulos, Jeremy M. G. Taylor, Joost van Rosmalen +2
Screening and surveillance are routinely used in medicine for early detection of disease and close monitoring of progression. Biomarkers are one of the primarily tools used for the…