25 citations · 32 across the 8 of their papers we have counts for
6 papers · 1 filter
Addressing delayed case reporting in infectious disease forecast modeling
Lauren J Beesley, Dave Osthus, Sara Y Del Valle
Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future an…
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…
Patient Recruitment Using Electronic Health Records Under Selection Bias: a Two-phase Sampling Framework
Guanghao Zhang, Lauren J. Beesley, Bhramar Mukherjee +1
Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort fr…
Variable selection with multiply-imputed datasets: choosing between stacked and grouped methods
Jiacong Du, Jonathan Boss, Peisong Han +5
Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is…
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…