JSTOR Daily

Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data

We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize ...
Booth School of Business

A Better Way for Finance (and Others) to Handle Missing Data

There are data about practically everything these days, and they can be used to try to answer any number of questions. Do clinical trials really show a drug works? Can surveys really signal who’s ...
JSTOR Daily

Handling Missing Data in Diaries of Alcohol Consumption

Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 163, No. 3 (2000), pp. 381-402 (22 pages) Missing data can rarely be avoided in large scale studies in which subjects ...
The American Journal of Managed Care

A New Framework for Handling Missing Data as RWD Sources Rise

Interview on Ontada research presented at ISPOR 2024. Increased variety of real-world data (RWD) sources can give scientists a richer picture than ever of what’s happening to patients. But for every ...
News Medical

Revolutionizing missing data management in EHRs with machine learning

Researchers from the National Institute of Health Data Science at Peking University and the Department of Clinical Epidemiology and Biostatistics at Peking University People's Hospital have conducted ...
Nature

Longitudinal Data Analysis and Bayesian Modeling

Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...