ArticleStatistical methods in medical research2017
Accounting for dropout reason in longitudinal studies with nonignorable dropout.
Article in Statistical methods in medical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Refining the Parkinson's Disease Risk Estimator for Decline In Cognition Tool (PREDICT).Cognitive and behavioral neurology : official journal of the Society for Behavioral and Cognitive Neurology · 2026Article
- Statistical Methods for Binary Outcomes Adjusting for Outcome Dependent Sampling in Longitudinal Studies with Nonignorable Dropout.bioRxiv : the preprint server for biology · 2025Article
- Sample size matters: A step-by-step guide for radiologists.Journal of clinical imaging science · 2025Review
- Sample size determination: A practical guide for health researchers.Journal of general and family medicine · 2023Review
- The role of individual differences in attentional blink phenomenon and real-time-strategy game proficiency.Heliyon · 2021Article
- Psychophysiological, but Not Behavioral, Indicator of Working Memory Capacity Predicts Video Game Proficiency.Frontiers in human neuroscience · 2021Article
- A Bayesian natural cubic B-spline varying coefficient method for non-ignorable dropout.BMC medical research methodology · 2020Review
- Longitudinal change in physical functioning and dropout due to death among the oldest old: a comparison of three methods of analysis.European journal of ageing · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Dropout is a common problem in longitudinal cohort studies and clinical trials, often raising concerns of nonignorable dropout. Selection, frailty, and mixture models have been proposed to account for potentially nonignorable missingness by relating the longitudinal outcome to time of dropout. In addition, many longitudinal studies encounter multiple types of missing data or reasons for dropout, such as loss to follow-up, disease progression, treatment modifications and death. When clinically distinct dropout reasons are present, it may be preferable to control for both dropout reason and time to gain additional clinical insights. This may be especially interesting when the dropout reason and dropout times differ by the primary exposure variable. We extend a semi-parametric varying-coefficient method for nonignorable dropout to accommodate dropout reason. We apply our method to untreated HIV-infected subjects recruited to the Acute Infection and Early Disease Research Program HIV cohort and compare longitudinal CD4
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.