Handling drop‐out in longitudinal studies

Hogan, Joseph W.; Roy, Jason; Korkontzelou, Christina

doi:10.1002/sim.1728

Cited by 404Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Handling drop‐out in longitudinal studies

Hogan, Joseph W., Roy, Jason, Korkontzelou, Christina·2004·Statistics in Medicine

DOI 10.1002/sim.1728

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Abstract

Drop‐out is a prevalent complication in the analysis of data from longitudinal studies, and remains an active area of research for statisticians and other quantitative methodologists. This tutorial is designed to synthesize and illustrate the broad array of techniques that are used to address outcome‐related drop‐out, with emphasis on regression‐based methods. We begin with a review of important assumptions underlying likelihood‐based and semi‐parametric models, followed by an overview of models and methods used to draw inferences from incomplete longitudinal data. The majority of the tutorial is devoted to detailed analysis of two studies with substantial rates of drop‐out, designed to illustrate the use of effective methods that are relatively easy to apply: in the first example, we use both semi‐parametric and fully parametric models to analyse repeated binary responses from a clinical trial of smoking cessation interventions; in the second, pattern mixture models are used to analyse longitudinal CD4 counts from an observational cohort study of HIV‐infected women. In each example, we describe exploratory analyses, model formulation, estimation methodology and interpretation of results. Analyses of incomplete data requires making unverifiable assumptions, and these are discussed in detail within the context of each application. Relevant SAS code is provided. Copyright © 2004 John Wiley & Sons, Ltd.

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LMM is robust to missing data and utilizes all available data for each participant as the data are suggested to be missing at random.
“LMM is robust to missing data and utilizes all available data for each participant as the data are suggested to missing at random [48], so that those participants who had missing answers at some time-point were not dropped out.”
From Work Ability and Functioning Among Individuals with Persistent Indoor Environment-Related Symptoms or Chronic Fatigue: Nine-Month Follow-Up of a Web-Based Randomized Controlled Trial. (2026) · cites this paper as [48]

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SUMMARY
Hogan, J. W., Roy, J., & Korkontzelou, C. (2016)
Self-citation
Comparative review of methods for handling drop‐out in longitudinal studies
Philipson, P., Ho, W.-K., & Henderson, R. (2008) · Statistics in Medicine · doi:10.1002/sim.3450 · Cited by 24 · see citing works
Bayesian semi-parametric methods for non-ignorable dropout
Moore, C. M. (2016) · doi:10.25677/923p-1886
The Practical Use of Different Strategies to Handle Dropout in Longitudinal Studies
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Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies
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Authors: Hogan, J. W., Roy, J., & Korkontzelou, C.
Affiliations: Brown University (US)
Corresponding author: Hogan, Joseph W.
ORCID: Hogan, Joseph W.
Roy, Jason
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2004-04-06
Volume: 23
Issue: 9
Pages: 1455-1497
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.1728
OpenAlex: W2154534198
Cited by: 404
References: 111
Citations by year: 2026: 11 · 2025: 8 · 2024: 10 · 2023: 6 · 2022: 12 · 2021: 7 · 2020: 13 · 2019: 16 · 2018: 12 · 2017: 32 · 2016: 23 · 2015: 31 · 2014: 19 · 2013: 27 · 2012: 19
FWCI: 3.43
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Statistics in Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: Centers for Disease Control and Prevention (U64/CCU506831, U64\CCU306802, U64\CCU106795, U64\CCU206798)
National Cancer Institute (R01CA077249, R01-CA77249)
National Institute of Allergy and Infectious Diseases (P30-AI 42853, R01-AI50505, R01AI050505, P30AI042853)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: dropout, longitudinal data, missing data, pattern-mixture models, semiparametric models, likelihood-based models, smoking cessation interventions, women living with HIV, CD4 count
MeSH terms: Biometry, Female, Humans, Longitudinal Studies, Patient Dropouts, Regression Analysis, Models, Statistical, HIV Infections, Smoking Cessation

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Handling drop‐out in longitudinal studies

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