ArticleClinical epidemiology2015
Using multiple imputation to deal with missing data and attrition in longitudinal studies with repeated measures of patient-reported outcomes.
Article in Clinical epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed.
- Exploring the role of gender on treatment outcomes in older adults with alcohol use disorder.Alcohol, clinical & experimental research · 2025Trial
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- Longitudinal Impacts of Gastric Bypass Surgery on Pharmacodynamics and Pharmacokinetics of Statins.Obesity surgery · 2019Trial
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- Web-Delivered Cognitive Behavioral Therapy for Distressed Cancer Patients: Randomized Controlled Trial.Journal of medical Internet research · 2018Trial
- Men's and women's response to treatment and perceptions of outcomes in a randomized controlled trial of injectable opioid assisted treatment for severe opioid use disorder.Substance abuse treatment, prevention, and policy · 2017Trial
- Tracking of dietary patterns from early childhood to school age in the Brazilian Food and Nutritional Surveillance System, 2008-2019.Public health nutrition · 2026Article
- How to Analyze Longitudinal Patient-Reported Outcomes in Populations With High Mortality Rates.Journal of the American Geriatrics Society · 2026Article
- A Framework for Locally Imputing and Predicting Biomarker Trajectories Under Irregular Monitoring: Application to Chronic Myeloid Leukemia.Research square · 2026Article
- Attrition and representativeness in development and validation of online symptom checkers-a case study on theFrontiers in artificial intelligence · 2026Article
- Effects of Dapagliflozin on Health-Related Quality of Life in Patients with CKD.Journal of the American Society of Nephrology : JASN · 2026Article
- A Real-World Retrospective Cohort Study Evaluating Clinical Effects and Safety of Radiofrequency Microneedling for the Treatment of Rosacea.Clinical, cosmetic and investigational dermatology · 2026Article
- Predictors of independence in older people: A longitudinal, population-based study using the CARE75 + cohort.BMC geriatrics · 2025Article
- An Oversampling-Enhanced Multi-Class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-Reported Outcomes.IEEE access : practical innovations, open solutions · 2025Article
- Longitudinal Associations Between Depression Symptoms and Cognitive Functions in Chinese Older Adults: A Cross-Lagged Panel Network Analysis.Depression and anxiety · 2025Article
- To Identify and Compare ActivPAL Objectively Measured Activity Levels With Self-Reported Activity From the International Physical Activity Questionnaire in Older Adults With Intellectual Disability.Journal of applied research in intellectual disabilities : JARID · 2025Article
- Eating Problems Among Adolescent Boys and Girls Before and During the Covid-19 Pandemic.The International journal of eating disorders · 2025Article
- AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Review
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMissing data is a ubiquitous problem in studies using patient-reported measures, decreasing sample sizes and causing possible bias. In longitudinal studies, special problems relate to attrition and death during follow-up. We describe a methodological approach for the use of multiple imputation (MI) to meet these challenges.
methodsIn a cohort of patients treated with percutaneous coronary intervention followed with use of repetitive questionnaires and information from national registers over 3 years, only 417 out of 1,726 patients had complete data on all measure points and covariates. We suggest strategies for use of MI and different methods for dealing with death along with sensitivity analysis of deviations from the assumption of missing at random, all with the use of standard statistical software. The Mental Component Summary from Short Form 12-item survey was used as an example.
conclusionIgnoring missing data may cause bias of unknown size and direction in longitudinal studies. We have illustrated that MI is a feasible method to try to deal with bias due to missing data in longitudinal studies, including attrition and nonresponse, and should be considered in combination with analysis of sensitivity in longitudinal studies. How to handle dropout due to death is still open for debate.
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