SynthesisBMC medical research methodology2017
Longitudinal studies that use data collected as part of usual care risk reporting biased results: a systematic review.
Synthesis in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Omitting patients with no follow-up leads to bias when using inverse-intensity weighted GEEs to handle irregular and informative assessment times.BMC medical research methodology · 2025Article
- Timing of repeat epinephrine to inform paediatric anaphylaxis observation periods: a retrospective cohort study.The Lancet. Child & adolescent health · 2025Article
- Randomized Trials With Repeatedly Measured Outcomes: Handling Irregular and Potentially Informative Assessment Times.Epidemiologic reviews · 2022Review
- Understanding International Variations in Kidney Failure Incidence and Initiation of Replacement Therapy.Kidney international reports · 2022Article
- Summarizing the extent of visit irregularity in longitudinal data.BMC medical research methodology · 2020Article
- Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.Statistica Neerlandica · 2020Article
- How and when informative visit processes can bias inference when using electronic health records data for clinical research.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Visceral Origin: An Underestimated Source of Neck Pain. A Systematic Scoping Review.Diagnostics (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLongitudinal studies using data collected as part of usual care risk providing biased results if visit times are related to the outcome of interest. Statistical methods for mitigating this bias are available but rarely used. This lack of use could be attributed to a lack of need or to a lack of awareness of the issue.
methodsWe performed a systematic review of longitudinal studies that used data collected as part of patients' usual care and were published in MEDLINE or EMBASE databases between January 2005 through May 13
resultsOf 44 eligible articles, 57% (n = 25) reported on the total follow-up time, 7% (n = 3) on the gaps between visits, and 57% (n = 25) on the number of visits per patient; 78% (n = 34) reported on at least one of these. Two studies assessed predictors of visit times, and 86% of studies did not report enough information to assess whether there was a need to account for informative follow-up. Only one study used a method designed to account for informative visit times.
conclusionsThe low proportion of studies reporting on whether there were important predictors of visit times suggests that researchers are unaware of the potential for bias when data is collected as part of usual care and visit times are irregular. Guidance on the potential for bias and on the reporting of longitudinal studies subject to irregular follow-up is needed.
Indexed as
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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.