ArticleThe Permanente journal2020
On the Use of Sampling Weights for Retrospective Medical Record Reviews.
Article in The Permanente journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- A pragmatic randomized trial to compare strategies for implementing primary HPV testing for routine cervical cancer screening in a large healthcare system.Implementation science : IS · 2025Trial
- Adult children migration and perceived discrimination among Indian older adults: evidence from longitudinal ageing study in India.BMC public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retrospective medical record review is often used to answer the "why" questions that statistical modeling cannot. In addition to its utility as an explanatory tool, it can be used to generate hypotheses using available retrospective data and so is a convenient guide for developing future prospective studies. A recent review of articles that used the retrospective medical record review method listed 10 best practices that ought to be followed. However, an issue that is not listed is the use of sampling weights, which are important when one can only conduct retrospective medical record review for a sample of the target population. Although that review acknowledged the importance of carefully selecting a sampling strategy for such a scenario and indeed had outlined 3 commonly used sampling methods (convenience, simple random, and systematic), the authors say nothing of the use of sampling information at the data analysis stage. This article aims to fill that gap and to demonstrate why the use of sample weights ought to be another best practice to add to the list by reviewing well-known theoretical details and some published data analysis examples.
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Registered trials
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