Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.098 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Missing data are a frequently encountered problem in epidemiologic and clinical research.[1][1],[2][2] One approach is to include in the analysis only those participants without missing observations (complete or available case analysis).[1][1]–[4][3] However, in addition to reducing statistical
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Canadian Medical Association Journal
- Topic
- Statistical Methods and Bayesian Inference
- Field
- Mathematics
- Canadian institutions
- —
- Funders
- Economic and Social Research CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekCancer Research UK
- Keywords
- Missing dataCovariateComputer scienceData miningData scienceStatisticsMachine learningMathematics
- Has abstract in OpenAlex
- yes