Reflection on modern methods: planned missing data designs for epidemiological research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Taking advantage of the ability of modern missing data treatments in epidemiological research (e.g. multiple imputation) to recover power while avoiding bias in the presence of data that is missing completely at random, planned missing data designs allow researchers to deliberately incorporate missing data into a research design. A planned missing data design may be done by randomly assigning participants to have missing items in a questionnaire (multiform design) or missing occasions of measurement in a longitudinal study (wave-missing design), or by administering an expensive gold-standard measure to a random subset of participants while the whole sample is administered a cheaper measure (two-method design). Although not common in epidemiology, these designs have been recommended for decades by methodologists for their benefits-notably that data collection costs are minimized and participant burden is reduced, which can increase validity. This paper describes the multiform, wave-missing and two-method designs, including their benefits, their impact on bias and power, and other factors that must be taken into consideration when implementing them in an epidemiological study design.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.413 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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