Effect of Study Duration and Outcome Measurement Frequency on Estimates of Change for Longitudinal Cohort Studies in Routinely-Collected Administrative Data
Bibliographic record
Abstract
INTRODUCTION: When designing longitudinal cohort studies, investigators must make decisions about study duration (i.e. length of follow-up) and frequency of outcome measurement. This research explores these design decisions for longitudinal cohort studies constructed using routinely-collected administrative data. OBJECTIVES: To illustrate the effects of varying study duration and frequency of outcome measurement in longitudinal cohort studies conducted using routinely-collected administrative data using a numeric example. METHODS: Linked administrative data from Manitoba, Canada were used. The cohort included mothers who experienced the death of an infant between April 1, 1999 and March 31, 2012 and a matched (three:one) group of mothers who did not experience an infant death. A generalized linear model was used to test for differences between groups in the non-linear (i.e. quadratic) and linear trend over time for the number of healthcare contacts. Holding sample size constant, models were fit to the data for various combinations of study duration and measurement frequency. Regression coefficient estimates and their standard errors were compared. RESULTS: A total of 2576 mothers were included; 644 experienced an infant death and 1932 were matches. Thirteen combinations of measurement frequency (one, two, three, four periods/year) and study duration (one, two, three, four years) were investigated. As frequency increased from one to four periods/year, the standard errors of the regression coefficients for the group difference in the non-linear trend (i.e. group-time-time interaction) decreased up to 98.9%. As duration increased from one to fours years, the standard errors decreased up to 96.9%. As frequency and duration increased, the estimated regression coefficients trended toward zero. Similar results were observed for the linear trend model. CONCLUSION: Longitudinal cohort studies based on administrative data offer flexibility in time-related design elements, but present potential challenges. Recommendations about how to select and report design decisions in studies should be included in reporting guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.657 | 0.787 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.018 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".