Comparing strategies to estimate the association of obesity with mortality via a Markov model
Bibliographic record
Abstract
We used a first order discrete Markov model to investigate strategies to obtain unbiased estimates of the relative mortality hazard for comparing obese with non-obese participants. This hazard ratio is confounded by the fact that obese participants can be either sick or well, as can non-obese participants, and participants can migrate over time from their initial classification on obesity and health status. The parameters of the model were estimated from national survey data and used to illustrate different analytic approaches. The purpose was to compare analytic approaches and not to provide an analysis of a particular data set. Under this model, short term health-stratum-specific estimates are unbiased for estimating the health-stratumspecific instantaneous mortality hazard ratios from obesity, and updating information on body mass index and disease status during long term follow-up reduces bias. For followup over 10 or 20 years, exclusion of participants with preexisting disease, excluding the first five years of follow-up, and methods of analysis that ignore health status yield biased estimates of the instantaneous mortality hazard ratios. However, over 10 or 20 year time periods, long-term average mortality hazard ratios or cumulative mortality relative risks are a better reflection of the total impact of obesity, including its tendency to accelerate transitions to sickness under this model, than are instantaneous mortality hazard ratios. Over these longer time periods, average relative hazard estimates or cumulative mortality relative risks based on initially well participants, on initially sick participants, and on the combined initial population each provide valuable descriptions of associations of obesity with mortality.
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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 | 0.060 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".