Improved adjustment for covariate measurement error in radon studies: alternatives to regression calibration
Bibliographic record
Abstract
Measurement error is a type of non-sampling error that could attenuate the effect of a risk factor on an outcome variable if no correction is made. Therefore, an effect might not be detectable, even if there is one. If a classical error type is present, then the power of the analysis will be lowered or a bigger sample size will be needed in order to maintain the desirable power. Thus, a correction should be made before drawing any conclusions from the analysis. The regression calibration and simulation extrapolation methods are some of the available methods developed to deal with this kind of problem.\nThis dissertation proposes a Bayesian method that uses a hierarchical approach to jointly model true radon exposure (measurement error model) and its effect on lung cancer (excess odds model). This method takes subject-specific characteristics into account when making the correction, and uses random effects when missing data are present. We carried out a simulation study in order to compare this method to the regression calibration and simulation extrapolation (SIMEX). Different scenarios were simulated and the simulated data were analyzed with the three methods. This is the first time that these three methods have been compared in the context of radon risk assessment.\nThe simulation results showed that the proposed Bayesian method had a consistent coverage through out the scenarios. However, the SIMEX method had the lowest bias and mean squared error and, most of the time, its coverage was the closest to the nominal coverage of 95%. The regression calibration was the fastest method to be implemented, but it was outperformed by the other methods.\nThe dissertation finalizes by performing individual and pooled analyses using data from five case-control North America radon studies (Iowa, Missouri, Winnipeg, Connecticut, and Utah/South Idaho). The data from each study were analyzed individually, first without making any correction, and then using the three correction methods. Finally, the data were combined and the methods were applied to this bigger sample. To the best of our knowledge, regression calibration and SIMEX have not been implemented using this combined dataset.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".