Application of Penalized Mixed Model in Identification of Most Associated Factors with Hemoglobin A1c Level in Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: The effect of controlling blood sugar on decreasing diabetes complications and their fatality has been investigated in many cross-sectional studies, but instability of blood sugar and some of the potential effective factors on it during the time render these studies imprecise and unreliable. Exploring among a big number of possible covariates is another challenging issue which renders the traditional methods inefficient. Therefore, we aimed to determine factors which are mostly associated with HbA1c level, among a large number of potential covariates using penalized linear mixed model in a longitudinal study METHOD: The participants consisted of diabetic patients referred to Endocrine and Metabolism Research Center of Isfahan from 2000 to 2012 who were measured 4-12 times. Linear mixed model with LASSO penalty was used to investigate the relationship between HbA1c and the factors which potentially affect HbA1c. SPSS version 18 and glmmLassopackage in R. 3.1.3 software were used for statistical analysis. RESULTS: Most of the 360 patients, (62.5%) were female. Their mean age was 52.2 years (SD=9.24) and median number of their visit was 5 with inter-quartile range of 4 to 6. The simple mixed model revealed that all of the covariates had significant effects on HbA1c, but using LMMLASSO led to elimination of 8 redundant covariates from the final model. CONCLUSION: By Appling linear mixed model with LASSO penalty retinopathy, hypertension, cholesterol, HDL and TG had the most significant association with HbA1c level.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".