A Bayesian approach for two‐stage multivariate Mendelian randomization with mixed outcomes
Bibliographic record
Abstract
Many research studies have investigated the relationship between baseline factors or exposures, such as patient demographic and disease characteristics, and study outcomes such as toxicities or quality of life, but results from most of these studies may be problematic because of potential confounding effects (eg, the imbalance in baseline factors or exposures). It is important to study whether the baseline factors or exposures have causal effects on the clinical outcomes, so that clinicians can have better understanding of the diseases and develop personalized medicine. Mendelian randomization (MR) provides an efficient way to estimate the causal effects using genetic instrumental variables to handle confounders, but most of the existing studies focus on a single outcome at a time and ignores the correlation structure of multiple outcomes. Given that clinical outcomes like toxicities and quality of life are usually a mixture of different types of variables, and multiple datasets may be available for such outcomes, it may be much more beneficial to analyze them jointly instead of separately. Some well-established methods are available for building multivariate models on mixed outcomes, but they do not incorporate MR mechanism to deal with the confounders. To overcome these challenges, we propose a Bayesian-based two-stage multivariate MR method for mixed outcomes on multiple datasets, called BMRMO. Using simulation studies and clinical applications on the CO.17 and CO.20 studies, we demonstrate better performance of our approach compared to the commonly used univariate two-stage method.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".