Bayesian causal inference with longitudinal data
Bibliographic record
Abstract
Bayesian statistical methods are becoming increasingly in demand in clinical and public health research. In the context of causal inference, Bayesian methods propagate estimation uncertainty, allow direct probability summaries of the causal parameters of interest, and afford us the ability to incorporate prior clinical/expert beliefs. Despite their unique estimation features and wide applicability, Bayesian causal inference methods for handling longitudinal data under observational designs have received little attention in the statistical literature. Under the observational setting, treatment assignment at each clinical visit follows a patient-adaptive decision strategy, where the clinician tailors treatment to the individual patient based on the current and past clinical measurements, as well as the treatment histories. Given the inherent time-dependent structure with longitudinal data, any additional data complexity, such as a repeatedly measured outcome, censoring, and high dimensional time-dependent covariates, brings substantial analytical challenges. In this thesis, two novel Bayesian causal methods to account for time-dependent confounding and time-dependent treatment in longitudinal observational studies are proposed. The first method extends Bayesian propensity score analysis and Bayesian marginal structural models to estimate visit-specific treatment effects with a repeatedly measured outcome. The second method introduces a Bayesian latent class approach to achieve causal inference from a joint model of time-dependent covariates, treatment and an end of study outcome. These proposed methods are compared to existing frequentist causal methods in simulation studies and their use is illustrated using real-world pediatric clinical data.
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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 | 0.039 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".