Effects of sparse follow-up on marginal structural models for time-to-event data
Bibliographic record
Abstract
Background: Survival time is a common parameter of interest that can be estimated by using Cox Proportional Hazards models when measured continuously. An alternative way to estimate hazard ratios is to cut up time into equal-lengthed intervals and consider the by-interval outcome to be 0 if the person is alive during this interval and 1 otherwise. In this discrete-time approximation, instead of using a Cox model, one should perform pooled logistic regression to get unbiased estimate of survival time under the assumption of low death rate per interval. This fact is satisfied when shorter intervals is used in order to have fewer events in each time, however, by doing this, problems such as missing values can arise because the actual visits occur less frequently in a survival setting and one must therefore account for the missing values. Objective: We investigate the effect of two methods of filling in missing data, Last Observation Carried Forward (LOCF) and Multiple Imputation (MI), as well as Available Case Study. We compare these three different approaches to complete data analysis. Methods: Weighted pooled logistic regression is used to estimate the causal marginal treatment effect. Complete data were generated using Young's algorithm to obtain monthly information for all individuals, and from the complete data, observed data were selected by assuming follow-up visits occurred every six or three months. Thus, to analyze the observed data at a monthly level, we performed LOCF and MI to fill in the missing values and compared the results to those from a completely-observed data analysis. We also included an analysis of the observed-data without any imputation. We then applied these methods to the Canadian Co-infection Cohort to estimate the impact of alcohol consumption on liver fibrosis.Results: In most simulations, MI produced the least biased and least variant estimators, even outperforming analyses based on completely-observed data. In the presence of stronger confounding, MI-based estimators were more biased but nevertheless less variant than the estimators based on completely-observed data.Conclusion: Multiple Imputation is superior to last-observation carried forward and observed-data analysis when marginal structural models are used to adjust for time-varying exposure and variables in the context of survival analysis and data are missing or infrequently measured.
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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 | 0.239 | 0.495 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".