Effect of immortal time bias on the association between atrial fibrillation ablation and incident stroke: a meta-analysis
Bibliographic record
Abstract
Abstract Background/Introduction In the era of big data and large observational studies, identification and prevention of immortal time bias (ITB) is essential to attain unbiased effect estimates. Immortal time refers to a period of follow-up in a cohort when the outcome cannot occur. Typically, it results when the exposure is initiated after the start of follow-up. The misclassification of person-time results in a systematic overestimation of the treatment effect. ITB is a common methodological issue in epidemiology studies. We sought to assess the presence and magnitude of bias due to ITB in observational studies evaluating the effectiveness of catheter ablation (CA) for atrial fibrillation. Purpose To compare the association between CA and stroke in 1) observational studies that controlled for ITB, 2) observational studies that did not address ITB, and 3) randomized controlled trials (RCT). Methods The PUBMED database was screened from inception to January 15, 2020 for publications with the following string of search terms: (“ablation” or “catheter ablation” or “pulmonary vein isolation”) and “atrial fibrillation” and (“stroke” or “thromboembolism”). Observational studies and RCTs comparing CA to medical therapy were eligible. Studies were excluded if: 1) evaluation of the association was limited to a subgroup of AF patients, 2) cryoablation was performed, 3) strokes were not reported, and 4) HRs or 95% CI were not presented. Information on study characteristics, HRs, and the potential for ITB was extracted. Subsequently, articles were classified based on the type of study and whether ITB was addressed. For each group of articles, HRs were logarithmically transformed and pooled using the random effects model. Results A total of 10 observational studies and 1 randomized controlled trial were included in the present analysis. Of the 10 observational studies, only 2 studies were designed to prevent ITB. The pooled HR for observational studies without ITB prevention showed a statistically significant reduction in risk of stroke (HR 0.66 (95% CI 0.58–0.74); I2=27.3%) in CA patients compared to non-CA patients. However, pooling the two observational studies that prevented ITB indicated no difference in the incidence of stroke [HR 0.75 (95% CI 0.49–1.02); I2=4.5%] among patients with and without CA, a finding similar to CABANA trial [HR 0.42 (95% CI 0.11–1.21)]. Conclusion It is important to evaluate the effectiveness of procedures and medications using observational data to determine if the results from RCTs translate to the real-world. However, careful consideration needs to be taken in the design phase to avoid ITB and produce effect estimates that more accurately represent the true association between treatment and outcomes. Examples of design methods to prevent ITB include the use of a time-varying covariate or matching on pre-treatment exposure time. Funding Acknowledgement Type of funding source: None
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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 | 0.055 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.081 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".