An interactive, structured algorithm for evaluating methodological bias in observational comparative-effectiveness research.
This algorithm follows a design → bias threat → remedy logic. Each step asks whether a key component of a hypothetical randomized trial has been clearly and validly reproduced in the observational setting.
The structure covers:
- Internal Validity: causal question, estimand, eligibility, source population, treatment classification, time-zero alignment, confounding, outcome ascertainment, censoring, adherence, missing data, and analytic robustness
- Evidence Dissemination & Interpretation: whether conclusions are appropriately restrained and reporting is complete
- Start from the top of the flowchart
- For each diamond-shaped checkpoint, assess whether the criterion is met
- If Yes — proceed to the next checkpoint
- If No — review the identified bias threat and consider the suggested remedy
- A study that passes all checkpoints has no major design-related bias identified within this framework
- Dark/light mode support (follows system preference)
- Responsive design
- Color-coded bias threats (red) and remedies (green)
- Critical checkpoints highlighted (amber)
- Operationalization checkpoints distinguished (blue-grey)
If you use this algorithm in your work, please cite appropriately.
This work is provided as a practical methodological guide for educational and research purposes.