# AKI Health Analytics Blog


## Posts

- [Population-Level Interventions and the Limits of Subgroup Analyses in Time-Series Studies](https://blog.akianalytics.co.uk/population-level-interventions/): Interrupted time series (ITS) designs are among the strongest quasi-experimental approaches available when randomized trials are infeasible, unethical, or impractical.
- [Synthesising Real World Evidence: Are we doing it right?](https://blog.akianalytics.co.uk/synthesising-real-world-evidence/): For decades, randomised controlled trials (RCTs) have been regarded as the gold standard for evaluating the efficacy and safety of healthcare interventions.
- [Confounding Bias II: Target Trial Emulation — Helpful Framework or False Comfort?](https://blog.akianalytics.co.uk/target-trial-emulation/): Target trial emulation (TTE) is a causal inference framework that explicitly defines the protocol of a hypothetical randomized trial—the “target trial”—and then uses observational data to emulate each of its components.
- [Confounding Bias I: The Underutilisation of DAGs in Real-World Evidence Studies](https://blog.akianalytics.co.uk/confounding-bias/): Modern epidemiology routinely employs three broad types of modelling frameworks—association, prediction, and causal modelling—each serving distinct scientific aims.
- [Immortal Time Bias](https://blog.akianalytics.co.uk/immortal-time-bias/): In both observational and Randomised Clinical Trial studies, the validity of causal inferences depends on the proper classification of exposure/treatment and follow-up time.

## Optional

- [Agent (MCP protocol)](websites-agents.hostinger.com/blog.akianalytics.co.uk/mcp)

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