Real-World Evidence Analysis in R

To emulate a target trial using real-world data in R, configure a workflow specifying eligibility, treatment strategies, follow-up, and outcomes. Run the TrialEmulation package to quantify comparative effectiveness through observational data.

Target trial emulation is a causal inference framework that designs observational studies to mimic randomized trials. It specifies eligibility, treatment strategies, and follow-up to generate credible real-world evidence without actual randomization.

Handle time-varying confounding in observational studies by implementing sequential trial emulation and marginal structural models. These methods address dynamic treatment regimens to estimate accurate causal effects over time.

Yes, you can apply propensity score methods for external control arms in R. Estimate and apply weights or matching to balance covariates between groups, integrating external data sources to augment trial comparisons.

The best way to quantify comparative effectiveness with real-world evidence is by leveraging survival models and propensity score methods. This combination estimates treatment effects over time while balancing baseline covariates.

Yes, target trial emulation supports survival analysis workflows. Leverage survival models and causal inference techniques to estimate comparative effectiveness over time, addressing dynamic treatment regimens through sequential trial emulation.