real-world-evidencelisted

# Real-World Evidence Analysis in R ## Overview Methods for analyzing real-world data (RWD) to generate real-world evidence (RWE). Covers target trial emulation, comparative effectiveness research, propensity score methods for observational data, external control arms, bias quantification, and sensitivity analysis for unmeasured confounding. ## Target Trial Emulation ### Conceptual Framework ```r # Target trial emulation framework # Specify the target trial protocol, then emulate using observational data # Key elements to specify: # 1. Eligibility criteria # 2. Treatment strategies # 3. Assignment procedures # 4. Follow-up period # 5. Outcome # 6. Causal contrast (ITT, per-protocol, etc.) # 7. Analysis plan ``` ### Using TrialEmulation Package ```r library(TrialEmulation) # Prepare data for target trial emulation # Data should be in long format with time-varying covariates # Example: Clone-censor-weight approach trial_data <- initiators( data = rwd, id = "patient_id", period = "period", treatment = "treatment", outcome = "outcome", eligible = "eligible", outcome_cov = ~ age + sex + comorbidity, model_var = "assigned_treatment", switch_d_cov = ~ time_since_start + lag_outcome, first_period = 1, last_period = 52, use_censor_weights = TRUE ) # Fit the model fit <- trial_msm( trial_data, outcome_cov = ~ assigned_treatment * poly(follow_up, 2), model_var = "assigned_treatment", include_followup_time = TRUE, include_trial_period = TRUE,