Perils of Randomized Controlled Trial Survival Extrapolation Assuming Treatment Effect Waning: Why the Distinction Between Marginal and Conditional Estimates Matters

A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up, hence sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a HR (hazard ratio) to 1 does not necessarily estimate loss of individual-level treatment effect accurately due to HR selection bias. A simulation study was designed to explore the behaviour of marginal HRs under a waning conditional (individual-level) treatment effect and demonstrate bias in forcing a marginal HR to 1 when the estimand is 'survival difference with individual-level waning'.Data were simulated under 4 parameter combinations (varying prognostic strength of heterogeneity and treatment effect). Time varying marginal HRs were estimated in scenarios where the true conditional HR attenuated to 1. ΔRMSTs (restricted mean survival time difference), estimated having constrained the marginal HR to 1, were compared to true values to assess bias induced by marginal constraints.Under loss of conditional treatment effect, the marginal HR took a value >1 due to covariate imbalances. Constraining this value to 1 lead to ΔRMST bias of up to 0.8 years (57% increase). Inflation of effect size estimates also increased with the magnitude of initial protective treatment effect.Important differences exist between survival extrapolations assuming marginal versus conditional treatment effect waning. When a marginal HR is constrained to 1 to assess efficacy under individual-level treatment effect waning, the survival benefits associated with the new treatment will be over-estimated and incremental cost-effectiveness ratios will be under-estimated.

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