Impact of Event-Related Treatment Modifications in Exposure-Response Analyses of Repeated Time-to-Event Data

Exposure-response (ER) analyses of repeated time-to-event (RTTE) data can be confounded when treatment modifications occur due to the event of interest. One particularly challenging scenario is when an event is preceded by an undocumented clinical worsening leading to treatment discontinuation shortly before the event. Event-related treatment modifications introduce potential reversed causality in the ER analysis and bias, as events may predict exposure, rather than exposure predicting events. We systematically evaluated three analysis approaches in multiple simulated placebo-controlled studies to assess their capacity to estimate unbiased ER relationships: (1) average exposure (2) time-varying exposure (3) average exposure + percentile-rank of the average amount of blinded study treatment received. The novel Approach 3 was designed to remove confounding from event-related treatment modifications by leveraging information on blinded study treatment received (including in the placebo treatment arm). Approach 1 was biased in all scenarios with event-related treatment discontinuations. Approach 2 generally resulted in unbiased estimates, except in the scenarios in which events were sometimes preceded by a treatment discontinuation. In these scenarios, only Approach 3 provided unbiased estimates with linear ER, though it showed slight bias with pronounced nonlinear relationships. In conclusion, treatment modifications associated with events warrant careful consideration in ER analyses of RTTE data. Using a ranked measure of the average amount of blinded study treatment received may be used to lower the risk of confounding due to treatment modifications.