ACCOUNTING FOR INTERVAL CENSORING WHEN ESTIMATING TIME-TO-EVENT CURVES FOR COST-EFFECTIVENESS ANALYSIS

Purpose:

Certain endpoints, such as progression-free survival, are by definition interval censored. The exact time of the event is unknown; rather we only know that it occurred between two assessment times. However, this censoring is typically ignored in survival analysis for cost-effectiveness analysis, despite the fact that statistical methods for taking account of the interval censoring are well established.

The objective of this study was to investigate, in the context of cost-effectiveness analysis, the potential bias that may occur if interval censoring is not accounted for in survival analysis.

Method:

Time-to-event data including interval censoring were simulated. 10,000 sets of 500 event times (representing a typical trial) were simulated for two treatment groups from Weibull distributions that had common shape but different scale parameters. Interval censoring was simulated assuming that assessments (for example, for progression) were conducted every four months. The actual event times were then rounded down and up to the nearest assessment to form the left and right hand censoring times respectively.

The mean time-to-event was estimated using two different parametric survival models; (i) assuming no censoring, and (ii) accounting for the interval censoring. The degree of bias in the mean difference in time-to-event between treatments was assessed by comparing the mean difference estimated from both models with the true value estimated using the parameters of the distribution. Two scenario analyses were conducted; decreasing the frequency of assessments to eight months, and increasing the event hazard in the control arm.

Result:

When interval censoring was ignored, the mean time-to-event difference was overestimated (bias = 1.91 months). The bias was reduced when methods that account for interval censoring were used (bias = 0.51 months).  When the frequency of assessments was decreased to eight months, accounting for the interval censoring reduced the bias from 1.47 months to -0.01 months. Similarly, when increasing the event hazard in the control arm, accounting for the interval censoring reduced the bias from 1.75 months to -0.70 months.

Conclusion:

Interval censoring is a common finding in clinical studies, resulting from periodic assessments for events such as disease progression. When interval censoring is present, ignoring this censoring will yield biased estimates of mean time-to-event and potentially cost-effectiveness. Therefore, accounting for interval censoring is important when estimating time-to-event curves for cost-effectiveness analysis.