Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection

Abstract

Background: Due to limited duration of follow up in clinical trials of cancer treatments, estimates of lifetime survival benefits are typically derived using statistical extrapolation methods. To justify the method used, a range of approaches have been proposed including statistical goodness-of-fit tests and comparing estimates against a previous data cut (i.e. interim data collected). In this study, we extend these approaches by presenting a range of extrapolations fitted to four pre-planned data cuts from the JAVELIN Merkel 200 (JM200) trial. By comparing different estimates of survival and goodness-of-fit as JM200 data mature, we undertook an iterative process of fitting and re-fitting survival models to retrospectively identify early indications of likely long-term survival.

Methods: Standard and spline-based parametric models were fitted to overall survival data from each JM200 data cut. Goodness-of-fit was determined using an assessment of the estimated hazard function, information theory-based methods and objective comparisons of estimation accuracy. Best-fitting extrapolations were compared to establish which one provided the most accurate estimation, and how statistical goodness-of-fit differed.

Results: Spline-based models provided the closest fit to the final JM200 data cut, though all extrapolation methods based on the earliest data cut underestimated the 'true' long-term survival (difference in restricted mean survival time [RMST] at 36 months: - 1.1 to - 0.5 months). Goodness-of-fit scores illustrated that an increasingly flexible model was favored as data matured. Given an early data cut, a more flexible model better aligned with clinical expectations could be reasonably justified using a range of metrics, including RMST and goodness-of-fit scores (which were typically within a 2-point range of the statistically 'best-fitting' model).

Conclusions: Survival estimates from the spline-based models are more aligned with clinical expectation and provided a better fit to the JM200 data, despite not exhibiting the definitively 'best' statistical goodness-of-fit. Longer-term data are required to further validate extrapolations, though this study illustrates the importance of clinical plausibility when selecting the most appropriate model. In addition, hazard-based plots and goodness-of-fit tests from multiple data cuts present useful approaches to identify when a more flexible model may be advantageous.

Trial registration: JAVELIN Merkel 200 was registered with ClinicalTrials.gov as NCT02155647 on June 4, 2014.

Keywords: Cancer; Extrapolation; Immune-oncology; Immunotherapy; Survival.

Conflict of interest statement

MB is an employee of EMD Serono, Rockland, MA, USA (a business of Merck KGaA, Darmstadt, Germany); MS is an employee of Merck KGaA, Darmstadt, Germany; AA is an employee of EMD Serono, Northwood, UK. AB and AJH are employees of Delta Hat, who were a paid consultant to Merck KGaA, Darmstadt, Germany. AW is an employee of BresMed, who were a paid consultant to Merck KGaA, Darmstadt, Germany.

Figures

Fig. 1

Overall survival data from Part A of the JAVELIN Merkel 200 clinical trial. Key: mFU, minimum follow-up; mo, month(s)

Fig. 2

Smoothed hazard plots from Part A of the JAVELIN Merkel 200 clinical trial. Note: Owing to the sample size of JAVELIN Merkel 200 Part A (n = 88 patients), the max.time argument required by the muhaz function was set to the minimum follow-up time for each data cut. Consequently, the smoothed hazard estimate for each data cut is presented within this figure for a limited time period

Fig. 3

Fitted models from Part A of the JAVELIN Merkel 200 clinical trial. Notes: A, 12-month data cut; B, 18-month data cut; C, 24-month data cut; D, 36-month data cut. Key: k, knot(s); KM, Kaplan-Meier; n, normal; o, odds.