Multistate Pharmacometric Model to Define the Impact of Second‐Line Immunotherapies on the Survival Outcome of the IMpower131 Study

Abstract

Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow-up), and is considered to be the most reliable cancer end point. However, the introduction of second-line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second-line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non-small cell lung cancer treated with or without atezolizumab plus nab-paclitaxel and carboplatin, and characterized the survival data. High PD-L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response. The hazard of death after progression was lower for participants who had longer treatment response, i.e., longer time to progression. The simulations based on the final multistate model showed that the addition of atezolizumab to the nab-paclitaxel and carboplatin regimen had significant improvement in the patients' survival (hazard ratio = 0.75, 95% prediction interval: 0.61-0.90 favoring the atezolizumab + nab-paclitaxel and carboplatin arm). The developed modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, and investigate the benefit of primary therapy in survival while accounting for the switch to alternative treatment in the case of disease progression.

A multistate modeling and simulation framework to learn dose–response of oncology drugs: Application to bintrafusp alfa in non‐small cell lung cancer

“…Bias in the estimation of treatment benefit can be reduced naturally with multistate modeling framework. In a recent publication, “second‐line treatment” was treated as a state and enabled the separation of the confounding effect of subsequent therapy from the investigated effect of primary treatment on OS 31 . In this study, the framework was expanded to incorporate the unknown state to accommodate patients who were lost to follow‐up for tumor assessment ( n = 10, 12.5%) due to treatment termination before progression or have missing tumor response data, thus avoided assuming such patients retained in the stable or response state.…”

“…In a recent publication, "second-line treatment" was treated as a state and enabled the separation of the confounding effect of subsequent therapy from the investigated effect of primary treatment on OS. 31 In this study, the framework was expanded to incorporate the unknown state F I G U R E 3 Comparison of simulated Kaplan-Meier curves of progression-free survival (PFS) and overall survival (OS) between dose groups (1200 mg q2w vs. 500 mg q2w). Kaplan-Meier plot of expected PFS and OS for virtual patients with second-line non-small cell lung cancer receiving 500 mg (red) or 1200 mg (blue) every 2 weeks (q2w) of bintrafusp alfa.…”

Comparison of two‐stage and joint TGI‐OS modeling using data from six atezolizumab clinical studies in patients with metastatic non‐small cell lung cancer

“…The multistate model that allows for simultaneous estimation of transition hazards of intermediate events (RECIST response status) along with tumor model-derived metrics offers an alternative approach to predicting OS distributions 31 and particularly OS HR when it is confounded by the introduction of subsequent (e.g., second-line) treatments after disease progression. 32 Recently, Chen et al presented a comparison of joint and two-stage approaches using data from a phase I/ II investigating durvalumab in patients with metastatic urothelial cancer. 33 They concluded the joint modeling more accurately predicted OS than the two-stage approach based on the associated concordance index and Brier score.…”

“…As those patients are likely to die early or leave the study, predicted OS distributions may be less favorable to atezolizumab. The multistate model that allows for simultaneous estimation of transition hazards of intermediate events (RECIST response status) along with tumor model‐derived metrics offers an alternative approach to predicting OS distributions 31 and particularly OS HR when it is confounded by the introduction of subsequent (e.g., second‐line) treatments after disease progression 32 …”

Multistate modeling for survival analysis in critically ill patients treated with meropenem

“…This would induce biased estimation of the death hazard when competing (or semi‐competing) events appear (e.g., antibiotic treatment termination, discharge, or death due to other causes in patients with infection). Multistate modeling, introducing multiple intermediate states and transition‐dependent hazard functions, has recently gained popularity for describing the natural progression of diseases like cancer (e.g., stable disease, response or progression, and death), and helps to mitigate the bias introduced by confounding factors (e.g., second‐line treatment after disease progression) 11,12 . Compared to single hazard TTE analysis, multistate modeling allows to simultaneously perform related TTE analysis (e.g., transition from stable disease to response/progression, or transition from stable disease to death/censoring) 13–15 .…”