Modeling Therapy Sequence for Advanced Cancer: A Microsimulation Approach Using Real-World Data

Project Summary/Abstract In modern treatment of advanced cancers, many patients go through multiple rounds of therapy using different pharmaceutical agents. Although therapy switching is common in clinical practice, the cost-effectiveness of sequences of therapies is under-studied. Effective and accurate modeling of multiple lines of therapy remains an open problem. The often-used Markov model is memoryless, which is problematic when studying multiple lines of therapy, as the model cannot easily deal with dependence within patients. Therefore, there is an unmet and growing need to develop specialized models to evaluate the cost-effectiveness of therapy sequence (that is, which agents are given and in what order). Real-world patient information extracted from the Electronic Medical Record (EMR) provides a novel opportunity to better understand treatment outcomes in practice. We can use EMR data to create improved health-state models which synthesize patient-level data, published results from clinical trials, and health utility (quality of life) estimates from the literature. Here, we propose to study therapy sequence by incorporating real-world data in microsimulation models. These state-transition models use Monte Carlo methods to simulate individual paths through different health states. Our models will particularly focus on correctly accounting for the dependence of events over time within patients. Patient-level variables, such as tumor biology, age, and comorbid conditions, will impact treatment outcomes on all lines of therapies, thus introducing statistical dependence. We will first estimate transition probabilities between states using several parametric models fit to the EMR-based data. These transition probabilities will then be used in a microsimulation model which also includes auxiliary information including healthcare costs and quality of life adjustments (utilities). We will also develop a fully non-parametric approach using bootstrap resampling of the patient population, where each individual’s path through model states will be used directly. Again, costs and utilities will be incorporated for each health state. In simulation studies, we will assess the performance of each model compared to a traditional Markov model approach. Finally, we will illustrate the application of this method in a study of advanced urothelial carcinoma using an EMR-derived dataset created by Flatiron Health. This dataset contains extensive data on treatments and outcomes including progression events and death, however, it lacks all the measures we would need to conduct a full cost-effectiveness analysis (i.e. financial costs and quality of life are not captured). We will demonstrate how our approach can be used to estimate the cost-effectiveness of first-line carboplatin based therapy (vs. first-line cisplatin based therapy) followed by second-line immune checkpoint inhibitors, and we will compare results from the different strategies.