Immune checkpoint inhibitors at any treatment line in advanced NSCLC: Real-world overall survival in a large Italian cohort

Abstract

To estimate the average treatment effect of immune checkpoint inhibitors in any line of treatment in a 2016-2018 population-based cohort of patients with advanced non-small-cell lung cancer (NSCLC). Materials and methods: The cohort, and information on the tumor, were derived from the cancer registry of the Agency for Health Protection of Milan, Italy. Inclusion criteria were adult age, microscopically confirmed NSCLC, stage IIIB or IV at diagnosis, and having received at least one line of treatment. Treatment with all licensed anti PD-1/PD-L1 inhibitors was derived from inpatients and outpatients' pharmaceutical databases of the ATS and vital status at 31 December 2019 from the health registry office of the Lombardy region. We investigated, with a causal approach, the relationship between survival and anti PD-1/PD-L1 treatment at any line constructing a directed acyclic graph and fitting a Marginal Structural Cox Model (MSCM). Results: Of 1673 subjects, 324 received anti PD-1/PD-L1 at any treatment line. Overall, one-year survival was 61.1% (95 %CI, 55.6-66.2%) in the group treated with anti PD-1/PD-L1 at any line and 31.1% (95 %CI, 28.6-33.5%) among not treated. One-year hazard ratio (HR) of death for not treated vs. treated was 2.15 (95 % CI, 1.91-2.41), decreasing to 1.23 (95 %CI, 1.03-1.46) at two years and reaching one in the third year. Conclusion:In un unselected population-based cohort with advanced lung cancer, treatment with anti PD-1/PD-L1 at any line lowered the hazard of death up to two-years from date of diagnosis, confirming the efficacy of immunotherapy outside clinical trials.

Administrative healthcare data to predict performance status in lung cancer patients

“…The scale of Performance Status (PS), developed by the Eastern Cooperative Oncology Group (ECOG) in 1982 [1] describes patient's level of functioning in terms of their ability to care for themself, daily activity, and physical ability (walking, working, etc.). We wanted to estimate the average treatment effect (ATE) of immune checkpoint inhibitors in any line of treatment in a 2016–2018 population-based cohort of patients with advanced non-small cell lung cancer (NSCLC) [2] . PS was among the variables needed for adjustment, but it was available only in 23% of the 1673 patients included in the study.…”

An introduction to causal inference for pharmacometricians

“…9 • The use of DAGs for covariate selection in epidemiological studies of relevance to pharmacometrics, for example, for characterizing longitudinal progression toward end-stage renal disease 10 or for characterizing overall survival in oncology in response to immune checkpoint inhibitors (CPIs). 11,12 • The application of interpretable artificial intelligence/ machine-learning (AI/ML) algorithms (e.g., with interpretation assisted by Shapley values) to population pharmacokinetic modeling 13 and prediction of relapse and related disease activity in multiple sclerosis, 14 contemporaneous with an increased recognition of the interpretive value of formal causal frameworks in AI/ML research. [15][16][17][18] • The advent of real-world evidence (RWE) usage in pharmacometric analyses, 19 contemporaneous with a growing body of guidance for the use of RWE that advocates for the use of causal DAGs.…”

“… 9 The use of DAGs for covariate selection in epidemiological studies of relevance to pharmacometrics, for example, for characterizing longitudinal progression toward end‐stage renal disease 10 or for characterizing overall survival in oncology in response to immune checkpoint inhibitors (CPIs). 11 , 12 The application of interpretable artificial intelligence/machine‐learning (AI/ML) algorithms (e.g., with interpretation assisted by Shapley values) to population pharmacokinetic modeling 13 and prediction of relapse and related disease activity in multiple sclerosis, 14 contemporaneous with an increased recognition of the interpretive value of formal causal frameworks in AI/ML research. 15 , 16 , 17 , 18 The advent of real‐world evidence (RWE) usage in pharmacometric analyses, 19 contemporaneous with a growing body of guidance for the use of RWE that advocates for the use of causal DAGs.…”

“…The use of DAGs for covariate selection in epidemiological studies of relevance to pharmacometrics, for example, for characterizing longitudinal progression toward end‐stage renal disease 10 or for characterizing overall survival in oncology in response to immune checkpoint inhibitors (CPIs). 11 , 12 …”

Overall survival and role of programmed death ligand 1 expression in patients with metastatic non-small-cell lung cancer and immunotherapy: an observational study from central Switzerland

“…Moreover, patients with NSCLC and a poor Eastern Cooperative Oncology Group performance status (ECOG-PS ≥2), active brain metastases, autoimmune disease, organ dysfunction, or a life expectancy below 3 months are generally excluded from checkpoint inhibitor registration studies [33]. Patients with such conditions are very common in the clinic and are now treated with checkpoint inhibitors [25,34,35], but few studies have addressed the impact of these therapies on patients treated outside of registration trials [36].…”