DOI: 10.25934/pr00007384
title: Application of Estimands and Causal Reasoning in the Reanalysis of Endpoint Measures in Advanced/Metastatic Non-Small Cell Lung Cancer Clinical Trials

description: Lung cancer (LC) is the most frequently diagnosed cancer and the leading cause of cancer-related deaths globally. Non-Small Cell Lung Cancer (NSCLC) is the most common subtype, accounting for 85–90% of LC cases. 

The effect of treatment is assessed from randomized clinical trials that aim to describe how the outcome of treatment compares to what would have happened to the same subjects under different treatment conditions. Analysis of clinical trials could be done using different strategies. One way is the application of the intention to treat (ITT) analysis. The other way is the Per-Protocol (PP) analysis. Both of these methods have strengths and flaws. 

The ITT analysis is a strategy for analyzing data where subjects initially allocated to a treatment group should be followed up and analyzed as members of that group whether or not they completed the intervention given to that group. This is achieved by ignoring changes in treatment caused by intercurrent events (ICEs). ICEs are events occurring after treatment initiation that affect either the interpretation or the existence of measurements associated with the clinical question of interest. Some examples of ICEs are switching from placebo to active treatment or death. Applying the ITT principle, may not certainly result in estimating the causal effect of receiving the treatment but rather the effect of assignment to intervention. The ITT estimate often gives a pragmatic and conservative estimate of the causal effect influenced by the effects of the so called ICEs.

The other way of analyzing clinical trials is to exclude patients who did not comply with the Clinical trial protocol, this is called ‘per-protocol’ (PP) analysis. The PP population is a specific subset of the trial population who complied with the protocol. The selection of this subset of population for the analysis though many times is of clinical interest causes disruption of randomization and may introduce confounding resulting in biased effect estimates.

Treatment switching after first progress in cancer trials is common. Treatment switching and other ICEs though unavoidable can introduce complexities in estimating treatment effects for longer-term effects, most notably overall survival (OS). Hence, it is often desirable to adjust OS estimates to reflect what would have been observed had control group patients not switched treatments.

The International Conference on Harmonization (ICH), a global organization that issues and maintains numerous guidance documents, has recognized that a precise definition of the scientific question of interest is required to ensure alignment between trial objectives, trial design, data collection, analysis and interpretation. Recently, the ICH issued the final version of an addendum which introduces a new framework that precisely describe the treatment effect of interest (Estimand). Estimand is the quantity estimated by a statistical analysis to address the scientific question of interest posed by the trial objective. It is of interest to operationalize this abstract concept to specific clinical trial settings. We plan to operationalize different estimand concepts for OS in NSCLC clinical trials regarding treatment switching along with other ICEs. We aim to reanalyze the outcomes in advanced/metastatic NSCLC patients using phase III randomized clinical trials following the Estimands framework and based on causal statistics.

To meet our research objectives a multidisciplinary team will be involved in the discussion around all possible ICEs and suitable strategies to account for these events. We as an academic institution with statistical background will be mainly interested in applying methodological best practice approaches in clinical trials based on causal reasoning. To comply with the estimand concept, a clinician will be looking if the clinical problem at hand is transformed into the correct methodological concept.

Until now, academic oncology doesn’t consider routinely estimands as an opportunity. The criticality of the disease, its poor prognosis and the costs of innovative therapies request a careful methodological approach to provide analyses that correctly answer relevant clinical questions regarding the effects of novel therapies. By developing specific objectives relating to evaluating the patient’s perspective of treatment and embracing the estimand framework, we can guarantee useful collection of data and deliver clear interpretation and conclusions to all stakeholders, thereby providing clear evaluation on the impact of treatment on patients’ lives.