Causal effect of chemotherapy received dose intensity on survival outcome: a retrospective study in osteosarcoma

TL;DR

This study develops a novel causal inference approach to assess how reducing chemotherapy dose intensity affects survival in osteosarcoma patients, revealing differential effects based on histological response.

Contribution

It introduces a new methodology combining IPTW and bootstrap techniques to analyze retrospective RCT data for treatment effect modification.

Findings

Higher RDI reduction improves survival in poor responders.

Lower RDI reduction worsens survival in good responders.

The approach addresses confounding and extends RCT data analysis.

Abstract

This study aims to analyse the effects of reducing Received Dose Intensity (RDI) in chemotherapy treatment for osteosarcoma patients on their survival by using a novel approach. In this scenario, toxic side effects are risk factors for mortality and predictors of future exposure levels, introducing post-assignment confounding. Chemotherapy administration data from BO03 and BO06 Randomized Clinical Trials (RCTs) in ostosarcoma are employed to emulate a target trial with three RDI-based exposure strategies: 1) standard, 2) reduced, and 3) highly-reduced RDI. Investigations are conducted between subgroups of patients characterised by poor or good Histological Responses (HRe). Inverse Probability of Treatment Weighting (IPTW) is first used to transform the original population into a pseudo-population which mimics the target randomized cohort. Then, a Marginal Structural Cox Model with effect modification is employed. Conditional Average Treatment Effects (CATEs) are ultimately measured as the difference between the Restricted Mean Survival Time of reduced/highly-reduced RDI strategy and the standard one. Confidence Intervals for CATEs are obtained using a novel IPTW-based bootstrap procedure. Significant effect modifications based on HRe were found. Increasing RDI-reductions led to contrasting trends for poor and good responders: the higher the reduction, the better (worsen) was the survival in poor (good) reponders. This study introduces a novel approach to (i) comprehensively address the challenges related to the analysis of chemotherapy data, (ii) mitigate the toxicity-treatment-adjustment bias, and (iii) repurpose existing RCT data for retrospective analyses extending beyond the original trials' intended scopes.