  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.
