Marginal Structural Models (MSMs) are causal models designed to adjust for time-dependent
confounders in observational studies with dynamically-adjusted treatments. They are robust tools
to assess causality in complex longitudinal data. In this paper a MSM is proposed with an innovative
dose-delay joint-exposure model for Inverse-Probability-of-Treatment Weighted (IPTW) estimation of
the causal effect of alterations to the therapy intensity. The model is motivated by a precise clinical
question concerning the possibility of reducing dosages in a regimen. It is applied to data from a
randomised trial of chemotherapy in osteosarcoma, an aggressive primary bone-tumour.
Chemotherapy data are complex because their longitudinal nature encompasses many clinical
details like composition and organisation of multi-drug regimens, or dynamical therapy adjustments.
This manuscript focuses on the clinical dynamical process of adjusting the therapy according to
the patient’s toxicity history, and the causal effect on the outcome of interest of such therapy
modifications. Depending on patients’ toxicity levels, variations to therapy intensity may be achieved
by physicians through the allocation of either a reduction or a delay of the next planned dose.
Thus, a negative feedback is present between exposure to cytotoxic agents and toxicity levels, which
acts as time-dependent confounders.
The construction of the model is illustrated highlighting the high complexity and entanglement of
chemotherapy data. Built to address dosage reductions, the model also shows that delays should
not be introduced in the therapy administration. The last aspect makes sense from the cytological
point of view, but it is seldom addressed in the literature
