Causal inference with RCTs of chemotherapy Thank the audience for joining this session Thank the organizers for the opportunity of presenting this work the importance of well-documented side-effects C. Lancia, Leiden Univ. Medical Center

Summary Question Effect of treatment adaptations on mortality in osteosarcoma, and cancer treatment in general Problem Treatment-adjustment bias, i.e. toxicities are time-dependent confounders Mehods Data from MRC BO06, RCT of chemotherapy in osteosarcoma Inverse Probability of Treatment Weighing (IPTW) [Hernán & Robins, J Epi Comm, 2006] Adjusted Kaplan-Meier estimators (AKME) [Therneau, Crowson & Atkinson, 2015]

MRC BO06 The data I am going to present next were collected during the Randomized Clinical Trial MRC BO06 Patients were randomised to two regimens having the same cumulative dose for both Doxorubicin, here represented by an A, and Cisplatin, represented by a P The only difference in the two regimens is the intended duration, From the first to the last course, Regimen 1, the control arm, was intended to last 19 weeks in the control arm, while Regimen 2, the dose-intense arm, was intended to last 14 weeks Thus, the intended difference in treatment time was 5 weeks

Treatment trajectories

Treatment adaptations and outcome Event-free survival in a landmark analysis at 180 days since randomisation Patients that completed all 6 cycles of BO06 Treatment adaptations: dose reduction of at least 0.15 standardized units We use the number of full cycles received as a proxy of the actual dose received We are interested in its relation with event-free survival Note: the intended dose received in BO06 is the same in the two arms! The idea of this reweighing is that, the decision of reducing the following cycle is independent of the toxicities reported provided that these are sufficiently severe

Treatment-adjustment bias This picture shows the so-called treatment-adjustment bias Treatment administration, in terms of cycle delay and course reductions, predict future side effects The latter predict in turn future delays and reductions Everything is a risk factor for the outcome Therefore, toxicities are time-dependent confounders for the effect of treatment adaptations on survival

Inverse Probability-of-Treatment Weighing IPTW reweighs each patient with the proability of observing a treatment adaptation 𝐴 𝑡 given the reported toxicities 𝑳 𝑡 and his/her baseline characteristics 𝐵 In formulas, the ith patient contributes with 𝑠 𝑤 𝑖 = ∏ 𝑡=1 𝑇 P A 𝑖,t 𝐴 𝑖,𝑡−1 ) P A 𝑖,t 𝐴 𝑖,𝑡−1 , 𝑳 𝑖,𝑡−1 , 𝐵 i ) copies of himself This techinque creates a pseudopopulation where the effect of treatment adapatations on mortality is the same treatment is adapted independently of the reported toxicities Why is it appealing: a crude analysis on this pseudopopulation enables causal inference because in absence of confounding, association means causation Inverse Probability-of-Treatment Weighing is a technique for reweighing patients with the inverse probability of observing the allocated treatment given the past medical history and the baseline characteristics Theoretically, IPWT creates a pseudopopulation where, in each strata of baseline variable and toxicity severity, treatment adaptations are independent of treatment side-effects Further, the effect of treatment adaptations on survival is the same as in the original population As such, a crude analysis on this pseudopopulation enables causal inference as in absence of confounding, associations means causation

The role of side effects The model for adaptations given the side effects is a crucial aspect of IPTW Different modelling strategies can lead to completely different results Simulations show that unfortunate modelling choices can yield bias enhancement Attention: If toxicities are not well-documented, the association between side effects and therapy adaptations can be distorted; this might lead to uncorrect reweigthing Attention: if toxicity and treatment histories are not recorded at the same timepoints, the causal dependencies might not be accurately captured and many modelling choices will be dictated by the less informative history

Construction of IPTW Weights: pooled logistic regression for the first reduction of 0.15 standardized units Patients are weighted with the regression until the first reduction Acute toxicity: any occurrence of Low WBC (CTCAE ≥ 3) Low plateletes (CTCAE ≥ 3) Infection (CTCAE ≥ 3) Mucositis (CTCAE ≥ 3) Cardiotoxicity (CTCAE ≥ 3) Ototoxicity (CTCAE ≥ 3) Baseline confounders: Age (paediatric/adult patient) and regimen (2-weekly/3-weekly) The idea of this reweighing is that, the decision of reducing the following cycle is independent of the toxicities reported provided that these are sufficiently severe

Crude vs Adjusted Kaplan-Meier EFS at two years is 2.5 years is basically the same in the right plot Possible confounding left Treatment duration Patients’tolerability

Conclusions The effect of the delivered treatment on survival is confounded by toxicity IPTW + AKME is an attempt of dealing with such confounding Well-reported side effects are the key to the application of IPTW Reporting only the most severe grade over a cycle or a period masks causal relationships Addressing the effect of delays on outcome requires detailed reporting of myelotoxicity Patient’s tolerability is an important aspect that is currently left out It could be possibly included by stratifying according to the reported toxicities

Acknowledments Marta Fiocco, Leiden University Medical Center Dr. Jakob Anninga, Radboud University Nijmegen Cristian Spitoni, University Medical Center Utrecht Matthew R. Sydes, Medical Research Council Gordana Jovic, Medical Research Council Dr. Jeremy Whelan, University College London Hospital This work was supported by Dutch Foundation KiKa