Decision making in cancer: Causal questions require causal answers

Treatment decisions in cancer care are guided by treatment effect estimates from randomized controlled trials (RCTs). RCTs estimate the average effect of one treatment versus another in a certain population. However, treatments may not be equally effective for every patient in a population. Knowing the effectiveness of treatments tailored to specific patient and tumor characteristics would enable individualized treatment decisions. Getting tailored treatment effects by averaging outcomes in different patient subgroups in RCTs requires an unfeasible number of patients to have sufficient statistical power in all relevant subgroups for all possible treatments. The American Joint Committee on Cancer (AJCC) recommends that researchers develop outcome prediction models (OPMs) in an effort to individualize treatment decisions. OPMs sometimes called risk models or prognosis models, use patient and tumor characteristics to predict a patient outcome such as overall survival. The assumption is that the predictions are useful for treatment decisions using rules such as "prescribe chemotherapy only if the OPM predicts the patient has a high risk of recurrence". Recognizing the importance of reliable predictions, the AJCC published a checklist for OPMs to ensure dependable OPM prediction accuracy in the patient population for which the OPM was designed. However, accurate outcome predictions do not imply that these predictions yield good treatment decisions. In this perspective, we show that OPM rely on a fixed treatment policy which implies that OPM that were found to accurately predict outcomes in validation studies can still lead to patient harm when used to inform treatment decisions. We then give guidance on how to develop models that are useful for individualized treatment decisions and how to evaluate whether a model has value for decision-making.