A generalised model for individualising a treatment recommendation based on group-level evidence from randomised clinical trials

Abstract

Objectives: Randomised controlled trials report group-level treatment effects. However, an individual patient confronting a treatment decision needs to know whether that person's expected treatment benefit will exceed the expected treatment harm. We describe a flexible model for individualising a treatment decision. It individualises group-level results from randomised trials using clinical prediction guides.

Methods: We constructed models that estimate the size of individualised absolute risk reduction (ARR) for the target outcome that is required to offset individualised absolute risk increase (ARI) for the treatment harm. Inputs to the model include estimates for the individualised predicted absolute treatment benefit and harm, and the relative value assigned by the patient to harm/benefit. A decision rule recommends treatment when the predicted benefit exceeds the predicted harm, value-adjusted. We also derived expressions for the maximum treatment harm, or the maximum relative value for harm/benefit, above which treatment would not be recommended.

Results: For the simpler model, including one kind of benefit and one kind of harm, the individualised ARR required to justify treatment was expressed as required ARRtarget(i)=ARIharm(i) × RVharm/target(i). A complex model was also developed, applicable to treatments causing multiple kinds of benefits and/or harms. We demonstrated the applicability of the models to treatments tested in superiority trials (either placebo or active control, either fixed harm or variable harm) and non-inferiority trials.

Conclusions: Individualised treatment recommendations can be derived using a model that applies clinical prediction guides to the results of randomised trials in order to identify which individual patients are likely to derive a clinically important benefit from the treatment. The resulting individualised prediction-based recommendations require validation by comparison with strategies of treat all or treat none.

Keywords: STATISTICS & RESEARCH METHODS.

Figures

Figure 1

Models for individualising treatment. Variable benefit/fixed harm (A) and variable benefit/variable harm (B) models are shown. In each model, treatment benefit, modelled as absolute risk reduction for the target event, varies directly with baseline risk for the target event. Treatment harm is modelled as the absolute risk increase for the harm of treatment. Harm is then value-adjusted based on a relative value (RV) assigned to the treatment harm as compared with the target event prevented. With a fixed harm (A), the absolute risk increase for the harm of treatment is constant. With a variable harm (B), the absolute risk increase for the harm of treatment varies with the baseline risk for the harm. As indicated by the arrow in each panel, the point at which the value-adjusted treatment harm intersects the treatment benefit defines the clinically important difference (CID) for the treatment benefit.

Figure 2

Maximum ARIbleed for treatment to be justified, by CHADS2 score and relative valuestroke/bleed. The scatter plot shows the maximum ARIbleed (%/year) above which warfarin would not be justified, according to the CHADS2 score and different RVbleed/stroke. The horizontal lines depict the predicted ARIbleed with warfarin for each HEMORR2HAGES score. As examples: at RVbleed/stroke 0.6, we would treat CHADS2 score 0 patients only if their predicted ARIbleed given warfarin were less than 2%/year. Accordingly, we would treat HEMORR2HAGES score 0–1 patients because their predicted ARIbleed (1.1, 1.4%/year (table 3)) is less than 2%/year. We would not treat HEMORR2HAGES score ≥2 patients because their predicted ARIbleed (3–7%/year (table 3)) is greater than 2%/year. Again at RVbleed/stroke 0.6, we would treat CHADS2 score 2 patients only if their predicted ARIbleed were less than 4.3%/year. Thus, we would treat HEMORR2HAGES score 0–2 patients because their predicted ARIbleed (1.1–3%/year (table 3)) is less than 4.3%/year. We would not treat HEMORR2HAGES ≥3 patients because their predicted ARIbleed (4.8–7%/year (table 3)) is greater than 4.3%/year. At the RVbleed/stroke set higher or lower than 0.6, fewer patients or more patients, respectively, would be recommended for treatment according to the model. ARI, absolute risk increase; RV, relative value.

Figure 3

Maximum RVbleed/stroke for treatment to be justified, by CHADS2 score and HEMORR2HAGES score. The scatter plot shows the variation of the maximum RVbleed/stroke according to CHADS2 and HEMORR2HAGES (abbreviated as HEMO) scores. The horizontal lines depict three illustrative maximum relative values. The model predicts the maximum RVbleed/stroke to vary over a range between 0.1 (ie, a value assigned to a stroke 10 times higher than that assigned to a major bleeding) and about 10 (ie, a value assigned to a major bleeding 10 times higher than that assigned to a stroke). As examples, the insert zooms in the results for patients with a CHADS2 score of 0–2 and HEMO scores of 0, 2 and 4. Among patients with a CHADS2 score of 0, warfarin would be recommended for HEMO 0 patients if their RVbleed/stroke were <1.1; for HEMO 2 patients, if their RVbleed/stroke were <0.4; for HEMO 4 patients if their RVbleed/stroke were <0.2. For patients with a CHADS2 score of 2, warfarin would be recommended for HEMO 0 patients if their RVbleed/stroke were <2.3; for HEMO 2 patients if their RVbleed/stroke were <0.8; for HEMO 4 patients if their RVbleed/stroke were <0.4. RV, relative value.