Estimating treatment effects for individual patients based on the results of randomised clinical trials

Abstract

Objectives: To predict treatment effects for individual patients based on data from randomised trials, taking rosuvastatin treatment in the primary prevention of cardiovascular disease as an example, and to evaluate the net benefit of making treatment decisions for individual patients based on a predicted absolute treatment effect.

Setting: As an example, data were used from the Justification for the Use of Statins in Prevention (JUPITER) trial, a randomised controlled trial evaluating the effect of rosuvastatin 20 mg daily versus placebo on the occurrence of cardiovascular events (myocardial infarction, stroke, arterial revascularisation, admission to hospital for unstable angina, or death from cardiovascular causes). Population 17,802 healthy men and women who had low density lipoprotein cholesterol levels of less than 3.4 mmol/L and high sensitivity C reactive protein levels of 2.0 mg/L or more.

Methods: Data from the Justification for the Use of Statins in Prevention trial were used to predict rosuvastatin treatment effect for individual patients based on existing risk scores (Framingham and Reynolds) and on a newly developed prediction model. We compared the net benefit of prediction based rosuvastatin treatment (selective treatment of patients whose predicted treatment effect exceeds a decision threshold) with the net benefit of treating either everyone or no one.

Results: The median predicted 10 year absolute risk reduction for cardiovascular events was 4.4% (interquartile range 2.6-7.0%) based on the Framingham risk score, 4.2% (2.5-7.1%) based on the Reynolds score, and 3.9% (2.5-6.1%) based on the newly developed model (optimal fit model). Prediction based treatment was associated with more net benefit than treating everyone or no one, provided that the decision threshold was between 2% and 7%, and thus that the number willing to treat (NWT) to prevent one cardiovascular event over 10 years was between 15 and 50.

Conclusions: Data from randomised trials can be used to predict treatment effect in terms of absolute risk reduction for individual patients, based on a newly developed model or, if available, existing risk scores. The value of such prediction of treatment effect for medical decision making is conditional on the NWT to prevent one outcome event. Trial registration number Clinicaltrials.gov NCT00239681.

Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: PMR is the principal investigator of the investigator initiated Justification for the Use of Statins in Prevention trial, which was funded by AstraZeneca (Wilmington, Delaware). PMR received grant support from Novartis and Roche; consulting fees from Siemens Medical Systems, ISIS, and Vascular Biogenetics; and is listed as a co-inventor on patents held by the Brigham and Women’s Hospital that relate to the use of inflammatory biomarkers in cardiovascular disease that have been licensed to Siemens Medical Systems (Erlangen, Germany) and AstraZeneca. FLJV’s department receives grant support from Merck, the Netherlands Organisation for Health Research and Development, and the Catharijne Foundation Utrecht; and speaker fees from Merck and AstraZeneca. JAND, AMJW, NPP, EWS, YvdG, and NRC have no relationships with industry that might have an interest in the submitted work in the previous three years. All authors have no non-financial interests that may be relevant to the submitted work.

Figures

Fig 1 Basic concept for weighing treatment effect against harm. Treatment effect usually increases with baseline risk, whereas harm is relatively constant for all patients. Those whose treatment effect exceeds treatment related harm (reflected by decision threshold) benefit from treatment

Fig 2 Calibration plots. Predicted and observed two year event free survival for cardiovascular events within 10ths of predicted survival using three models. P values derived from the Hosmer-Lemeshow test

Fig 3 Distribution of predicted 10 year absolute treatment effect (absolute risk reduction) based on Framingham risk score, Reynolds risk score, and optimal fit model, with coloured bars indicating predicted treatment effects for two different patient scenarios. JUPITER=the Justification for the Use of Statins in Prevention trial

Fig 4 Decision curve: graphical representation of net benefit. For large values of numbers willing to treat (NWT), the net benefit of treating all patients is about equal to the net benefit of prediction based treatment. The net benefit of treating all patients becomes negative if the NWT is less than 20, whereas the net benefit of prediction based treatment is still positive for a NWT of 20 and converges to zero for smaller values of NWT

Fig 5 Implications for clinical practice. Justification for the Use of Statins in Prevention trial shows that treatment of all patients is the strategy of choice if the 10 year number willing to treat (NWT) is 50 or more. Treating no one is preferable if the 10 year NWT is 15 or fewer. If the NWT is between 15 and 50, prediction based treatment results in most net benefit