Estimating treatment effects for individual patients based on the results of randomised clinical trials
- Johannes A N Dorresteijn, epidemiologist and medical doctor1,
- Frank L J Visseren, professor of vascular medicine, epidemiologist, and internist1,
- Paul M Ridker, Eugene Braunwald professor of medicine, epidemiologist, and cardiologist2,
- Annemarie M J Wassink, internist and postdoctoral researcher1,
- Nina P Paynter, assistant professor of epidemiology2,
- Ewout W Steyerberg, professor of medical decision making, and methodologist3,
- Yolanda van der Graaf, professor of epidemiology and imaging4,
- Nancy R Cook, associate professor of biostatistics and epidemiology2
1Department of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, Netherlands
2Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
3Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
4Julius Center for Health Sciences and Primary Care, Utrecht, Netherlands
- Correspondence to: F L J Visseren F.L.J.Visseren@umcutrecht.nl
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.