Abstract
Objectives To describe the use of risk-difference curves for communicating time-dependent absolute treatment effects. Study Design and Setting Three examples based on individual patient data meta-analyses for adjuvant treatments for early-stage breast cancer are presented. Unit record datasets were re-created from the published Kaplan-Meier curves and numbers at risk or person-years at risk. Risk-difference curves, with corresponding 95% confidence bands, are presented and discussed. Results Risk-difference curves are useful for communicating the results from trials of adjuvant treatments for early-stage cancer when standard measures of the absolute treatment effect for survival data (ie, difference-in-mean and difference-in-median survival) can be difficult to estimate. They also avoid the problem of "evolving selection bias", which can affect interval-specific hazard ratio (HR)s in trials with long follow-up and where the participants are heterogeneous with respect to prognosis. Conclusion Clinical epidemiologists should consider reporting risk-difference curves in addition to Kaplan-Meier curves and the HR.
Original language | English |
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Pages (from-to) | 966-972 |
Number of pages | 7 |
Journal | Journal of Clinical Epidemiology |
Volume | 67 |
Issue number | 9 |
DOIs | |
Publication status | Published - Sept 2014 |
Keywords
- Absolute treatment effect
- Difference-in-mean survival
- Difference-in-median survival
- Hazard ratio
- Kaplan-Meier curves
- Relative treatment effect
- Risk-difference curve
- Survival
- Time-dependent treatment effect