Michael Coory1, Karen E Lamb2, Michael Sorich3. 1. Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, Melbourne, Victoria 3052, Australia; Department of Paediatrics, Royal Children's Hospital, Flemington Road, Parkville, Melbourne, Victoria 3052, Australia. 2. Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, Melbourne, Victoria 3052, Australia; Department of Paediatrics, Royal Children's Hospital, Flemington Road, Parkville, Melbourne, Victoria 3052, Australia. Electronic address: karen.lamb@mcri.edu.au. 3. Department of Clinical Pharmacology, School of Medicine, Flinders University, Bedford Park, South Australia 5042, Australia.
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. Crown
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. Crown
Authors: Joana T de Oliveira; Ana L Santos; Catarina Gomes; Rita Barros; Cláudia Ribeiro; Nuno Mendes; Augusto J de Matos; M Helena Vasconcelos; Maria José Oliveira; Celso A Reis; Fátima Gärtner Journal: PLoS One Date: 2015-04-07 Impact factor: 3.240
Authors: György Rokszin; Zoltán Kiss; Gábor Sütő; Péter Kempler; György Jermendy; Ibolya Fábián; Zoltán Szekanecz; Gyula Poór; István Wittmann; Gergő Attila Molnár Journal: Front Oncol Date: 2021-10-28 Impact factor: 6.244