Original Article Medical Decision Making 1–11 Ó The Author(s) 2019 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0272989X19832879 journals.sagepub.com/home/mdm Predicting Difference in Mean Survival Time from Reported Hazard Ratios for Cancer Patients Eeva-Liisa Røssell Johansen , Mette Lise Lousdal, Mette Vinther Skriver, Michael Væth, Ivar Sønbø Kristiansen, and Henrik Støvring Background. Gain in mean survival time from new cancer treatments is a core component of cost-effectiveness analy- ses frequently used by payers for reimbursement decisions. Due to limited follow-up time, clinical trials rarely report this measure, whereas they often report hazard ratios comparing treatment groups. Aim. We aimed to explore the empirical relationship between gain in mean survival time and the hazard ratio for cancer patients. Methods. We included all patients in Norway diagnosed from 1965 through 2004 with late-stage cancer at the point of diagnosis and with one of the following cancers: stomach, colon, rectal, pancreas, lung and trachea, kidney excluding renal pel- vis, and metastasized breast and prostate. Patients were followed until emigration, death, or June 30, 2016, whichever came first. Observed mean survival times and hazard ratios were obtained in subcohorts defined by patients’ sex, age, cancer type, and time period of diagnosis, which had nearly complete follow-up. Based on theoretical considera- tions, we fitted a linear relationship between observed differences in mean survival and logarithmic hazard ratios. For validation, we estimated differences in mean survival from hazard ratios of bootstrap samples with artificially induced censoring and compared with fitting a Weibull distribution. Results. The relationship between differences in mean survival time and corresponding logarithmic hazard ratios was linear for each of the included cancers. The pre- dicted differences in mean survival of the empirical approach generally had smaller bias than the Weibull approach. Conclusion. For cancer diagnoses with poor prognosis, differences in mean survival times could be predicted from corresponding hazard ratios. This hazard ratio–based approach outperforms or is similar to fitting Weibull models to data with incomplete follow-up, while making fewer assumptions. Keywords oncology, population-based studies, survival analysis, hazard ratios, cost-effectiveness analysis Date received: March 16, 2018; accepted: January 29, 2019 A range of new cancer therapies improves survival of patients, but valid estimation of gain in mean survival time from clinical trials remains challenging. The gain in mean survival, however, is a key measure in effectiveness analyses that payers increasingly use in reimbursement decisions. Clinical trials form the primary evidence base in the evaluation of such drugs, where survival times are compared in 2 or more randomized groups receiving dif- ferent treatments. Trials, however, are discontinued before all patients have died, and thus survival times will be right censored for patients surviving the follow-up period. This complicates estimation of mean survival time after start of treatment, particularly because the Department of Public Health, Biostatistics, Aarhus University, Aarhus C, Denmark (ELRJ, MLL, MV, HS); Department of Public Health, Health Promotion and Health Services, Aarhus University, Aarhus C, Denmark (MVS); and Department of Health Management and Health Economics, University of Oslo, Oslo, Norway (ISK). The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The author(s) received no financial support for the research, authorship, and/or publication of this article. Corresponding Author Eeva-Liisa Røssell Johansen, Department of Public Health, Aarhus University, Bartholins Alle´ 2, 8000 Aarhus C, Denmark (el-johansen@ph.au.dk).