title: Conditional Survival: A Useful Concept to Provide Information on How Prognosis Evolves over Time.
abstract: Conditional survival (CS) is defined as the probability of surviving further t years, given that a patient has already survived s years after the diagnosis of a chronic disease. It is the simplest form of a dynamic prediction in which other events in the course of the disease or biomarker values measured up to time s can be incorporated. CS has attracted attention in recent years either in an absolute or relative form where the latter is based on a comparison with an age-adjusted normal population being highly relevant from a public health perspective. In its absolute form, CS constitutes the quantity of major interest in a clinical context. Given a clinical cohort of patients with a particular type of cancer, absolute CS can be estimated by conditional Kaplan-Meier estimates in strata defined, for example, by age and disease stage or by a conditional version of the Cox and other regression models for time-to-event data. CS can be displayed as a function of the prediction time s in parametric as well as nonparametric fashion. We illustrate the use of absolute CS in a large clinical cohort of patients with multiple myeloma. For investigating CS, it is necessary to ensure almost complete long-term follow-up of the patients enrolled in the clinical cohort and to consider potential age-stage migration as well as changing treatment modalities over time. CS provides valuable and relevant information on how prognosis develops over time. It also serves as a starting point for identifying factors related to long-term survival.
authors.affiliation: Institute for Medical Biometry and Statistics, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany.
authors.forename: Stefanie
authors.initials: S
authors.lastname: Hieke
authors.affiliation: Department of Medicine I, Hematology, Oncology and Stem Cell Transplantation, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany. Clinic for Internal Medicine, University Hospital Basel, Basel, Switzerland.
authors.forename: Martina
authors.initials: M
authors.lastname: Kleber
authors.affiliation: Department of Medicine I, Hematology, Oncology and Stem Cell Transplantation, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany.
authors.forename: Christine
authors.initials: C
authors.lastname: König
authors.affiliation: Department of Medicine I, Hematology, Oncology and Stem Cell Transplantation, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany.
authors.forename: Monika
authors.initials: M
authors.lastname: Engelhardt
authors.affiliation: Institute for Medical Biometry and Statistics, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany. ms@imbi.uni-freiburg.de.
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D000368:Aged; D015987:Effect Modifier, Epidemiologic; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D009101:Multiple Myeloma / Q000401:mortality*; D016016:Proportional Hazards Models