Pseudo-observations in survival analysis.
We review recent work on the application of pseudo-observations in survival and event history analysis. This includes regression models for parameters like the survival function in a single point, the restricted mean survival time and transition or state occupation probabilities in multi-state models, e.g. the competing risks cumulative incidence function. Graphical and numerical methods for assessing goodness-of-fit for hazard regression models and for the Fine-Gray model in competing risks studies based on pseudo-observations are also reviewed. Sensitivity to covariate-dependent censoring is studied. The methods are illustrated using a data set from bone marrow transplantation.
Andersen
Per Kragh
PK
Department of Biostatistics, University of Copenhagen, Copenhagen K, Denmark. P.K.Andersen@biostat.ku.dk
Perme
Maja Pohar
MP
D000328:Adult; D016026:Bone Marrow Transplantation / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000401:mortality / Q000601:surgery; D008297:Male; D008875:Middle Aged; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000401:mortality / Q000601:surgery; D016016:Proportional Hazards Models; D012044:Regression Analysis; D018570:Risk Assessment / Q000706:statistics & numerical data; D016019:Survival Analysis; D015996:Survival Rate; D055815:Young Adult