|Title||Statistical Methods for Conditional Survival Analysis.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Jung, Sin-Ho, Ho Yun Lee, and Shein-Chung Chow|
|Journal||J Biopharm Stat|
|Keywords||Data Interpretation, Statistical, Humans, Models, Statistical, Proportional Hazards Models, Survival Analysis|
We investigate the survival distribution of the patients who have survived over a certain time period. This is called a conditional survival distribution. In this paper, we show that one-sample estimation, two-sample comparison and regression analysis of conditional survival distributions can be conducted using the regular methods for unconditional survival distributions that are provided by the standard statistical software, such as SAS and SPSS. We conduct extensive simulations to evaluate the finite sample property of these conditional survival analysis methods. We illustrate these methods with real clinical data.
|Alternate Journal||J Biopharm Stat|
|Original Publication||Statistical methods for conditional survival analysis.|
|PubMed Central ID||PMC6195126|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Statistical Methods for Conditional Survival Analysis.