Muditha Perera1, Alok Kumar Dwivedi1,2. 1. Division of Biostatistics & Epidemiology, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, Texas. 2. Biostatistics and Epidemiology Consulting Lab, Office of the Vice President for Research, Texas Tech University Health Sciences Center El Paso, El Paso, Texas.
Abstract
BACKGROUND: Cancer studies that are designed for early detection and screening, or used for identifying prognostic factors, or assessing treatment efficacy and health outcome are frequently assessed with survival or time-to-event outcomes. These studies typically require specific methods of data analysis. Appropriate statistical methods in the context of study design and objectives are required for obtaining reliable results and valid inference. Unfortunately, variable methods for the same study objectives and dubious reporting have been noticed in the survival analysis of oncology research. Applied researchers often face difficulties in selecting appropriate statistical methods due to the complex nature of cancer studies. RECENT FINDINGS: In this report, we describe briefly major statistical issues along with related challenges in planning, designing, and analyzing of survival studies. For applied researchers, we provided flow charts for selecting appropriate statistical methods. Various available statistical procedures in common statistical packages for applying survival analysis were classified according to different objectives of the study. In addition, an illustration of the statistical analysis of some common types of time-to-event outcomes was shown with STATA codes. CONCLUSIONS: We anticipate that this review article assists oncology researchers in understanding important statistical concepts involved in survival analysis and appropriately select the statistical approaches for survival analysis studies. Overall, the review may help in improving designing, conducting, analyzing, and reporting of data in survival studies.
BACKGROUND:Cancer studies that are designed for early detection and screening, or used for identifying prognostic factors, or assessing treatment efficacy and health outcome are frequently assessed with survival or time-to-event outcomes. These studies typically require specific methods of data analysis. Appropriate statistical methods in the context of study design and objectives are required for obtaining reliable results and valid inference. Unfortunately, variable methods for the same study objectives and dubious reporting have been noticed in the survival analysis of oncology research. Applied researchers often face difficulties in selecting appropriate statistical methods due to the complex nature of cancer studies. RECENT FINDINGS: In this report, we describe briefly major statistical issues along with related challenges in planning, designing, and analyzing of survival studies. For applied researchers, we provided flow charts for selecting appropriate statistical methods. Various available statistical procedures in common statistical packages for applying survival analysis were classified according to different objectives of the study. In addition, an illustration of the statistical analysis of some common types of time-to-event outcomes was shown with STATA codes. CONCLUSIONS: We anticipate that this review article assists oncology researchers in understanding important statistical concepts involved in survival analysis and appropriately select the statistical approaches for survival analysis studies. Overall, the review may help in improving designing, conducting, analyzing, and reporting of data in survival studies.
Authors: Jack Cuzick; Ivana Sestak; Simon Cawthorn; Hisham Hamed; Kaija Holli; Anthony Howell; John F Forbes Journal: Lancet Oncol Date: 2014-12-11 Impact factor: 41.316