TY - JOUR
T1 - Categorizing a prognostic variable
T2 - Review of methods, code for easy implementation and applications to decision-making about cancer treatments
AU - Mazumdar, Madhu
AU - Glassman, Jill R.
PY - 2000/1/15
Y1 - 2000/1/15
N2 - Categorizing prognostic variables is essential for their use in clinical decision-making. Often a single cutpoint that stratifies patients into high- risk and low-risk categories is sought. These categories may be used for making treatment recommendations, determining study eligibility, or to control for varying patient prognoses in the design of a clinical trial. Methods used to categorize variables include: biological determination (most desirable but often unavailable); arbitrary selection of a cutpoint at the median value; graphical examination of the data for a threshold effect; and exploration of all observed values for the one which best separates the risk groups according to a chi-squared test. The last method, called the minimum p-value approach, involves multiple testing which inflates the type I error rates. Several methods for adjusting the inflated p-values have been proposed but remain infrequently used. Exploratory methods for categorization and the minimum p-value approach with its various p-value corrections are reviewed, and code for their easy implementation is provided. The combined use of these methods is recommended, and demonstrated in the context of two cancer-related examples which highlight a variety of the issues involved in the categorization of prognostic variables.
AB - Categorizing prognostic variables is essential for their use in clinical decision-making. Often a single cutpoint that stratifies patients into high- risk and low-risk categories is sought. These categories may be used for making treatment recommendations, determining study eligibility, or to control for varying patient prognoses in the design of a clinical trial. Methods used to categorize variables include: biological determination (most desirable but often unavailable); arbitrary selection of a cutpoint at the median value; graphical examination of the data for a threshold effect; and exploration of all observed values for the one which best separates the risk groups according to a chi-squared test. The last method, called the minimum p-value approach, involves multiple testing which inflates the type I error rates. Several methods for adjusting the inflated p-values have been proposed but remain infrequently used. Exploratory methods for categorization and the minimum p-value approach with its various p-value corrections are reviewed, and code for their easy implementation is provided. The combined use of these methods is recommended, and demonstrated in the context of two cancer-related examples which highlight a variety of the issues involved in the categorization of prognostic variables.
UR - http://www.scopus.com/inward/record.url?scp=0034650624&partnerID=8YFLogxK
U2 - 10.1002/(SICI)1097-0258(20000115)19:1<113::AID-SIM245>3.0.CO;2-O
DO - 10.1002/(SICI)1097-0258(20000115)19:1<113::AID-SIM245>3.0.CO;2-O
M3 - Article
C2 - 10623917
AN - SCOPUS:0034650624
SN - 0277-6715
VL - 19
SP - 113
EP - 132
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 1
ER -