Emilie Bogart1, Anaïs Jouin2, Hélène Béhal3, Alain Duhamel3, Thomas Filleron4, Andrew Kramar2. 1. Methodology and Biostatistics Unit, Centre Oscar Lambret, Lille and SIRIC ONCOLille, France. Electronic address: scientifique@o-lambret.fr. 2. Methodology and Biostatistics Unit, Centre Oscar Lambret, Lille and SIRIC ONCOLille, France. 3. Methodology and Biostatistics Unit, EA2694, UDSL2, Univ Lille Nord de France, CHRU Lille and SIRIC ONCO Lille, France. 4. Institut Claudius Regaud, IUCT-O, Bureau des Essais Cliniques, Cellule Biostatistique, Toulouse F-31059, France.
Abstract
OBJECTIVE: The analysis of treatment effects in clinical trials usually focus on efficacy and safety in separate descriptive statistical analyses. The Q-TWiST (Quality adjusted Time Without Symptoms and Toxicity) method has been proposed by Gelber in the 90s to enable a statistical comparison between two groups with a graphical representation by incorporating benefit and risk into a single analysis. Although the method has been programmed in SAS, it is rarely used. The availability of the method in the freely software environment system like R would greatly enhanced the accessibility by researchers. The objective of this paper is to present a program for Q-TWiST analyses within R software environment. METHODS: The qtwist function was developed in order to estimate and compare Q-TWiST for two groups. Two individual patient data files are required used for input: one for visits and one for follow-up. Q-TWiST is obtained as a sum of time spent in three health states: period in toxicity (TOX), period without relapse and toxicity (TWiST) and period in relapse (REL), weighted by associated utility scores restricted to median overall survival for example. The bootstrap method is used for testing statistical significance. Threshold analysis and gain functions allow a group comparison for different utility values. RESULTS: Input data is checked for consistency. Descriptive statistics and mean durations for each health state are provided, allowing statistical comparisons. Graphical results are presented in a PDF file. The use of the function is illustrated with data from a simulated data set and a randomized clinical trial. CONCLUSIONS: qtwist is an easy to use R function, allowing a quality adjusted survival analysis with the Q-TWiST method.
OBJECTIVE: The analysis of treatment effects in clinical trials usually focus on efficacy and safety in separate descriptive statistical analyses. The Q-TWiST (Quality adjusted Time Without Symptoms and Toxicity) method has been proposed by Gelber in the 90s to enable a statistical comparison between two groups with a graphical representation by incorporating benefit and risk into a single analysis. Although the method has been programmed in SAS, it is rarely used. The availability of the method in the freely software environment system like R would greatly enhanced the accessibility by researchers. The objective of this paper is to present a program for Q-TWiST analyses within R software environment. METHODS: The qtwist function was developed in order to estimate and compare Q-TWiST for two groups. Two individual patient data files are required used for input: one for visits and one for follow-up. Q-TWiST is obtained as a sum of time spent in three health states: period in toxicity (TOX), period without relapse and toxicity (TWiST) and period in relapse (REL), weighted by associated utility scores restricted to median overall survival for example. The bootstrap method is used for testing statistical significance. Threshold analysis and gain functions allow a group comparison for different utility values. RESULTS: Input data is checked for consistency. Descriptive statistics and mean durations for each health state are provided, allowing statistical comparisons. Graphical results are presented in a PDF file. The use of the function is illustrated with data from a simulated data set and a randomized clinical trial. CONCLUSIONS: qtwist is an easy to use R function, allowing a quality adjusted survival analysis with the Q-TWiST method.
Authors: Quynh-Nhu Nguyen; Stephen G Chun; Edward Chow; Ritsuko Komaki; Zhongxing Liao; Rensi Zacharia; Bill K Szeto; James W Welsh; Stephen M Hahn; C David Fuller; Bryan S Moon; Justin E Bird; Robert Satcher; Patrick P Lin; Melenda Jeter; Michael S O'Reilly; Valerae O Lewis Journal: JAMA Oncol Date: 2019-06-01 Impact factor: 31.777