%TTD and %TUDD: New SAS macro programs to calculate the survival data of the time to deterioration for patient-reported outcomes data in oncology.

BACKGROUND AND OBJECTIVE: Longitudinal analysis of patient-reported outcome (PRO) data remains challenging, as no standardization of statistical methods has been proposed, making comparison of PRO results between clinical trials difficult. In this context, the time to deterioration approach has recently been proposed and is regularly used as a modality of longitudinal PRO analysis in oncology. METHODS: Two new SAS macro programs were developed, %TTD and %TUDD, which implement longitudinal analysis of PRO data according to the time to deterioration approach. These programs implement the recommended deterioration definitions. We described the programs with their different functionalities. RESULTS: The %TTD macro calculates the time to first or transient deterioration, and the %TUDD macro calculates the time until definitive deterioration. These macros allow to obtain the survival variables from the time to deterioration approach. We illustrate our programs by presenting different applications on the randomized phase II AFUGEM GERCOR clinical trial. CONCLUSION: The implementation of the deterioration definitions in SAS software allows the dissemination of this approach, in order to move toward the goal of standardization of longitudinal PRO analysis in oncology clinical trials.

Handling informative dropout in longitudinal analysis of health-related quality of life: application of three approaches to data from the esophageal cancer clinical trial PRODIGE 5/ACCORD 17.

BACKGROUND: Health-related quality of life (HRQoL) has become a major endpoint to assess the clinical benefit of new therapeutic strategies in oncology clinical trials. Typically, HRQoL outcomes are analyzed using linear mixed models (LMMs). However, longitudinal analysis of HRQoL in the presence of missing data remains complex and unstandardized. Our objective was to compare the modeling alternatives that account for informative dropout. METHODS: We investigated three alternative methods-the selection model (SM), pattern-mixture model (PMM), and shared-parameters model (SPM)-in relation to the LMM. We first compared them on the basis of methodological arguments highlighting their advantages and drawbacks. Then, we applied them to data from a randomized clinical trial that included 267 patients with advanced esophageal cancer for the analysis of four HRQoL dimensions evaluated using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire. RESULTS: We highlighted differences in terms of outputs, interpretation, and underlying modeling assumptions; this methodological comparison could guide the choice of method according to the context. In the application, none of the four models detected a significant difference between the two treatment arms. The estimated effect of time on HRQoL varied according to the method: for all analyzed dimensions, the PMM estimated an effect that contrasted with those estimated by the SM and SPM; the LMM estimated effects were confirmed by the SM (on two of four HRQoL dimensions) and SPM (on three of four HRQoL dimensions). CONCLUSIONS: The PMM, SM, or SPM should be used to confirm or invalidate the results of LMM analysis when informative dropout is suspected. Of these three alternative methods, the SPM appears to be the most interesting from both theoretical and practical viewpoints. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov , number NCT00861094 .

GUIP1: a R package for dose escalation strategies in phase I cancer clinical trials.

BACKGROUND: The main objective of phase I cancer clinical trials is to identify the maximum tolerated dose, usually defined as the highest dose associated with an acceptable level of severe toxicity during the first cycle of treatment. Several dose-escalation designs based on mathematical modeling of the dose-toxicity relationship have been developed. The main ones are: the continual reassessment method (CRM), the escalation with overdose control (EWOC) method and, for late-onset and cumulative toxicities, the time-to-event continual reassessment method (TITE-CRM) and the time-to-event escalation with overdose control (TITE-EWOC) methods. The objective of this work was to perform a user-friendly R package that combines the latter model-guided adaptive designs. RESULTS: GUIP1 is an R Graphical User Interface for dose escalation strategies in Phase 1 cancer clinical trials. It implements the CRM (based on Bayesian or maximum likelihood estimation), EWOC and TITE-CRM methods using the dfcrm and bcrm R packages, while the TITE-EWOC method has been specifically developed. The program is built using the TCL/TK programming language, which can be compiled via R software libraries (tcltk, tkrplot, tcltk2). GUIP1 offers the possibility of simulating and/or conducting and managing phase I clinical trials in real-time using file management options with automatic backup of study and/or simulation results. CONCLUSIONS: GUIP1 is implemented using the software R, which is widely used by statisticians in oncology. This package simplifies the use of the main model-based dose escalation methods and is designed to be fairly simple for beginners in R. Furthermore, it offers multiple possibilities such as a full traceability of the study. By including multiple innovative adaptive methods in a free and user-friendly program, we hope that GUIP1 will promote and facilitate their use in designing future phase I cancer clinical trials.

Time to deterioration in cancer randomized clinical trials for patient-reported outcomes data: a systematic review.

PURPOSE: The time to deterioration (TTD) approach has been proposed as a modality of longitudinal analysis of patient-reported outcomes (PROs) in cancer randomized clinical trials (RCTs). The objective of this study was to perform a systematic review of how the TTD approach has been used in phase III RCTs to analyze longitudinal PRO data. METHODS: A systematic literature search was conducted in PubMed/MEDLINE, the Cochrane Library and through manual search to identify studies published between January 2014 and June 2018. All phase III cancer RCTs including a PRO endpoint using the TTD approach were considered. We collected general information about the study, PRO assessment and the TTD approach, such as the event definition, the choice of reference score and whether the deterioration was definitive or not. RESULTS: A total of 1549 articles were screened, and 39 studies were finally identified as relevant according to predefined criteria. Among these 39 studies, 36 (92.3%) were in advanced and/or metastatic cancer. Several different deterioration definitions were used in RCTs, 10 studies (25.6%) defined the deterioration as "definitive", corresponding to a deterioration maintained over time until the last PRO assessment available for each patient. The baseline score was explicitly stated as the reference score to qualify the deterioration for most studies (n = 31, 79.5%). CONCLUSION: This review highlights the lack of standardization of the TTD approach for the analysis of PRO data in RCTs. Special attention should be paid to the definition of "deterioration", and this should be based on the specific cancer setting.

More accurate cancer-related excess mortality through correcting background mortality for extra variables.

Relative survival methods used to estimate the excess mortality of cancer patients rely on the background (or expected) mortality derived from general population life tables. These methods are based on splitting the observed mortality into the excess mortality and the background mortality. By assuming a regression model for the excess mortality, usually a Cox-type model, one may investigate the effects of certain covariates on the excess mortality. Some covariates are cancer-specific whereas others are variables that may influence the background mortality as well. The latter should be taken into account in the background mortality to avoid biases in estimating their effects on the excess mortality. Unfortunately, the available life table might not include such variables and, consequently, might provide inaccurate values of the background mortality. We propose a model that uses multiplicative parameters to correct potentially inaccurate background mortality. The model can be seen as an extension of the frequently used Estève model because we assume a Cox-type model for the excess mortality with a piecewise constant baseline function and introduce additional parameters that multiply the background mortality. The original and the extended model are compared, first in a simulation study, then in an application to colon cancer registry data.