Flexible modeling of longitudinal health-related quality of life data accounting for informative dropout in a cancer clinical trial

Winter, Audrey; Cuer, Benjamin; Conroy, Thierry; Juzyna, Béata; Gourgou, Sophie; Mollévi, Caroline; Touraine, Célia

doi:10.1007/s11136-022-03252-6

Cited by 2 publications

(3 citation statements)

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“…Alongside PFS, patient quality of life is an important consideration when testing new treatments. Winter et al 27 have developed a joint modeling approach for linking patient dropout to health‐related quality of life data. Based on our data, we have assumed that dropout follows a Weibull distribution.…”

Section: Discussionmentioning

confidence: 99%

“…This is accomplished by joining a tumor growth inhibition (TGI) model to a time‐to‐event (TTE) model for non‐target progression (NTP). An additional TTE model is also developed to account for patient dropout 27 . The models are calibrated to clinical data gathered from the PRIME study where FOLFOX was tested against FOLFOX + panitumumab 28 .…”

Section: Introductionmentioning

confidence: 99%

“…An additional TTE model is also developed to account for patient dropout. 27 The models are calibrated to clinical data gathered from the PRIME study where FOLFOX was tested against FOLFOX + panitumumab. 28 Second, to test the PFS model's predictive capabilities by both predicting patients' future PFS as well as predicting the PFS for a different study.…”

Section: Introductionmentioning

confidence: 99%

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Model‐based prediction of progression‐free survival for combination therapies in oncology

Baaz

Cardilin

Jirstrand

2023

CPT Pharmacom & Syst Pharma

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Progression‐free survival (PFS) is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. After the completion of a clinical trial, a descriptive analysis of the patients' PFS is often performed post hoc using the Kaplan–Meier estimator. However, to perform predictions, more sophisticated quantitative methods are needed. Tumor growth inhibition models are commonly used to describe and predict the dynamics of preclinical and clinical tumor size data. Moreover, frameworks also exist for describing the probability of different types of events, such as tumor metastasis or patient dropout. Combining these two types of models into a so‐called joint model enables model‐based prediction of PFS. In this paper, we have constructed a joint model from clinical data comparing the efficacy of FOLFOX against FOLFOX + panitumumab in patients with metastatic colorectal cancer. The nonlinear mixed effects framework was used to quantify interindividual variability (IIV). The model describes tumor size and PFS data well, and showed good predictive capabilities using truncated as well as external data. A machine‐learning guided analysis was performed to reduce unexplained IIV by incorporating patient covariates. The model‐based approach illustrated in this paper could be useful to help design clinical trials or to determine new promising drug candidates for combination therapy trials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model‐based prediction of progression‐free survival for combination therapies in oncology

Baaz

Cardilin

Jirstrand

2023

CPT Pharmacom & Syst Pharma

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When a joint model should be preferred over a linear mixed model for analysis of longitudinal health-related quality of life data in cancer clinical trials

Touraine

Cuer

Conroy

et al. 2023

BMC Med Res Methodol

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Background Patient-reported outcomes such as health-related quality of life (HRQoL) are increasingly used as endpoints in randomized cancer clinical trials. However, the patients often drop out so that observation of the HRQoL longitudinal outcome ends prematurely, leading to monotone missing data. The patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. In case of informative dropout, the usual linear mixed model analysis will produce biased estimates. Unbiased estimates cannot be obtained unless the dropout is jointly modeled with the longitudinal outcome, for instance by using a joint model composed of a linear mixed (sub)model linked to a survival (sub)model. Our objective was to investigate in a clinical trial context the consequences of using the most frequently used linear mixed model, the random intercept and slope model, rather than its corresponding joint model. Methods We first illustrate and compare the models on data of patients with metastatic pancreatic cancer. We then perform a more formal comparison through a simulation study. Results From the application, we derived hypotheses on the situations in which biases arise and on their nature. Through the simulation study, we confirmed and complemented these hypotheses and provided general explanations of the bias mechanisms. Conclusions In particular, this article reveals how the linear mixed model fails in the typical situation where poor HRQoL is associated with an increased risk of dropout and the experimental treatment improves survival. Unlike the joint model, in this situation the linear mixed model will overestimate the HRQoL in both arms, but not equally, misestimating the difference between the HRQoL trajectories of the two arms to the disadvantage of the experimental arm.

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Flexible modeling of longitudinal health-related quality of life data accounting for informative dropout in a cancer clinical trial

Cited by 2 publications

References 20 publications

Model‐based prediction of progression‐free survival for combination therapies in oncology

Model‐based prediction of progression‐free survival for combination therapies in oncology

When a joint model should be preferred over a linear mixed model for analysis of longitudinal health-related quality of life data in cancer clinical trials

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