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
Abstract 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 missi...
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-023-01846-3 |
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| author | Célia Touraine Benjamin Cuer Thierry Conroy Beata Juzyna Sophie Gourgou Caroline Mollevi |
| author_facet | Célia Touraine Benjamin Cuer Thierry Conroy Beata Juzyna Sophie Gourgou Caroline Mollevi |
| author_sort | Célia Touraine |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-10T15:43:03Z |
| format | Article |
| id | doaj.art-2fb623bd4c5749f4957ebbb7bcc2f139 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-04-10T15:43:03Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-2fb623bd4c5749f4957ebbb7bcc2f1392023-02-12T12:15:27ZengBMCBMC Medical Research Methodology1471-22882023-02-0123111510.1186/s12874-023-01846-3When a joint model should be preferred over a linear mixed model for analysis of longitudinal health-related quality of life data in cancer clinical trialsCélia Touraine0Benjamin Cuer1Thierry Conroy2Beata Juzyna3Sophie Gourgou4Caroline Mollevi5Biometrics Unit, Cancer Institute of Montpellier, University of MontpellierBiometrics Unit, Cancer Institute of Montpellier, University of MontpellierDepartment of Medical Oncology, Institut de cancérologie de LorraineR&D UnicancerBiometrics Unit, Cancer Institute of Montpellier, University of MontpellierBiometrics Unit, Cancer Institute of Montpellier, University of MontpellierAbstract 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.https://doi.org/10.1186/s12874-023-01846-3Joint modelInformative dropoutLinear mixed modelRandom intercept and slope modelHealth-related quality of lifeLongitudinal outcome |
| spellingShingle | Célia Touraine Benjamin Cuer Thierry Conroy Beata Juzyna Sophie Gourgou Caroline Mollevi 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 BMC Medical Research Methodology Joint model Informative dropout Linear mixed model Random intercept and slope model Health-related quality of life Longitudinal outcome |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Joint model Informative dropout Linear mixed model Random intercept and slope model Health-related quality of life Longitudinal outcome |
| url | https://doi.org/10.1186/s12874-023-01846-3 |
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