@article{fdi:010093672,
  title = {{W}hen 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},
  author = {{T}ouraine, {C}. and {C}uer, {B}enjamin and {C}onroy, {T}. and {J}uzyna, {B}. and {G}ourgou, {S}. and {M}ollevi, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {P}atient-reported outcomes such as health-related quality of life ({HRQ}o{L}) are increasingly used as endpoints in randomized cancer clinical trials. {H}owever, the patients often drop out so that observation of the {HRQ}o{L} longitudinal outcome ends prematurely, leading to monotone missing data. {T}he patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. {I}n case of informative dropout, the usual linear mixed model analysis will produce biased estimates. {U}nbiased 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. {O}ur 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. {M}ethods: {W}e first illustrate and compare the models on data of patients with metastatic pancreatic cancer. {W}e then perform a more formal comparison through a simulation study. {R}esults: {F}rom the application, we derived hypotheses on the situations in which biases arise and on their nature. {T}hrough the simulation study, we confirmed and complemented these hypotheses and provided general explanations of the bias mechanisms. {C}onclusions {I}n particular, this article reveals how the linear mixed model fails in the typical situation where poor {HRQ}o{L} is associated with an increased risk of dropout and the experimental treatment improves survival. {U}nlike the joint model, in this situation the linear mixed model will overestimate the {HRQ}o{L} in both arms, but not equally, misestimating the difference between the {HRQ}o{L} trajectories of the two arms to the disadvantage of the experimental arm.},
  keywords = {},
  booktitle = {},
  journal = {{BMC} {M}edical {R}esearch {M}ethodology},
  volume = {23},
  numero = {1},
  pages = {36 [15 ]},
  ISSN = {1471-2288},
  year = {2023},
  DOI = {10.1186/s12874-023-01846-3},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093672},
}