Joint Analysis of Longitudinal Ordinal Categorical Item Response Data and Survival Times with Cure Fraction

Abstract–For longitudinal ordinal categorical item response data which may not be observable after a subject develops a terminal event, some statistical models have been proposed for the joint analysis of the longitudinal item responses and times to the development of a terminal event denoted as the survival times. All of these models used an accelerated failure time or Cox proportional model for the survival times, which may not be suitable when some of the subjects may be considered as cured and may, therefore, never develop an event. In this paper, we propose a new joint model which uses a promotion time cure model for the survival times. Statistical procedures are developed for the inference of the parameters in the model. The proposed model and inference procedures are assessed through a simulation study and application to data from a randomized clinical trial for patients with early breast cancer.KEYWORDS: Item responsesMultilevel modelsPromotion time cure modelAdaptive Gaussian-Hermite quadratureDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. FundingThe author(s) reported there is no funding associated with the work featured in this article.