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Joint Modeling of Longitudinal and Survival Data with a Cure Fraction
Published in Yingwei Peng, Binbing Yu, Cure Models, 2021
Longitudinal data consist of repeated outcome measurements from the same subjects over time and are frequently collected in medical and social science studies along with time to event data from the subjects. Even though longitudinal data and time to event data can be analyzed separately by existing statistical models, a joint analysis of the longitudinal and survival data becomes very popular in recent years because it may provide more efficient estimation than the separate analyses when longitudinal data and survival data are highly correlated (Ibrahim et al., 2010; Rizopoulos, 2012; Gould et al., 2015). In this chapter, we will introduce some methods for a joint analysis of longitudinal data and survival data when some subjects are cured and thus a cured fraction has to be properly taken into account in the joint analysis. The chapter is organized as follows: Section 5.2 introduces examples of longitudinal data and survival data from the same group of study subjects and notations that will be used in this chapter. Section 5.3 and 5.4 discuss a shared random effects approach to jointly model longitudinal continuous and proportional data and survival data with a cured fraction. Section 5.5 presents another approach for the joint model by including the trajectory of the longitudinal model in the cure model for survival data. Finally, applications of some joint models to data from in a breast cancer clinical trial are given in Section 5.6.
Bayesian Methods for Longitudinal Data with Missingness
Published in Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger, Bayesian Methods in Pharmaceutical Research, 2020
There is extensive literature on approaches for the analysis of longitudinal data. The two major features of longitudinal data that need to be addressed in modeling include the fact that repeated observations on the same unit over time are correlated and that it is typical that the complete vector of longitudinal responses is not collected on all subjects. The approaches for modeling longitudinal data can be dichotomized into non-likelihood-based and likelihood-based.
Clustered Data
Published in Peter Cummings, Analysis of Incidence Rates, 2019
A common type of clustered data arises if there are repeated observations on the same person or the same geographic area. For example, we may have state-level annual mortality rates for several years. The past event rate for an individual, community, or institution is often related to their current event rates. Repeated observations over time are often called longitudinal data. This is sometimes called cross-sectional panel data: the panels are the clusters that are followed over time, the cross-sections are the data at one point in time. Clustering of rates over time will be discussed in the next chapter.
Rates of discontinuation and non-publication of trials for the pharmacologic treatment of alcohol use disorder
Published in Substance Abuse, 2022
Micah Hartwell, Nicholas B. Sajjadi, Samuel Shepard, John Whelan, Jamie Roberts, Alicia Ito Ford, Jason Beaman, Matt Vassar
Clinical trials (CTs) provide longitudinal data of the feasibility, efficacy, and safety for AUD interventions, equipping researchers with critical evidence for clinical application. As such, ensuring the high-quality design and implementation of CTs, while absolutely vital, is only half of the battle; insufficient access to trial results remains a serious threat to the advancement of scientific research in many fields6–8. Lack of access to trial results is a significant waste of resources and is thought to contribute to the estimated 85% of all research that is wasted.9 Patients participating in CTs willingly subject themselves to a degree of risk in the hopes of receiving novel effective treatment and of advancing what is known to potentially help others down the road—a sacrifice that is thwarted should the outcomes of a trial remain unknown and unusable. There are two significant factors contributing to insufficient CT result access: discontinuation of initiated studies and non-publication of completed studies.
Conditional standards for the quantification of foetal growth in an ethnic Chinese population: a longitudinal study
Published in Journal of Obstetrics and Gynaecology, 2022
Jian Jiang, Xiaodan Zhu, Linyu Zhou, Shanyu Yin, Weilian Feng, Tian’an Jiang
There are two types of raw data that can be used to construct the standard or reference of foetal biometry: cross-sectional data and longitudinal data. In cross-sectional studies, each foetus is observed only once, thus it can only assess foetal size (Royston 1995). Longitudinal data are derived from serial scans and thus reflect the dynamic process of foetal growth. Longitudinal data is a multilevel data that has two levels of variation: within foetuses and between foetuses. Utilising the multilevel modelling, longitudinal data can calculate conditional percentile that can be used to assess foetal growth (Royston and Altman 1995; Owen and Ogston 1998). The conditional percentile is an adjusted growth interval based on the previous measurements of the same foetus earlier in pregnancy and individualised growth trajectory (Hiersch and Melamed 2018).
A transition copula model for analyzing multivariate longitudinal data with missing responses
Published in Journal of Applied Statistics, 2022
A. Ahmadi, T. Baghfalaki, M. Ganjali, A. Kabir, A. Pazouki
Baghfalaki and Ganjali [3] used Gaussian copula functions for joint modeling of mixed balanced longitudinal data. In this paper, instead of restricting ourselves to use the Gaussian copula, we use different copula functions from elliptical and Archimedean families simultaneously and choose the best model for each time point using the AIC. Also, we consider unbalanced longitudinal data by the possibility to have ignorable missing values. Some simulation studies are performed to investigate the performance of our approach and the proposed approach is used for analyzing a real data set on obesity data. The paper is organized as follows. Section 2 introduces obesity data. Section 3 demonstrates our method for analyzing multivariate longitudinal data with missing responses. Section 4 reports some simulation results for investigating the performance of the proposed approach. Section 5 includes analysis of obesity data. Section 6 contains discussion and some conclusions.