Joint modelling of accelerated failure time and longitudinal data

Tseng, Yi Kuan; Hsieh, Fushing; Wang, Jane-Ling

doi:10.1093/biomet/92.3.587

Cited by 128 publications

(105 citation statements)

References 24 publications

Supporting

Mentioning

105

Contrasting

Order By: Relevance

“…For such situations other models need to be used. Tseng et al [26] used an accelerated failure time survival model as an alternative to the Cox model with longitudinal covariates following a linear mixed-effects model with measurement errors. Song and Huang [27] used a joint longitudinal-survival model with an additive hazard, where timedependent covariates measured with errors are added to the baseline hazard.…”

Section: Introductionmentioning

confidence: 99%

Stochastic model for analysis of longitudinal data on aging and mortality

Yashin

Arbeev

Akushevich

et al. 2007

Mathematical Biosciences

133

View full text Add to dashboard Cite

Aging-related changes in a human organism follow dynamic regularities, which contribute to the observed age patterns of incidence and mortality curves. An organism's "optimal" (normal) physiological state changes with age, affecting the values of risks of disease and death. The resistance to stresses, as well as adaptive capacity, declines with age. An exposure to improper environment results in persisting deviation of individuals' physiological (and biological) indices from their normal state (due to allostatic adaptation), which, in turn, increases chances of disease and death. Despite numerous studies investigating these effects, there is no conceptual framework, which would allow for putting all these findings together, and analyze longitudinal data taking all these dynamic connections into account. In this paper we suggest such a framework, using a new version of stochastic process model of aging and mortality. Using this model, we elaborated a statistical method for analyses of longitudinal data on aging, health and longevity and tested it using different simulated data sets. The results show that the model may characterize complicated interplay among different components of aging-related changes in humans and that the model parameters are identifiable from the data.

show abstract

Section: Introductionmentioning

confidence: 99%

Stochastic model for analysis of longitudinal data on aging and mortality

Yashin

Arbeev

Akushevich

et al. 2007

Mathematical Biosciences

133

View full text Add to dashboard Cite

show abstract

“…Joint modelling of the two different types of endpoints simultaneously has received considerable attention in recent years [9][10][11][12][13][14][15][16][17][18][19][20]. Tsiatis and Davidian provided a nice overview of joint models [21].…”

Section: Introductionmentioning

confidence: 99%

“…A fourth advantage of joint modelling stems from scientific investigations such as AIDS studies, in which the interest is to characterize the relationship between CD4 count and the time to AIDS. One common procedure is that the true underlying trajectory of the CD4 count can be first modelled, and then be incorporated into a Cox model for the time to AIDS [14,15], or into an accelerated failure time model in other applications if the proportional hazards assumption fails [16]. Non-likelihood-based approaches include the work of Robins and his colleagues [22][23][24] who used augmented inverse probability of censoring weighted estimating equations.…”

Section: Introductionmentioning

confidence: 99%

An approach to joint analysis of longitudinal measurements and competing risks failure time data

Elashoff

2006

Statistics in Medicine

View full text Add to dashboard Cite

SUMMARYJoint analysis of longitudinal measurements and survival data has received much attention in recent years. However, previous work has primarily focused on a single failure type for the event time. In this paper we consider joint modelling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint which occurs frequently in clinical trials. Our model uses latent random variables and common covariates to link together the sub-models for the longitudinal measurements and competing risks failure time data, respectively. An EM-based algorithm is derived to obtain the parameter estimates, and a profile likelihood method is proposed to estimate their standard errors. Our method enables one to make joint inference on multiple outcomes which is often necessary in analyses of clinical trials. Furthermore, joint analysis has several advantages compared with separate analysis of either the longitudinal data or competing risks survival data. By modelling the event time, the analysis of longitudinal measurements is adjusted to allow for non-ignorable missing data due to informative dropout, which cannot be appropriately handled by the standard linear mixed effects models alone. In addition, the joint model utilizes information from both outcomes, and could be substantially more efficient than the separate analysis of the competing risk survival data as shown in our simulation study. The performance of our method is evaluated and compared with separate analyses using both simulated data and a clinical trial for the scleroderma lung disease.

show abstract

“…The specific steps of the EM-algorithm to arrive at the parameter estimates are described in detail in Tseng et al (2005).…”

Section: Joint Models With Focus On Event Timesmentioning

confidence: 99%

A Review on Joint Models in Biometrical Research

Neuhaus

Augustin

Heumann

et al. 2009

Journal of Statistical Theory and Practice

View full text Add to dashboard Cite

In some fields of biometrical research joint modelling of longitudinal measures and event time data has become very popular. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modelled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in an uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research.

show abstract

Joint modelling of accelerated failure time and longitudinal data

Cited by 128 publications

References 24 publications

Stochastic model for analysis of longitudinal data on aging and mortality

Stochastic model for analysis of longitudinal data on aging and mortality

An approach to joint analysis of longitudinal measurements and competing risks failure time data

A Review on Joint Models in Biometrical Research

Contact Info

Product

Resources

About