Armitage Lecture 2011: the design and analysis of life history studies

Lawless, J. F.

doi:10.1002/sim.5754

Cited by 12 publications

(15 citation statements)

References 63 publications

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“…Life history studies collect information on events and other outcomes during people's lifetimes (Lawless 2013). Thus gender, age, family diagnostics, lifestyles typically could be important covariates that have an impact on the ssurvival time T. As will be discussed in more detail in example, we also observed some covariates have significant impacts on the lifetime of the transplanted kidneys.…”

Section: Introductionmentioning

confidence: 70%

Aalen's Additive, Cox Proportional Hazards and the Cox-Aalen Model: Application to Kidney Transplant Data

Başar¹

2017

JSM

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Section: Introductionmentioning

confidence: 70%

Aalen's Additive, Cox Proportional Hazards and the Cox-Aalen Model: Application to Kidney Transplant Data

Başar¹

2017

JSM

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“…In the present setting, this is reasonable because individuals invariably seek medical care when joints become inflamed, which occurs considerably earlier to the development of joint damage. Since the times of the assessments are random, the observed data likelihood is

L \propto \prod_{r = 1}^{R} P (N_{r} = n_{r}, A_{r} = a_{r} | H_{r - 1}) .

The assessment process is sequentially ignorable (Hogan et al., ; Lawless, ) in a likelihood based analysis if given

H_{r - 1}

, the probability an assessment is made at

a_{r}

is independent of event occurrence over

[a_{r - 1}, a_{r})

. Under this assumption (i.e.…”

Section: A Dynamic Mover‐stayer Model Under Interval‐censoringmentioning

confidence: 99%

Analysis of interval‐censored recurrent event processes subject to resolution

Shen

Cook

2015

Biometrical J

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SummaryInterval-censored recurrent event data arise when the event of interest is not readily observed but the cumulative event count can be recorded at periodic assessment times. In some settings, chronic disease processes may resolve, and individuals will cease to be at risk of events at the time of disease resolution. We develop an expectation-maximization algorithm for fitting a dynamic mover-stayer model to interval-censored recurrent event data under a Markov model with a piecewise-constant baseline rate function given a latent process. The model is motivated by settings in which the event times and the resolution time of the disease process are unobserved. The likelihood and algorithm are shown to yield estimators with small empirical bias in simulation studies. Data are analysed on the cumulative number of damaged joints in patients with psoriatic arthritis where individuals experience disease remission.

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“…In studies with widely spaced visit times this condition is often violated to some extent. We discuss this further in Section 6.1; see also Lawless (2013).…”

Section: Likelihood With Intermittent Observationmentioning

confidence: 99%

Statistical Issues in Modeling Chronic Disease in Cohort Studies

Cook

Lawless

2013

Stat Biosci

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SummaryObservational cohort studies of individuals with chronic disease provide information on rates of disease progression, the effect of fixed and time-varying risk factors, and the extent of heterogeneity in the course of disease. Analysis of this information is often facilitated by the use of multistate models with intensity functions governing transition between disease states. We discuss modeling and analysis issues for such models when individuals are observed intermittently. Frameworks for dealing with heterogeneity and measurement error are discussed including random effect models, finite mixture models, and hidden Markov models. Cohorts are often defined by convenience and ways of addressing outcome-dependent sampling or observation of individuals are also discussed. Data on progression of joint damage in psoriatic arthritis and retinopathy in diabetes are analysed to illustrate these issues and related methodology.

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Armitage Lecture 2011: the design and analysis of life history studies

Cited by 12 publications

References 63 publications

Aalen's Additive, Cox Proportional Hazards and the Cox-Aalen Model: Application to Kidney Transplant Data

Aalen's Additive, Cox Proportional Hazards and the Cox-Aalen Model: Application to Kidney Transplant Data

Analysis of interval‐censored recurrent event processes subject to resolution

Statistical Issues in Modeling Chronic Disease in Cohort Studies

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