Conferences

Below is a list of multi-state modelling related talks at forthcoming conferences:

Joint Statistical Meetings 2009:
Somnath Datta and Ling Lan
Nonparametric Inference in Multistate Models with Interval-Censored Data

Richard Cook
Multistate Analysis of Bivariate Interval-Censored Failure Time Data

Hans C. van Houwelingen and Hein Putter
Dynamic Predicting by Landmarking as an Alternative for Multistate Modeling: An Application to Acute Lymphoid Leukemia Data

Liou Xu, David Snowdon and Richard J. Kryscio
A Markov Transition Model to Dementia with Death as a Competing Event

Wei-Ting Hwang, Neha Vapiwala and Lawrence J. Solin
A Stayer-Mover Mixture Markov Model for Disease Transitions in Early-Staged Breast Cancer Treated with Breast-Conserving Therapy (BCT)

Halina Frydman and Michael Szarek
Estimation of Overall Survival in an Illness-Death Model with Application to the Vertical Transmission of HIV-1

ISCB 30:

Talks:

Michael Lauseker, Jörg Hasford and Andreas Hochhaus
Prediction In Multi-State Models And Its Application In Chronic Myeloid Leukaemia     

Martin Wolkewitz, Arthur Allignol, Martin Schumacher and Jan Beyersmann
Understanding And Avoiding Survival Bias: An Application Of Multistate Models In A Cohort Of Oscar Nominees

Thomas Kneib
Semiparametric Multi-State Models

Giuliana Cortese and Per Kragh Andersen
Internal Time-Dependent Covariates In Competing Risks Models For Bone Marrow Transplant Studies 

Per Kragh Andersen, Kajsa Kvist and Lars Kessing
Effect Of Event-Dependent Sampling Of Recurrent Events.     

Michael Schemper and Alexandra Kaider
Quantifying The Correlation Of Bivariate Survival Times By Means Of A Novel Self-Consistency Approach     

Ronald Geskus, Nicolas Poulin, Hilton Whittle and Maarten Schim van der Loeff
A Markov Cure Model To Compare Progression Of HIV-1 And HIV-2 Infection

Posters:
Qing Wang, Linda Sharples and Nikolaos Demiris
Multi-State Models For The Analysis Of Lung Transplant Data     

Liesbeth de Wreede, Marta Fiocco and Hein Putter
The Analysis Of Multi-State Models By Means Of The Mstate Package     

ISI, Durban:
Invited Paper meeting:
Inference and Prediction in Competing Risks and Multi-State Models
Organiser: Hein Putter
Participants: Martin Schumacher, Bendix Carstensen, Ørnulf Borgan.

Parameter estimation in a model for misclassified Markov data - a Bayesian approach.

Rosychuk and Islam have a paper in Computational Statistics and Data Analysis. This concerns parameter estimation in a two-state recurrent misclassification type hidden Markov model, where the Markov process is assumed to be continuous time and in equilibrium and is observed at discrete, equally spaced time points. A Bayesian approach to estimation is considered via Gibbs sampling. To avoid identifiability issues, the misclassification probabilities are constrained to be below 0.5. An additional issue is the choice of starting values of the transition probabilities for the latent Markov process. Values based on simple correction formulae previously developed by Rosychuk and Thompson appear to perform better than values based on taking naive estimates of the transition probabilities of the observed process.

Competing risks and time-dependent covariates

Cortese and Andersen have a paper available as a research report from the Department of Biostatistics, University of Copenhagen. This concerns the problem of prediction in competing risks models where there are internal time-dependent covariates, meaning the trajectory of the covariate is not predictable, nor is it independent of the development of the disease/mortality process. The authors focus on the case where the time-dependent covariate is binary, and once it has value 1, cannot revert to value 0. They investigate three approaches. The first expands the state space of the competing risks model, having two alive states: 'alive and cov=0' and 'alive and cov=1' and applies standard methods based on Nelson-Aalen and Aalen-Johansen estimators. The two other methods are based on landmarking.

Update: This paper is now published in Biometrical Journal.

Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination

Richard Cook et al have a new paper in JASA. This concerns the estimation of mean functions for recurrent events under event dependent censoring. They consider several methods, including a multi-state approach using an estimate based on the Aalen-Johansen estimator of the transition intensities using IPCW to correct for dependent censoring.

Estimating life expectancy in health and ill health by using a hidden Markov model

Van den Hout, Jagger and Matthews have a paper to appear in JRSS C. The paper applies the misclassification hidden Markov model, developed by Satten and Longini and Jackson and Sharples, to modelling of data on cognitive impairment in the elderly and its effect on mortality. Patients with a cognition score (MMSE) below 22 were considered impaired. However, cognitive decline is considered to be progressive so backwards transitions in the dataset are explained through misclassification.

The main aim of the paper is to estimate life expectancies in the non-impaired and impaired states. As mortality will be highly dependent on age, non-homogeneous transition intensities are required. Rather than employ the standard approach of piecewise constant intensities, the authors instead include age as a log-linear time dependent covariate and assume that an individual observed at ages t and u, for t < u, has constant intensity Q(t) for the interval (t,u). This will clearly result in some degree of bias, particularly if observation times are widely spaced. Life expectancy is then calculated by assuming intensities are constant in 1 year intervals. As this is different from how the data were estimated, the bias may be further compounded.

Rudimentary goodness-of-fit is carried out by comparing estimated survival curves from the HMM with a Cox-regression performed directly on the survival data. It is worth noting that this approach could be problematic in certain circumstances because the HMM is not nested within the Cox-regression model, so there might be discrepancies between the curves even if the HMM is correctly specified.

Nonparametric estimation in an "illness-death" model when the transition times are interval-censored and one transition is not observed.

Frydman, Gerds, Groen and Keiding have a paper available as a research report from the Department of Biostatistics, Copenhagen. The paper develops previous work on the non-parametric estimation of interval-censored multi-state data. Here the data in question follow a progressive three-state "illness-death" model but the ill to death transition is never observed. This is because the data arise from clinical observation and the trial ceases if a patient is observed to be in the illness state. Such an observation scheme has strong similarities with data considered by Duffy et al relating to breast cancer screening where a three-stage unidirectional model was assumed and the intermediate state was pre-clinical detectable breast cancer. No data on pre-clinical to clinical breast cancer transitions were available as interest was in the natural progression of the disease. Duffy et al analysed the data parametrically, assuming a time homogeneous Markov model. In contrast Frydman et al fit a non-homogeneous Markov model non-parametrically. Since all transitions are interval censored, they model the process in discrete time.
Update: A paper broadly based upon the research report has now been published in Biometrical Journal. The supplementary materials also includes R code to implement the proposed algorithm.

A multistate approach for estimating the incidence of human immunodeficiency virus by using HIV and AIDS French surveillance data

Sommen, Alioum and Commenges have a new paper in Statistics in Medicine. This applies the penalized likelihood approach to multi-state models, used extensively by the INSERM group, to the area of back-calculation for estimating HIV incidence. The penalization parameters are chosen by minimizing an approximate cross-validation score.