The Analysis of Panel Data under a Markov Assumption

The role of HLA antigens as indicators of disease progression in psoriatic arthritis. Multivariate relative risk model

OBJECTIVE

To identify HLA markers for the development of severe disease in psoriatic arthritis (PsA).

METHODS

Patients with PsA who were followed up prospectively over a 14-year period were included. Clinical and laboratory assessments of both active inflammation and clinical damage were performed at 6-month intervals according to a standard protocol, which also included serologic HLA typing for class I and II antigens. Progression of damage was defined as transition into higher damage states, defined by the number of damaged joints. Both univariate and multivariate models were developed to identify predictors for progression of damage.

RESULTS

Univariate analysis revealed that the HLA antigens B27, B39, and DQw3 were associated with disease progression, while HLA-DR7 was "protective." The best multivariate model identified the HLA antigens B27, when DR7 was present, and DQw3, when DR7 was not present, as predicting disease progression across all transitions, while HLA-B39 was associated with progression in early disease. The addition of these HLA indicators to a model containing clinical variables resulted in a significant improvement in fit.

CONCLUSION

The HLA antigens associated with PsA, B27 and B39, are risk factors for disease progression, as is the HLA class II antigen DQw3. In combination with clinical measures of disease, these HLA variables are the dominant predictors of progression.

Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease

Multi-state Markov models can be useful in analysing disease history data. We apply the general estimation methods of Kalbfleisch and Lawless to panel data in which individuals are viewed over only a portion of their life history and complete information about transition times between states is unavailable. Methods to assess goodness-of-fit are proposed. To illustrate the methods, we consider models of HIV disease relating important immunological marker measurements to the onset of AIDS.

An annotated bibliography of methods for analysing correlated categorical data

This paper provides an annotated bibliography of over 100 articles concerning methods for analysing correlated categorical response data. Most of the papers listed here concern categorical regression models and estimation, with particular emphasis on binary responses. The papers are classified by several characteristics which group them according to common themes. The bibliography serves as a reference of methods for analysts of correlated categorical data, as well as for persons interested in methodologic work in this active area of statistical research.

Designing a clinical trial to demonstrate prevention of ulcer recurrence: modelling simulation approaches

We describe a Markov chain model for the ulcer recurrence and healing process, review the available literature to obtain appropriate parameter estimates, and use this model to evaluate alternative clinical trial designs. We focus on designs aimed at supporting recurrence prevention claims for a duodenal ulcer maintenance treatment. Our results show that a trial with endoscopies scheduled only at four month intervals is inadequate to support recurrence prevention claims; discrimination between the null and alternative hypotheses is impossible because of the size and direction of expected biases in observed recurrences. Our results suggest that endoscopy intervals should be at most four weeks to establish a claim of ulcer prevention.

Likelihood analysis of multi-state models for disease incidence and mortality

Data related to life histories of individuals can be obtained in many different ways, and the usefulness of multi-state models for statistical analysis is generally highly dependent on the type and nature of the data. In this paper, we focus on this, and present an approach to estimation for certain 'difficult' situations associated with retrospective or incomplete prospective observation. The paper begins with the identification of some problem areas in the analysis of data on life history processes. We discuss maximum likelihood estimation in some simple contexts and introduce a pseudo-likelihood which enables the simple analysis of some sampling procedures. This approach is illustrated on standard retrospective and case-cohort designs.

Comparison of baseline and repeated measure covariate techniques in the Framingham Heart Study

The purpose of this paper is to indicate how repeated measures on risk factors have been employed in the prediction of the development of disease in the Framingham Heart Study. Since these measures vary over time, the method accounts for time dependent covariates. The technique is a generalized person-years approach in that it treats each observation interval (of equal length) as a mini-follow-up study in which the current risk factor measurements are employed to predict an event in the interval. Observations over multiple intervals are pooled into a single sample to predict the short term risk of an event. This approach is compared to the long-term prediction of disease which utilizes only the baseline measurements and ignores subsequent repeated measures on the risk factors.

Issues in the analysis of repeated categorical outcomes

This paper discusses statistical methods for the analysis of repeated observations of categorical variables as they might arise in longitudinal studies. Two general types of models are described: marginal models that give representations for the marginal distribution of response at each occasion, and transitional models that give representations for the transition probabilities between outcome states at successive occasions. The conceptual and technical differences are discussed and recent work advancing both approaches is reviewed. The two approaches are illustrated through analysis of repeated observations on interval history of the respiratory symptom 'persistent wheeze' in preadolescent children.