Partially hidden multi-state modelling of a prolonged disease state defined by a composite outcome
Vernon T Farewell1, Li Su2, Christopher Jackson2
1MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Robinson Way, Cambridge, CB2 0SR, UK. vern.farewell@mrc-bsu.cam.ac.uk.
Abstract:
For rheumatic diseases, Minimal Disease Activity (MDA) is usually defined as a composite outcome which is a function of several individual outcomes describing symptoms or quality of life. There is ever increasing interest in MDA but relatively little has been done to characterise the pattern of MDA over time. Motivated by the aim of improving the modelling of MDA in psoriatic arthritis, the use of a two-state model to estimate characteristics of the MDA process is illustrated when there is particular interest in prolonged periods of MDA. Because not all outcomes necessary to define MDA are measured at all clinic visits, a partially hidden multi-state model with latent states is used. The defining outcomes are modelled as conditionally independent given these latent states, enabling information from all visits, even those with missing data on some variables, to be used. Data from the Toronto Psoriatic Arthritis Clinic are analysed to demonstrate improvements in accuracy and precision from the inclusion of data from visits with incomplete information on MDA. An additional benefit of this model is that it can be extended to incorporate explanatory variables, which allows process characteristics to be compared between groups. In the example, the effect of explanatory variables, modelled through the use of relative risks, is also summarised in a potentially more clinically meaningful manner by comparing times in states, and probabilities of visiting states, between patient groups.
Insights
This study introduces a new statistical model to better understand Minimal Disease Activity (MDA) in psoriatic arthritis. The model improves accuracy by using all available patient data, even with missing information, to track disease activity over time.
Area of Science:
- Rheumatology
- Biostatistics
- Clinical Epidemiology
Background:
- Minimal Disease Activity (MDA) is a key outcome in rheumatic diseases, but its temporal patterns are not well understood.
- Current methods for analyzing MDA over time are limited, especially when data is incomplete.
Purpose of the Study:
- To develop and illustrate a statistical model for characterizing the time-dependent process of Minimal Disease Activity (MDA) in psoriatic arthritis.
- To improve the modeling of MDA by accounting for incomplete data and identifying prolonged periods of disease control.
Main Methods:
- Utilized a partially hidden multi-state model with latent states to analyze MDA.
- Modeled defining outcomes as conditionally independent given latent states, accommodating missing data from clinic visits.
- Applied the model to data from the Toronto Psoriatic Arthritis Clinic.
Main Results:
- Demonstrated improved accuracy and precision in MDA assessment by incorporating data from visits with incomplete information.
- The model effectively estimates characteristics of the MDA process over time.
- Showcased the model's ability to incorporate explanatory variables for group comparisons.
Conclusions:
- The proposed partially hidden multi-state model offers a robust approach to analyzing MDA trajectories in psoriatic arthritis.
- This method enhances the understanding of disease activity patterns and can inform clinical decision-making.
- The model's flexibility allows for the incorporation of covariates to compare disease activity between patient groups.
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