Clustering based on adherence data

Sylvia Kiwuwa-Muyingo,Jonathan Levin,Pauliina Ilmonen,Hannu Oja,Sarah A Walker,Jim Todd

doi:10.1186/1742-5573-8-3

Abstract

Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long follow-up or treatment period, and some measurements may be missing due to death or other reasons. A natural question then is how to describe adherence behavior over the whole period in a simple way. In the literature, measurements over a period are usually combined just by using averages like percentages of compliant days or percentages of doses taken. In the paper we adapt an approach where patient adherence measures are seen as a stochastic process. Repeated measures are then analyzed as a Markov chain with finite number of states rather than as independent and identically distributed observations, and the transition probabilities between the states are assumed to fully describe the behavior of a patient. The patients can then be clustered or classified using their estimated transition probabilities. These natural clusters can be used to describe the adherence of the patients, to find predictors for adherence, and to predict the future events. The new approach is illustrated and shown to be useful with a simple analysis of a data set from the DART (Development of AntiRetroviral Therapy in Africa) trial in Uganda and Zimbabwe.

Highlights

Adherence is defined as the extent to which patients follow instructions for prescribed treatment necessary to achieve the full treatment benefits [1]
In our approach to develop new statistical tools to characterize and understand the adherence behavior of the HIV patients treated with anti-retroviral therapy (ART), and to illustrate the use of these tool with real data
The main motivation of this paper was to develop and illustrate new statistical tools to characterize and understand the adherence behavior of the HIV patients treated with ART, and to illustrate these tools with data from the Development of Antiretroviral Therapy in Africa (DART) study

Summary

Introduction

Adherence is defined as the extent to which patients follow instructions for prescribed treatment necessary to achieve the full treatment benefits [1]. In our approach to develop new statistical tools to characterize and understand the adherence behavior of the HIV patients treated with ART, and to illustrate the use of these tool with real data To do this the adherence measures at each time point are first categorized to a variable with finite number of values or “states”. Death and loss to follow-up or withdrawals from the study were recorded The objective of this analysis was to find groups of DART patients with a similar adherence behavior (same proportions of being in each state, or same transition probabilities in homogeneous or non-homogeneous Markov chains) during the first year of the follow-up. Predictors and explanatory variables As an illustration of the use of the clusters in a further analysis, we considered the relationship between age and sex and adherence which was categorized using clusters based on non-homogeneous Markov chain model (M3).

Discussion

Findings

20. Seber GAF: Multivariate observations New York