Kiwuwa-Muyingo Sylvia, Oja Hannu, Walker Sarah A, Ilmonen Pauliina, Levin Jonathan, Todd Jim
School of Health Sciences, University of Tampere, Finland.
Epidemiol Perspect Innov. 2011 Mar 8;8:3. doi: 10.1186/1742-5573-8-3.
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.
坚持医疗治疗是指患者遵循卫生专业人员的指示或建议的程度。有直接和间接的方法来测量坚持情况，这些方法已用于临床管理和研究。通常，坚持情况的测量是在长期随访或治疗期间进行监测的，并且由于死亡或其他原因，一些测量结果可能会缺失。那么一个自然的问题是，如何以一种简单的方式描述整个期间的坚持行为。在文献中，一段时间内的测量通常只是通过使用诸如依从天数百分比或服用剂量百分比等平均值来进行综合。在本文中，我们采用一种方法，将患者坚持情况的测量视为一个随机过程。然后将重复测量分析为具有有限状态数的马尔可夫链，而不是作为独立同分布的观测值，并且假设状态之间的转移概率完全描述了患者的行为。然后可以使用估计的转移概率对患者进行聚类或分类。这些自然聚类可用于描述患者的坚持情况、寻找坚持的预测因素以及预测未来事件。通过对乌干达和津巴布韦的DART（非洲抗逆转录病毒疗法的发展）试验数据集进行简单分析，对新方法进行了说明并证明其有用性。
