PREDICTION OF INTER EPISODIC TIME FOR RECURRING MENTAL ILLNESS USING ORDER STATISTICS
Recurrent episodes are common across various mental disorders. Information on time to next episode, also referred as inter episodic times, provides a valuable tool for planning and evaluating the health outcomes of treatment in patients and developing effective preventive maintenance therapy. The objective is to obtain the prediction interval for the future inter episodic time when the number of previous episodes for a patient is small and inter episodic times are dependent. A data of 28 patients with a history of 3 or more recurring episodes of illness is extracted from a retrospective data of 146 patients diagnosed with mental and behavioral disorders. The prediction interval for time to occurrence of next episode is obtained using order statistics assuming that it will follow the order followed by previous inter episodic times. The validity of the results is verified using simulation studies with data generated using covariance structure of the real dataset. From the simulation studies, we found that more than 80% of the simulated inter episodic times lie in the simulated prediction intervals. This paper is highly beneficial to medical health professionals to predict time to next episode for patients with few previously known episodes of the concerned disease. The study has an implication to rare diseases where generally small database (patients) is available.
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