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Detection of Progressive Deterioration in Early Onset Schizophrenia with a New Statistical Method

• Ying Qing Chen, Division of Biostatistics, School of Public Health, University of California, Berkeley
• Mei-Cheng Wang, Department of Biostatistics, School of Hygiene and Public Health, Johns Hopkins University
• William W. Eaton, Department of Mental Hygiene, School of Hygiene and Public Health, Johns Hopkins University

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Abstract:

Much controversy exists over whether the course of schizophrenia, as defined by the lengths of repeated community tenures, is progressively ameliorating or deteriorating. This article employs a new statistical method proposed by Wang and Chen (2000) to analyze the Denmark registry data in Eaton, et al (1992). The new statistical method correctly handles the bias caused by induced informative censoring, which is an interaction of the heterogeneity of schizophrenia patients and long-term follow-up. The analysis shows a progressive deterioration pattern in terms of community tenures for the full registry cohort, rather than a progressive amelioration pattern as reported for a selected sub-cohort in Eaton, et al (1992). When adjusted for the long-term chronicity of calendar time, no significant progressive pattern was found for the full cohort.

Suggested Citation:

Ying Qing Chen, Mei-Cheng Wang, and William W. Eaton, "Detection of Progressive Deterioration in Early Onset Schizophrenia with a New Statistical Method" (December 2001). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 102.
http://www.bepress.com/ucbbiostat/paper102