title: Discrete state analysis for interpretation of data from clinical trials.
abstract: OBJECTIVE
  The objective of this study was to demonstrate a multivariate health state approach to analyzing complex disease data that allows projection of long-term outcomes using clustering, Markov modeling, and preference weights.
  
  
  METHODS
  We studied patients hospitalized 30 to 364 days with refractory schizophrenia at 15 Veterans Affairs medical centers.
  
  
  METHODS
  We conducted a randomized clinical trial comparing clozapine, an atypical antipsychotic, and haloperidol, a conventional antipsychotic.
  
  
  METHODS
  Health status instruments measuring disease-related symptoms and drug side effects were administered in face-to-face interviews at baseline, 6 weeks, and quarterly follow-up intervals for 1 year. Cost data were derived from Veterans Affairs records supplemented by interviews. K-means clustering was used to identify a small number of health states for each instrument. Markov modeling was used to estimate long-term outcomes.
  
  
  RESULTS
  Multivariate models with 7 and 6 states, respectively, were required to describe patterns of psychiatric symptoms and side effects (movement disorders). Clozapine increased the proportion of clients in states characterized by mild psychiatric symptoms and decreased the proportion with severe positive symptoms but showed no long-term benefit for negative symptoms. Clozapine dramatically increased the proportion of patients with no movement side effects and decreased incidences of mild akathisia. Effects on extrapyramidal symptoms and tardive dyskinesia were far less pronounced and slower to develop. Markov modeling confirms the consistency of these findings.
  
  
  CONCLUSIONS
  Analyzing complex disease data using multivariate health state models allows a richer understanding of trial effects and projection of long-term outcomes. Although clozapine generates substantially fewer side effects than haloperidol, its impact on psychiatric aspects of schizophrenia is less robust and primarily involves positive symptoms.
authors.affiliation: Marshall School of Business, University of Southern California, Los Angeles, California 90089-0809, USA. sugar@usc.edu
authors.forename: Catherine A
authors.initials: CA
authors.lastname: Sugar
authors.forename: Gareth M
authors.initials: GM
authors.lastname: James
authors.forename: Leslie A
authors.initials: LA
authors.lastname: Lenert
authors.forename: Robert A
authors.initials: RA
authors.lastname: Rosenheck
mesh_terms: D014150:Antipsychotic Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000379:methods*; D003024:Clozapine / Q000627:therapeutic use; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D004311:Double-Blind Method; D006220:Haloperidol / Q000627:therapeutic use; D006801:Humans; D015233:Models, Statistical; D010043:Outcome and Process Assessment, Health Care / Q000379:methods; D025341:Principal Component Analysis / Q000379:methods; D016032:Randomized Controlled Trials as Topic; D012559:Schizophrenia / Q000188:drug therapy; D016896:Treatment Outcome; D014481:United States; D014493:United States Department of Veterans Affairs
chemical_list: D014150:Antipsychotic Agents; D003024:Clozapine; D006220:Haloperidol