title: Catching up on health outcomes: the Texas Medication Algorithm Project.
abstract: OBJECTIVE
  To develop a statistic measuring the impact of algorithm-driven disease management programs on outcomes for patients with chronic mental illness that allowed for treatment-as-usual controls to "catch up" to early gains of treated patients.
  
  
  METHODS
  Statistical power was estimated from simulated samples representing effect sizes that grew, remained constant, or declined following an initial improvement. Estimates were based on the Texas Medication Algorithm Project on adult patients (age > or = 18) with bipolar disorder (n = 267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas.
  
  
  METHODS
  Study patients were assessed at baseline and three-month follow-up for a minimum of one year. Program tracks were assigned by clinic.
  
  
  METHODS
  Hierarchical linear modeling was modified to account for declining-effects. Outcomes were based on 30-item Inventory for Depression Symptomatology-Clinician Version.
  
  
  RESULTS
  Declining-effect analyses had significantly greater power detecting program differences than traditional growth models in constant and declining-effects cases. Bipolar patients with severe depressive symptoms in an algorithm-driven, disease management program reported fewer symptoms after three months, with treatment-as-usual controls "catching up" within one year.
  
  
  CONCLUSIONS
  In addition to psychometric properties, data collection design, and power, investigators should consider how outcomes unfold over time when selecting an appropriate statistic to evaluate service interventions. Declining-effect analyses may be applicable to a wide range of treatment and intervention trials.
authors.affiliation: Dallas VA Center for Health Services Research, Department of Veterans Affairs, North Texas Health Care System, USA.
authors.forename: T Michael
authors.initials: TM
authors.lastname: Kashner
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Carmody
authors.forename: Trisha
authors.initials: T
authors.lastname: Suppes
authors.forename: A John
authors.initials: AJ
authors.lastname: Rush
authors.forename: M Lynn
authors.initials: ML
authors.lastname: Crismon
authors.forename: Alexander L
authors.initials: AL
authors.lastname: Miller
authors.forename: Marcia
authors.initials: M
authors.lastname: Toprac
authors.forename: Madhukar
authors.initials: M
authors.lastname: Trivedi
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D000928:Antidepressive Agents / Q000627:therapeutic use*; D001714:Bipolar Disorder / Q000188:drug therapy*; D002908:Chronic Disease; D003155:Community Mental Health Centers / Q000592:standards*; D019091:Critical Pathways; D003866:Depressive Disorder / Q000188:drug therapy; D019468:Disease Management; D005260:Female; D019983:Guideline Adherence; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D013781:Texas; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D000928:Antidepressive Agents