TY - JOUR
T1 - Catching up on health outcomes
T2 - The Texas medication algorithm project
AU - Kashner, T. Michael
AU - Carmody, Thomas J.
AU - Suppes, Trisha
AU - Rush, A. John
AU - Crismon, M. Lynn
AU - Miller, Alexander L.
AU - Toprac, Marcia
AU - Trivedi, Madhukar
PY - 2003/2
Y1 - 2003/2
N2 - 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. Data Sources/Study Setting. 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 ≥ 18) with bipolar disorder (n = 267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas. Study Design. Study patients were assessed at baseline and three-month follow-up for a minimum of one year. Program tracks were assigned by clinic. Data Collection/Extraction Methods. Hierarchical linear modeling was modified to account for declining-effects. Outcomes were based on 30-item Inventory for Depression Symptomatology - Clinician Version. Principal Findings. 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.
AB - 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. Data Sources/Study Setting. 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 ≥ 18) with bipolar disorder (n = 267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas. Study Design. Study patients were assessed at baseline and three-month follow-up for a minimum of one year. Program tracks were assigned by clinic. Data Collection/Extraction Methods. Hierarchical linear modeling was modified to account for declining-effects. Outcomes were based on 30-item Inventory for Depression Symptomatology - Clinician Version. Principal Findings. 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.
KW - Disease management systems
KW - Program evaluation
KW - Severe mental illness
KW - Treatment algorithm
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U2 - 10.1111/1475-6773.00117
DO - 10.1111/1475-6773.00117
M3 - Article
C2 - 12650393
AN - SCOPUS:0037301289
SN - 0017-9124
VL - 38
SP - 311
EP - 331
JO - Health Services Research
JF - Health Services Research
IS - 1 I
ER -