Catching Up on Health Outcomes: The Texas Medication Algorithm Project

Autor: Marcia G. Toprac, Thomas J. Carmody, A. John Rush, Madhukar H. Trivedi, T. Michael Kashner, M. Lynn Crismon, Trisha Suppes, Alexander L. Miller
Rok vydání: 2003
Předmět:
Zdroj: Health Services Research. 38:311-331
ISSN: 1475-6773
0017-9124
DOI: 10.1111/1475-6773.00117
Popis: In this paper, we developed a new approach, called the Declining-Effects Model, to analyze longitudinal data evaluating a disease management program (DMP) for patients with chronic illness, including mental illness. This approach takes into account how health outcomes may unfold over time by comparing the course of illness between patients assigned to new treatment programs with controls who receive treatment as usual (TAU). The model was tested using data from the Texas Medication Algorithm Project (TMAP), a DMP for severe mental illness that included consensus-based medication-algorithms, as well as patient education, uniform clinical reports, expert consultation, and clinical coordinators overseeing algorithm adherence (Rush et al. 1999). Investigators often evaluate DMPs by assigning patients to treatment tracks and repeatedly assessing their outcomes over time, beginning at baseline when treatment begins. Disease management programs are considered effective if the outcomes among treated patients are better than outcomes experienced among controls. Statisticians often summarize these differences by calculating an effect-statistic. While conceptual factors underlying program rationale often influence the choice of a primary outcome measure (McDowell and Newell 1996), the choice of an appropriate effect-statistic typically depends on: (1) the psychometric properties of selected outcome measures, (2) the research design, and (3) properties of the statistic itself. These properties include the power of the statistic to avoid falsely detecting effects that do not exist (false positives, or type I error) and failing to find effects that do exist (false negatives, or type II error) (Siegel 1956). Investigators often report the latter as statistical power, representing the chance that a statistic would significantly detect an actual effect. All things equal, the most desirable statistic is one with the greatest power for a given type I error. Not all effect-statistics require working assumptions about how outcomes unfold over time (Lavori 1990). However, to summarize program effectiveness into a single estimate, researchers often borrowed from the efficacy trials literature to select statistics powered to detect effects that grow with time. Under a growth hypothesis, the outcomes of patients receiving efficacious therapies are expected to improve with time, while their untreated counterparts would remain the same, or get worse. Thus, when differences in outcomes between program tracks grow with time, we say outcomes exhibit an increasing-effects pattern. In this paper, we assert that outcomes of algorithm-driven DMPs for chronic mental illness may be more complex. Specifically, we postulate that the size of an effect may increase by a lump-sum amount that accrues during an initial period following baseline. After such an initial advantage, differences may either remain constant, or decline as DMP versus TAU differences become negligible with time. We call this initial rise, then fall, of a DMP advantage a declining-effects pattern. We thus (1) formulated an effect-statistic that could detect declining-effects patterns; (2) compared the power of both declining-effects and traditional growth statistics to detect an initial effect that either grows, remains constant, or declines with time; and (3) applied the statistic to evaluate an algorithm-driven disease-management program for outpatients with bipolar disorder.
Databáze: OpenAIRE
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