impaginato 2-2009:imp. BIS 2/2005 17/12/09 11:59 Pagina 19A G G I O R N A M E N T O I N T E M A D I B I S F O S F O N A T I BISFOSFONATI ED EFFETTI SCHELETRICI Ombretta Di Munno, Andrea Delle Sedie U.O. Reumatologia, Dipartimento di Medicina Interna, Università di Pisa INTRODUZIONE Il tumore della mammella (CM) è il tipo di neoplasia maligna più comune nel- la donna, con un’incidenz
05335v3.qxdUsing Linear Regressions to Approximate Results of Decision Analysis: An Application
to a Cost Comparison Across Three First-Line Drug Strategies in Type 2 Diabetes
Botteman MF, Gao X, Stephens JM
HERQuLES, Abt Associates Clinical Trials, Bethesda, MD, USA
A literature-based decision tree model was Clinical and Economic Inputs
developed to project the number of patients Conclusions
The long-term, aggressive treatment of diabetes therapies and the associated costs over a Results from the baseline analysis and the Both methods demonstrate similar results that is cost effective but often requires the use of three-year timeframe (Figure 1).
SOMCS are presented in Table 1.
a sulfonyluea strategy with glipizide GITS is the Primary failures after first-line therapy leading multiple therapies to achieve American Diabetes least expensive first-line therapy for newly A hypothetical cohort of 1,000 patients was Regression models fitted from SOMCS results diagnosed type 2 diabetes patients, followed by Discontinuation due to adverse events leading assigned to begin one of the three first-line are presented in Table 2.
Few studies have addressed the clinical and strategies. For each first-line treatment cohort, Comparison of Results of SOMCS Model and economic impact of initiating patients on specific z Proportion of patients controlled with addition the model generated the cumulative cost per of a second or third agent if the patient was treated patient at the end of three years.
z An additional MCS was run and the inputs The analytical perspective was that of a payer, z Secondary failures over time leading to were used in both the SOMCS and LR models.
incorporating only direct medical costs. The cycle The LR can be used as a quick tool for use in useful tool to address these impacts. However, z Both methods led to identical conclusions length in the model was three months to reflect Medical resource use(1) and associated costs these techniques are complex and often not the frequency of physician office visits.(1) expensive in >97% of cases. The accordance decision tree, and providing decision support understood by decision makers. Simplified Long-term diabetic complications were excluded z Office visits, laboratory tests, patient between projected costs across methods was methods that allow end users to easily use and due to the short-term nature of the model.
statistically significant (Kappa>0.80, p<0.001) customize these decision models are very much in each head-to-head comparison, confirming the feasibility of using the LR to approximate References
the results of the decision analysis.
z Treatment of clinical failures and adverse 1. Standards of medical care for patients with Figure 1. Model Pathways and Outcomes
Table 1. Cost/Net Cost of Three-Year Treatment Per Patient*
diabetes mellitus. Diabetes Care 2001; 24:1-24.
To project and compare via a previously Costs assigned for resources were 2001-2002 Drug Comparison
Base Case Model
F irst-L in e T h erapy
2. Ramsdell et al. Economic model of first-line In itial M on oth erap y
glyburide/m etform in, rosiglitazone, or repaglinide three-year direct medical costs associated with average wholesale prices. All input data are glycemic control in newly diagnosed type 2 three common first-line oral antidiabetic classes diabetes. Pharmacoeconomics 2003; in press.
Second-Order Monte-Carlo Simulation and
Sulfonylurea: glipizide gastrointestinal therapeutic system (Glucotrol XL - GXL) Regression Analysis
Triangular distributions were used to describe the z Biguanide: generic metformin (MET), and The “Net Cost” of Drug 1 vs. Drug 2 is defined as Drug 1 cost minus Drug 2 cost. variability of the model input in the SOMCS.
Table 2. Regression Results for Three-Year Treatment Cost Per Patient
To summarize the model in an LR form, the costs (dependent variables) estimated via 1,000 MCS Regression 1
DV = GXL Cost
DV = METIR Cost
DV = ROS Cost
To evaluate the feasibility and usefulness of runs were summarized through ordinary least- Variables
summarizing the decision analytic model’s square regressions, using the most sensitive and/or relevant variables from the decision model as predictors (identified via Tornado diagrams).
Specifically, each MCS run was treated as a ran- dom observation. Three regression models were generated to predict three-year treatment cost of Overview
The target treatment population consisted of -1328.60* GXL monotherapy drug cost as switch The results generated via each method were patients newly diagnosed with type 2 diabetes compared. Multiple tests were conducted to determine the predictive abilities of the regres- 1. Unless specified, all p < 0.001. *p > 0.05.
sion against the results of the traditional model.
2. Regression Model 1: R2 =0.53, F = 111.20, p <0.001; Regression Model 2: R2 =0.49, F = 95.29, p < 0.001; Regression Model 3: R2 = 0.49, F = 96.20, p < 0.001.
Poster prepared at www.SciFor.com
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