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These data are from a Spanish double-blind clinical trial in which 55 patients were randomized to fluoxetine (an SSRI) plus pindolol (a Beta Blocker) and 56 patients were randomized to fluoxetine plus placebo for treatment of major depressive disorder (MDD), Sacristan et al. (2000).
A data frame of 3 variables on 111 patients; no NAs.
Patients are considered to have responded to treatment when a 50% or greater decrease in HAMD-17 total score occurred between baseline and end-point (at day 42), with no more than 10% additional variation between intermediate visits.
Resource utilization was prospectively collected alongside the clinical trial. Patients and caregivers were interviewed by the researcher concerning all resources consumed during the study period. Resources dictated by the protocol were not counted. Costs are expressed in Pesetas (Pts.) at 1996 prices (1 Dollar = 145 Pts.) Observed differences in average direct medical costs were mainly due to hospitalizations within the FlxPin = 0 group.
Treatment indicator variable. FlxPin = 1 implies receipt of fluoxetine 20 mg/day plus pindolol 7.5 mg/day (2.5 mg tid). FlxPin = 0 implies receipt of fluoxetine 20 mg/day plus placebo (tid).
Since both samples are rather small (55 and 56 patients) here and the Effectiveness variable, respond, is binary, this example illustrates how the Law of Large Numbers can fail to apply to ICE inferences. Specifically, the bootstrap distribution of sample differences between AVERAGES appears to be quite different from bivariate normal in three ways: (i) The Bootstrap Distribution of ICE Uncertainty appears to consist of vertical stripes because the horizontal variable is discrete here while the vertical variable is continuous. (ii) The Bootstrap Distribution of cost differences appears to end somewhat abruptly near the horizontal axis at DeltaCost = 0, rather than have a long upwards tail like its downwards tail. (iii) The equal density contours of the bivariate Bootstrap Distribution appear to NOT be elliptical. This third point can be dramatically illustrated by computing the Owen Empirical Likelihood contour that passes through the origin of the ICE plane.
Hamilton M. Development of a rating scale for primary depressive illness. British Journal of Social and Clinical Psychology 1967; 6: 278–296.
Sacristan JA, Obenchain RL. Reporting cost-effectiveness analyses with confidence. JAMA 1997; 277: 375.
Obenchain RL, Sacristan JA. In reply to: The negative side of cost-effectiveness ratios. JAMA 1997; 277: 1931–1933.
Sacristan JA, Gilaberte I, Boto B, Buesching DP, Obenchain RL, Demitrack M, Perez Sola V, Alvarez E, and Artigas F. Cost-effectiveness of fluoxetine plus pindolol in patients with major depressive disorder: results from a randomized, double blind clinical trial. Int Clin Psychopharmacol 2000; 15: 107–113.
Owen AB. Empirical Likelihood New York: Chapman and Hall/CRC. 2001.
Loading required package: lattice Incremental Cost-Effectiveness (ICE) Lambda Scaling Statistics Specified Value of Lambda = 1 Cost and Effe Differences are both expressed in cost units Effectiveness variable Name = respond Cost variable Name = cost Treatment factor Name = flxpin New treatment level is = 1 and Standard level is = 0 Observed Treatment Diff = 0.156 Std. Error of Trtm Diff = 0.089 Observed Cost Difference = -29361.751 Std. Error of Cost Diff = 15438.192 Observed ICE Ratio = -188012.873 Statistical Shadow Price = 173534.734 Power-of-Ten Shadow Price= 1e+05
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