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Stata Files For Costs and Cost-Effectiveness

Sampling Uncertainty for Cost-Effectiveness Analysis

The Sampling Uncertainty for Cost-Effectiveness Analysis button below provides Stata programs, data, and documentation for assessing sampling uncertainty that arises in cost-effectiveness analysis. The programs calculate confidence intervals for cost-effectiveness ratios, acceptability curves, net monetary benefits, and value of information. There are also programs for plotting many of these measures.

Many of the programs base calculations on parameters such as the difference in means, the standard error of the difference, and the correlation of the difference in means, which can be estimated by use of any number of statistical methods. For these types of programs, examples in the documentation report the parameter estimates that are being assumed in the example.

Some of the programs (in uprogs.do) use t-tests to estimate the difference in costs and effects in a dataset that is being analyzed. In the documentation, examples of these programs use the eeict11.dta dataset that is included in the .zip file. These programs will work with any dataset that has variable that represents per-patient costs, per-patient effects, and a treatment group.

Other of the programs (in bsceaprogs.do) take the results of a bootstrap analysis or a second-order Monte Carlo simulation and use them to make the calculations. In the documentation, examples of these programs use the bsceaexample.dta dataset that is included in the .zip file. These programs will work with any dataset that has variables that represent replicates of the difference in cost and difference in effect (each observation represents the difference in cost and difference in effect from either a bootstrap analysis or a second order Monte Carlo simulation).

For background about confidence intervals for cost-effectiveness ratios, acceptability curves, net monetary benefits, and value of information, see chapters 8 and 9 in Glick, et al., Economic Evaluation in Clinical Trials, the lectures on sampling uncertainty in the EPI 550 Medical Decision Making and Clinical Economics and Lectures by Invitation tabs, or articles on sampling uncertainty cited in the Publications tab.

Sample Size and Power for Cost-Effectiveness Analysis

The Sample Size and Power for Cost-Effectiveness Analysis button below provides Stata programs and documentation for assessing sample size and power for cost-effectiveness analysis.

No datasets accompany these programs. Instead, the programs base calculations on parameters such as the difference in means, the standard deviations of the means, the correlation of the difference in cost and difference in effect, etc. Examples in the documentation report the parameter estimates that are being assumed in the example.

For background about sample size and power for cost-effectiveness analysis, see chapter 9 in Glick, et al., Economic Evaluation in Clinical Trials, the lectures on sample size and power in the Lectures by Invitation tab, or articles on sample size and power cited in the Publications tab.

Univariate Analysis of Costs (or QALYs….)

The Univariate Analysis of Cost button provides a Stata .do file, a dataset, and documentation for performing univariate analysis of cost (or of any other continuous variable). The .do file provides code that is tailored to the specific dataset that is provided, but can be modified for the analysis of any continuous variable.

Unlike the .do files on the sampling uncertainty and sample size pages, “Doing†the .do file here performs calculations and produces a .log file.

The dataset here is the same as the dataset provided on the sampling uncertainty tab.

For background about univariate analysis of cost, chapter5 in Glick, et al., Economic Evaluation in Clinical Trials, the lecture on cost analysis in the EPI 550 Medical Decision Making and Clinical Economics and Lectures by Invitation tabs, or articles on cost analysis cited in the Publications tab.

Identifying Better Links and Families for GLM and GEE Models

The Identifying Better Links and Families button provides Stata programs, data, and documentation for assessing links and families for GLM and GEE models. The programs report fit statistics for these models. Two of the programs (glmdiagl and glmdiagl2) make recommendations about links and families.

For background about links and families, see the program documentation, chapter 5 in Glick, et al., Economic Evaluation in Clinical Trials or the lectures on cost analysis in the EPI 550 Medical Decision Making and Clinical Economics and Lectures by Invitation tabs. For the latter, particularly see “Identifying an Appropriate Link and Family for Generalized Linear Models,†ISPOR 20th Annual International Meeting, May 19, 2015, Philadelphia, Pennsylvania.