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To estimate cost and effect differences, relating confidence intervals and cost-effectiveness ratios bootstrapping techniques will be used.
Parametric (e.g. ANCOVA) and non-parametric bootstrapping techniques will be employed to compare the costs between groups, and to estimate a confidence interval around the mean difference [ 25].
Non parametric bootstrapping techniques will be used to estimate 95% confidence intervals for the mean differential cost between conservative and surgical treatment [ 19].
The assumption of linearity will be assessed by residual analysis; if necessary bootstrapping techniques will be employed.
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To assess uncertainty with regard to incremental cost-effectiveness a standard bootstrap technique will be applied [ 35].
1) A bootstrap technique will be used to estimate the costs from a patient and health insurer's perspective.
Non-parametric bootstrap re-sampling techniques will be used to explore uncertainty around estimates of cost-effectiveness derived from the study sample [ 76].
Bootstrapping 38 and Monte Carlo simulation techniques will be used for trial analyses and modelling studies respectively to generate the sampling distribution of the mean costs and effects, quantifying this uncertainty.
Standardized economic evaluation techniques will be used including incremental analysis of mean differences and bootstrapping to determine confidence intervals.
Our field techniques will be pretty simple.
Bad management techniques will be exposed.
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