Exact(6)
Since cost data are often skewed, non-parametric bootstrapping will generate 95% CIs around point estimates.
Since cost data are often skewed, bootstrapping methods will be used to produce 95% confidence intervals alongside point estimates.
Since cost data are often characterized by non-normality and right-skewness, we used bootstrap techniques (4,000 replications) to estimate standard errors [ 35].
Since cost data will be available for the duration of the trial only, the appropriate effectiveness measure is the one that allows treatment effects during the trial only.
Since cost data per patient (but not per day care) are typically highly skewed, we used nonparametric bootstrap techniques to derive a 95% confidence interval for the differences in distributions of the direct medical costs.
Since cost data generally have a highly skewed distribution, we performed a non-parametric bootstrap analysis (1000 replications) to estimate uncertainty intervals around the difference in mean costs and to quantify the uncertainty surrounding the incremental cost-effectiveness ratio (ICER) [ 25, 26].
Similar(53)
This model functional form was chosen since our cost data were significantly right-skewed).
Since the cost data were assumed to be non-normally distributed, p-values were estimated using a general linear model while employing non-parametric bootstrap analysis (5,000 replications).
Since only average housing cost data were available in the data set, no statistical test was performed to assess if cost differences existed between the housing groups.
Since long-term outcome and cost data from randomized controlled prospective trials are rarely available when new health technologies emerge, evidence-based reimbursement policy requires retrospective data mining and lags behind medical and technical evolution [ 9].
One study without cost data was included since it provided data on coping strategies [ 29].
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