Sensitivity analyses
REGRESSION
The influence of parameter uncertainty on the results was assessed through a one-way sensitivity analysis and presented in a tornado diagram. The incremental cognitive composite score was varied according to the upper and lower limits of the 95% confidence interval obtained from the regression. The personnel cost varied from being 50% reduced to being 20% increased. The low value was decided on the assumption that the cost of personnel would gradually be decreasing with the increasing number of participants. Concerning materials and other costs, we applied a range of ±20% from the baseline price. This could be explained by the fact that if the number of participants increased, the cost of materials and other costs would also increase. It is noteworthy that capital costs were around 1% of the total costs. Thus, the same range of ±20% was applied to test sensitivity analysis for the capital cost. The list of parameters, point estimates in deterministic and probabilistic sensitivity analyses are available in A probabilistic sensitivity analysis (PSA) was performed using Monte Carlo simulation, in which distributions were assigned to the uncertain model parameters. For the incremental cognitive composite score, a normal distribution was used with the mean and standard deviation set equal to the point estimate from the regression and its standard error, respectively. As for the cost parameters, uniform distributions were used, with ranges corresponding to those employed in the one-way sensitivity analysis. Assuming the parameters were independently distributed, we drew 1000 samples. We described these samples by creating a scatter plot that visualizes the joint distribution of incremental costs and incremental benefits. Additionally, we generated a cost-effectiveness acceptability curve to compare the intervention with the current practice.