Methods to analyse cost data of XXXX who withdraw in a clinical trial setting.

@entity1 @entity5489 @entity13265 @entity1030 @entity7634
BACKGROUND: Missing data resulting from premature study withdrawal are a common problem in the analysis of longitudinal data in clinical trials. To date, this subject has received little attention in the context of economic evaluations and with regard to the analysis of cost data. OBJECTIVES: To (i) demonstrate the impact of @entity1 who drop out during the study on the outcomes of an economic evaluation, and (ii) to compare the mean and variation in costs after applying five different methods to deal with incomplete data: multiple imputation, complete cases analysis, linear extrapolation, predicted mean and hot decking. STUDY DESIGN: The study was performed using cost data collected in two randomised clinical trials comparing @entity1 with @entity1030 receiving either @entity5489 or @entity13265 . The overall dropout rate was 17%, with the daily costs of the dropouts approximately 4 times higher than the costs of the completers. METHODS: Multiple imputation is a principled method that deals with missing observations by replacing each missing observation with a set of multiple plausible values. The variance between the resulting multiple datasets is combined with the variance between the datasets to take account of the extra uncertainty that results from missing data. The outcomes after multiple imputation were compared with the results of four naive methods to deal with missing observations: complete cases analysis, linear extrapolation, predicted mean and hot decking. All costs were expressed in 2001 @entity7634 . RESULTS: In the @entity5489 group, mean (standard error) costs varied from @entity7634 955 (137) after complete cases analysis to @entity7634 1298 (198) after linear extrapolation. The corresponding estimates in the @entity13265 group were @entity7634 970 (125) and @entity7634 1561 (244), respectively. The difference in costs between treatment groups varied from - @entity7634 15 (95% CI: -379 to 349) after complete cases analysis to - @entity7634 402 (95% CI: -883 to 79) after predicted mean, in favour of the @entity5489 group. The difference in costs according to the other methods varied from - @entity7634 263 (95% CI: -878 to 353) after linear extrapolation to - @entity7634 265 (95% CI: -709 to 180) after multiple imputation to - @entity7634 359 (95% CI: -771 to 54) after hot decking. CONCLUSION: This study showed that the method of dealing with the data of the dropouts had a large impact on the outcomes of an economic evaluation. Information about the rate of @entity1 withdrawal and the way data of dropouts are treated is of vital importance in assessing the results of economic evaluations and should always be reported. Multiple imputation is a principled method that can be used to deal with the data of these @entity1 .
