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P110 Analyses of repeated measurements data from two treatment groups
135s
Abstracts
Gray School of Medicine. RRC charges include quality assurance monitoring, providing Specifically, the RRC will resource information and facilitating CC communications. participate in annual quality assurance site visits to the east-coast CCs, act in a resource capacity to address east-coast CC questions about pre-defined study issues, participate in regional Principal Investigator and Staff conference calls, coordinate annual east-coast regional CC meetings and participate in some central training. As with any multicenter study, all WHI data will be transmitted and stored at the CCC. The presentation will focus on the rationale for utilizing a Regional Resource Center in a large multicenter clinical study to assume specified roles and will discuss the logistics involved in defining and implementing these roles. PllO ANALYSES OF REPEATED MEASUREMENTS FROM TWO TREATMENT GROUPS
DATA
David C. Huang Z?re Upjohn Company Kalamazoo, Michigan Different approaches for analyzing repeated measurements in clinical trials with two treatments are reviewed in this paper. Methods using mean summary statistics described by Frison and Pocock (Statistics in Medicine, 11 (1992) and the non-parametric two-sample tests proposed by Wei and Lachin (JASA, 79 (1984), and Wie and Johnson (Biometrika, 72 (1985) are illustrated with an example from a clinical study. Plll MISSING DATA IMPUTATION FOR CONTINUOUS OUTCOME MEASURES: THE DIETARY INTERVENTION STUDY IN CHILDREN (DISC) Robert P. McMahon, Sally Hunsberger and Bruce A. Barton Maryland Medical Research Institute Baltimore, Maryland Unobserved data are a potential source of bias in randomized clinical trials. Treatment comparisons based solely on observed outcome data could increase the risk of falsely declaring a treatment efficacious (Type 1 error) unless data are missing at random. In the DlSC trial, which compared a dietary-fat lowering special intervention (SI) to usual care (UC) in children with elevated LDL-cholesterol, the primary outcome was LDL at 36-months after enrollment (LDL36). LDL36 was missing in 8 % of UC children and 4% of SI children. A conservative imputation strategy was employed, in which missing LDL36 data in both UC and Sl were assumed to have the same distribution as observed data in the UC group. Bootstrap resampling to replace the missing data from the imputed distribution was used to estimate the mean difference between UC and Sl with a 95% confidence interval and to calculate a p-value. Observed and imputed estimates and test results were reported. A normal approximation was found to agree closely with the bootstrap results. Other assumptions regarding the distribution of missing data could be used with these methods, to test sensitivity of the study conclusions to the imputation selected. Imputation from a distribution using the bootstrap or normal approximation and conservative assumptions is a convenient and flexible way to take account of missing data in estimating treatment differences in clinical trials with continuous outcome measures.
Abstracts
Gray School of Medicine. RRC charges include quality assurance monitoring, providing Specifically, the RRC will resource information and facilitating CC communications. participate in annual quality assurance site visits to the east-coast CCs, act in a resource capacity to address east-coast CC questions about pre-defined study issues, participate in regional Principal Investigator and Staff conference calls, coordinate annual east-coast regional CC meetings and participate in some central training. As with any multicenter study, all WHI data will be transmitted and stored at the CCC. The presentation will focus on the rationale for utilizing a Regional Resource Center in a large multicenter clinical study to assume specified roles and will discuss the logistics involved in defining and implementing these roles. PllO ANALYSES OF REPEATED MEASUREMENTS FROM TWO TREATMENT GROUPS
DATA
David C. Huang Z?re Upjohn Company Kalamazoo, Michigan Different approaches for analyzing repeated measurements in clinical trials with two treatments are reviewed in this paper. Methods using mean summary statistics described by Frison and Pocock (Statistics in Medicine, 11 (1992) and the non-parametric two-sample tests proposed by Wei and Lachin (JASA, 79 (1984), and Wie and Johnson (Biometrika, 72 (1985) are illustrated with an example from a clinical study. Plll MISSING DATA IMPUTATION FOR CONTINUOUS OUTCOME MEASURES: THE DIETARY INTERVENTION STUDY IN CHILDREN (DISC) Robert P. McMahon, Sally Hunsberger and Bruce A. Barton Maryland Medical Research Institute Baltimore, Maryland Unobserved data are a potential source of bias in randomized clinical trials. Treatment comparisons based solely on observed outcome data could increase the risk of falsely declaring a treatment efficacious (Type 1 error) unless data are missing at random. In the DlSC trial, which compared a dietary-fat lowering special intervention (SI) to usual care (UC) in children with elevated LDL-cholesterol, the primary outcome was LDL at 36-months after enrollment (LDL36). LDL36 was missing in 8 % of UC children and 4% of SI children. A conservative imputation strategy was employed, in which missing LDL36 data in both UC and Sl were assumed to have the same distribution as observed data in the UC group. Bootstrap resampling to replace the missing data from the imputed distribution was used to estimate the mean difference between UC and Sl with a 95% confidence interval and to calculate a p-value. Observed and imputed estimates and test results were reported. A normal approximation was found to agree closely with the bootstrap results. Other assumptions regarding the distribution of missing data could be used with these methods, to test sensitivity of the study conclusions to the imputation selected. Imputation from a distribution using the bootstrap or normal approximation and conservative assumptions is a convenient and flexible way to take account of missing data in estimating treatment differences in clinical trials with continuous outcome measures.