In analyzing data from clinical trials and longitudinal studies, the issue of missing values is always a fundamental challenge since the missing data could introduce bias and lead to erroneous statistical inferences. ...In analyzing data from clinical trials and longitudinal studies, the issue of missing values is always a fundamental challenge since the missing data could introduce bias and lead to erroneous statistical inferences. To deal with this challenge, several imputation methods have been developed in the literature to handle missing values where the most commonly used are complete case method, mean imputation method, last observation carried forward (LOCF) method, and multiple imputation (MI) method. In this paper, we conduct a simulation study to investigate the efficiency of these four typical imputation methods with longitudinal data setting under missing completely at random (MCAR). We categorize missingness with three cases from a lower percentage of 5% to a higher percentage of 30% and 50% missingness. With this simulation study, we make a conclusion that LOCF method has more bias than the other three methods in most situations. MI method has the least bias with the best coverage probability. Thus, we conclude that MI method is the most effective imputation method in our MCAR simulation study.展开更多
Missing data can frequently occur in a longitudinal data analysis. In the literature, many methods have been proposed to handle such an issue. Complete case (CC), mean substitution (MS), last observation carried forwa...Missing data can frequently occur in a longitudinal data analysis. In the literature, many methods have been proposed to handle such an issue. Complete case (CC), mean substitution (MS), last observation carried forward (LOCF), and multiple imputation (MI) are the four most frequently used methods in practice. In a real-world data analysis, the missing data can be MCAR, MAR, or MNAR depending on the reasons that lead to data missing. In this paper, simulations under various situations (including missing mechanisms, missing rates, and slope sizes) were conducted to evaluate the performance of the four methods considered using bias, RMSE, and 95% coverage probability as evaluation criteria. The results showed that LOCF has the largest bias and the poorest 95% coverage probability in most cases under both MAR and MCAR missing mechanisms. Hence, LOCF should not be used in a longitudinal data analysis. Under MCAR missing mechanism, CC and MI method are performed equally well. Under MAR missing mechanism, MI has the smallest bias, smallest RMSE, and best 95% coverage probability. Therefore, CC or MI method is the appropriate method to be used under MCAR while MI method is a more reliable and a better grounded statistical method to be used under MAR.展开更多
The scientific foundation of a modern clinical trial is randomization–each patient is randomized to a treatment group,and statistical comparisons are made between treatment groups.Because the study units are individu...The scientific foundation of a modern clinical trial is randomization–each patient is randomized to a treatment group,and statistical comparisons are made between treatment groups.Because the study units are individual patients,this‘one patient,one vote’principle needs to be fol-lowed–bothinstudydesignandindataanalysis.Fromthephysicians’pointofview,eachpatient is equally important,and they need to be treated equally in data analysis.It is critical that statisti-cal analysis should respect design and study design is based on randomization.Hence from both statistical and medical points of view,data analysis needs to follow this‘one patient,one vote’principle.Under ICH E9(R1),five strategies are recommended to establish‘estimand’.This paper discusses how to implement these strategies using the‘one patient,one vote’principle.展开更多
文摘In analyzing data from clinical trials and longitudinal studies, the issue of missing values is always a fundamental challenge since the missing data could introduce bias and lead to erroneous statistical inferences. To deal with this challenge, several imputation methods have been developed in the literature to handle missing values where the most commonly used are complete case method, mean imputation method, last observation carried forward (LOCF) method, and multiple imputation (MI) method. In this paper, we conduct a simulation study to investigate the efficiency of these four typical imputation methods with longitudinal data setting under missing completely at random (MCAR). We categorize missingness with three cases from a lower percentage of 5% to a higher percentage of 30% and 50% missingness. With this simulation study, we make a conclusion that LOCF method has more bias than the other three methods in most situations. MI method has the least bias with the best coverage probability. Thus, we conclude that MI method is the most effective imputation method in our MCAR simulation study.
文摘Missing data can frequently occur in a longitudinal data analysis. In the literature, many methods have been proposed to handle such an issue. Complete case (CC), mean substitution (MS), last observation carried forward (LOCF), and multiple imputation (MI) are the four most frequently used methods in practice. In a real-world data analysis, the missing data can be MCAR, MAR, or MNAR depending on the reasons that lead to data missing. In this paper, simulations under various situations (including missing mechanisms, missing rates, and slope sizes) were conducted to evaluate the performance of the four methods considered using bias, RMSE, and 95% coverage probability as evaluation criteria. The results showed that LOCF has the largest bias and the poorest 95% coverage probability in most cases under both MAR and MCAR missing mechanisms. Hence, LOCF should not be used in a longitudinal data analysis. Under MCAR missing mechanism, CC and MI method are performed equally well. Under MAR missing mechanism, MI has the smallest bias, smallest RMSE, and best 95% coverage probability. Therefore, CC or MI method is the appropriate method to be used under MCAR while MI method is a more reliable and a better grounded statistical method to be used under MAR.
文摘The scientific foundation of a modern clinical trial is randomization–each patient is randomized to a treatment group,and statistical comparisons are made between treatment groups.Because the study units are individual patients,this‘one patient,one vote’principle needs to be fol-lowed–bothinstudydesignandindataanalysis.Fromthephysicians’pointofview,eachpatient is equally important,and they need to be treated equally in data analysis.It is critical that statisti-cal analysis should respect design and study design is based on randomization.Hence from both statistical and medical points of view,data analysis needs to follow this‘one patient,one vote’principle.Under ICH E9(R1),five strategies are recommended to establish‘estimand’.This paper discusses how to implement these strategies using the‘one patient,one vote’principle.
