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Data entry training for a small clinical trial
706
Abstracts
(of STEIN-type) for the regression parameters and a way for the diagnostic validation of the compared method. As a result of the proposal, the method comparison expedment should be performed only till the change of the clinical-chemical test method has (with a fixed probability) no consequences for the diagnostic decision. So the new procedure is cost optimal in the sense of the actual given task. A new quality characteristic called "diagnostic relevant residual variance" is defined. With the help of this characteristic the computation of the STOP-criterion only from the diagnostic demands is possible. The mathematical-statistical properties of the new strategy are demonstrated and the procedure of the method comparison experiment is referred.
PIO0 DATA ENTRY TRAINING FOR A SMALL CLINICAL TRIAL Scott D. Corley, Grace Ng, and Mary Jo GIIleeple
BiostaUsUcs Cardiovascular Research Unit University of Washington Seattle, Washington In August 1990, TRAP (Trial to Reduce AIIoimmunization to Platelets) Study Coordinators from seven Clinical Centers were trained in data entry on the PC. Top-down organization, balance between group instruction and hands-on practice, and handouts prepared in advance made it possible to provide in-depth training in each of the data entry procedures while maintaining the cohesiveness and momentum of the seminar. The seminar started with an overview of the data entry cycle and continued with coverage of each part of the cycle. Coverage of each part of the cycle, in turn, started with an overview and continued with detailed instruction and practice (top-down organization). One of the more difficult aspects of the training seemed to be that of balancing the amount of group instruction given with the amount of time devoted to practicing on the PCs. A good balance was struck by combining introductory group instruction with ample practice time and one-on-one assistance. Accommodation of different skill levels was important. Four types of practice forms and several work sheets containing hypothetical data were prepared in advance. Errors were intentionally included on some of the forms. The Coordinators were allowed to keep the handouts so that they could practice at their sites prior to the start of the study. A data entry manual was provided to serve as a step-bystep guide and detailed reference volume.
P1Ol DETECTING DATA ERRORS WITH STATISTICAL SCREENS Robert Ledlnghem, Mellssa Huther, Ruth McBride, Barbara Bane, Gunnel Hedelln, and Gunnel Schlyter
University of Washington Seattle, Washington The Cardiac Arrhythmia Suppression Trial (CAST) is a muiticenter RCT. Data in clinical records are transcribed to paper forms and entered. Range and logical checks are performed at data entry, and data must be verified before transmission to the Coordinating Center (CC). Certain key data are also elicited directly by the CC, and consistency is checked between these key data and the corresponding data entered at the clinic. Statistically screening the data to find "outliers" may catch further errors. The most desirable screens are those which minimize the work load (number of outliers to verify) and maximize the yield (number of errors found). An obvious screen for continuous variables is to consider a data point an outlier if it is ~> X standard deviations (SD) from the clinic-specific norm. We report here on pilot data (screening 8,478 data points) to determine the relative medts of using X = 3.0 SD vs. X = 2.5 SD. The method was: i) compute clinic-specific norms; ii) generate a report of outliers for each clinic; iii) clinic verifies value or, if incorrect, submits the correct value. =Outliers" True Outliers Errors Work
Load
SD = 2.5 + .................... 126 + .................... 7
SD --- 3.0 + .................... 55 -P . . . . . . . . . . . . . . . . . . . . 6
Jr-
-~
....................
133
....................
+ -I-}-
61
Abstracts
(of STEIN-type) for the regression parameters and a way for the diagnostic validation of the compared method. As a result of the proposal, the method comparison expedment should be performed only till the change of the clinical-chemical test method has (with a fixed probability) no consequences for the diagnostic decision. So the new procedure is cost optimal in the sense of the actual given task. A new quality characteristic called "diagnostic relevant residual variance" is defined. With the help of this characteristic the computation of the STOP-criterion only from the diagnostic demands is possible. The mathematical-statistical properties of the new strategy are demonstrated and the procedure of the method comparison experiment is referred.
PIO0 DATA ENTRY TRAINING FOR A SMALL CLINICAL TRIAL Scott D. Corley, Grace Ng, and Mary Jo GIIleeple
BiostaUsUcs Cardiovascular Research Unit University of Washington Seattle, Washington In August 1990, TRAP (Trial to Reduce AIIoimmunization to Platelets) Study Coordinators from seven Clinical Centers were trained in data entry on the PC. Top-down organization, balance between group instruction and hands-on practice, and handouts prepared in advance made it possible to provide in-depth training in each of the data entry procedures while maintaining the cohesiveness and momentum of the seminar. The seminar started with an overview of the data entry cycle and continued with coverage of each part of the cycle. Coverage of each part of the cycle, in turn, started with an overview and continued with detailed instruction and practice (top-down organization). One of the more difficult aspects of the training seemed to be that of balancing the amount of group instruction given with the amount of time devoted to practicing on the PCs. A good balance was struck by combining introductory group instruction with ample practice time and one-on-one assistance. Accommodation of different skill levels was important. Four types of practice forms and several work sheets containing hypothetical data were prepared in advance. Errors were intentionally included on some of the forms. The Coordinators were allowed to keep the handouts so that they could practice at their sites prior to the start of the study. A data entry manual was provided to serve as a step-bystep guide and detailed reference volume.
P1Ol DETECTING DATA ERRORS WITH STATISTICAL SCREENS Robert Ledlnghem, Mellssa Huther, Ruth McBride, Barbara Bane, Gunnel Hedelln, and Gunnel Schlyter
University of Washington Seattle, Washington The Cardiac Arrhythmia Suppression Trial (CAST) is a muiticenter RCT. Data in clinical records are transcribed to paper forms and entered. Range and logical checks are performed at data entry, and data must be verified before transmission to the Coordinating Center (CC). Certain key data are also elicited directly by the CC, and consistency is checked between these key data and the corresponding data entered at the clinic. Statistically screening the data to find "outliers" may catch further errors. The most desirable screens are those which minimize the work load (number of outliers to verify) and maximize the yield (number of errors found). An obvious screen for continuous variables is to consider a data point an outlier if it is ~> X standard deviations (SD) from the clinic-specific norm. We report here on pilot data (screening 8,478 data points) to determine the relative medts of using X = 3.0 SD vs. X = 2.5 SD. The method was: i) compute clinic-specific norms; ii) generate a report of outliers for each clinic; iii) clinic verifies value or, if incorrect, submits the correct value. =Outliers" True Outliers Errors Work
Load
SD = 2.5 + .................... 126 + .................... 7
SD --- 3.0 + .................... 55 -P . . . . . . . . . . . . . . . . . . . . 6
Jr-
-~
....................
133
....................
+ -I-}-
61