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Analytic aspects of single-subject trials
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Abstracts Imputing Missing Values Under Order Restrictions Bruce T h o m p s o n , Carol H a n d y , a n d Michael Terrin
Maryland Medical Research Institute, Baltimore, Maryland (68) The problem of excluding missing data from analyses can lead to substantial errors in estimations. Using actual data and simulations, we examine the bias in several models for imputing missing values. The situation of interest is when both the probability of being missing and the likelihood of a positive response change monotonically with respect to a covariate. The models examined are a ratio-allocation model, which imputes missing values on the basis of observed percentages, a logistic allocation model, which imputes missing values according to external covariates, and an order restricted model where the data are assumed to obey the above mentioned ordered relationships.
Analytic Aspects of Single-Subject Trials Robin Roberts, G o r d o n Guyatt, a n d Jana Keller
McMaster University, Hamilton, Ontario, Canada (69) Multiperiod crossover trials to determine efficacy in individual patients are popular at our institution. An N-of-1 service provides help to clinicians in randomization, outcome assessment, and analysis. Fifteen trials have been done to date in a variety of conditions. Typically a trial runs for 3-5 pairs of treatment periods with 3-6 measurements of functional status per period by questionnaire. Possible analytic approaches include ANOVA, time series, and randomization tests. We present data to show that autocorrelation is not a problem and thus repeated-measures ANOVA is appropriate. Usually there is a between period component of variance so that observations are essentially averaged within treatment period. Data on the presence of carry-over effects are presented. Randomization tests are less satisfactory with the relatively few treatment pairs available. Clinicians are often persuaded by the results before formal significance is achieved.
Experience with Distributed Data Analyses in the Cardia Study Gary Cutter, Laura Perkins, L y n n e W a g e n k n e c h t , D a v i d Martin, a n d S a m S h a n n o n for the C A R D I A S t u d y University of Alabama at Birmingham, Birmingham, Alabama (70) Low-cost PC hardware and analysis software has created the opportunity for collaborative studies to release the analysis process from centralized control. CARDIA has implemented a distributed data analysis system that permits each clinical center and coordinating center to perform primary analyses. This process has removed the coordinating center as the bottleneck in the initial writing phase. Responsibilities usually resident within a coordinating center have been distributed, such as outlier detection and documentation, choice of proper algorithms and statistical procedures, and complete programming documentation. Verification of final results has remained with the coordinating center using the official study database. Evolution of the process has led us to create a system that defines the degree of verification and the administrative process of conducting verification.
POSTER SESSION (P01-P81) A Geocentric Approach to Sponsor Conducted C l i n i c a l Trials L e o n a r d Jacob a n d Rita C a r e y
SK&F Labs, Philadelphia, Pennsylvania (POD A geocentric approach to clinical trials is being used to conduct drug development worldwide (WW). A WW Clinical R&D administration manages, coordinates, and supports all activities. Three units were established from which clinical investigations are conducted. These are North America, United Kingdom (Britain, Scandinavia, Australasia, South Africa), and the European Continent. A centrally located operations unit supports the clinical trials by preparing protocols and case report forms, managing clinical data, providing statistical services, monitoring safety information, and preparing clinical reports for registration to WW regulatory agencies. A WW Clinical R&D Executive Committee, composed of VPs from each clinical investigation unit and the VP of central operations, review Clinical Development Plans, monitor the progress of trials,
Abstracts Imputing Missing Values Under Order Restrictions Bruce T h o m p s o n , Carol H a n d y , a n d Michael Terrin
Maryland Medical Research Institute, Baltimore, Maryland (68) The problem of excluding missing data from analyses can lead to substantial errors in estimations. Using actual data and simulations, we examine the bias in several models for imputing missing values. The situation of interest is when both the probability of being missing and the likelihood of a positive response change monotonically with respect to a covariate. The models examined are a ratio-allocation model, which imputes missing values on the basis of observed percentages, a logistic allocation model, which imputes missing values according to external covariates, and an order restricted model where the data are assumed to obey the above mentioned ordered relationships.
Analytic Aspects of Single-Subject Trials Robin Roberts, G o r d o n Guyatt, a n d Jana Keller
McMaster University, Hamilton, Ontario, Canada (69) Multiperiod crossover trials to determine efficacy in individual patients are popular at our institution. An N-of-1 service provides help to clinicians in randomization, outcome assessment, and analysis. Fifteen trials have been done to date in a variety of conditions. Typically a trial runs for 3-5 pairs of treatment periods with 3-6 measurements of functional status per period by questionnaire. Possible analytic approaches include ANOVA, time series, and randomization tests. We present data to show that autocorrelation is not a problem and thus repeated-measures ANOVA is appropriate. Usually there is a between period component of variance so that observations are essentially averaged within treatment period. Data on the presence of carry-over effects are presented. Randomization tests are less satisfactory with the relatively few treatment pairs available. Clinicians are often persuaded by the results before formal significance is achieved.
Experience with Distributed Data Analyses in the Cardia Study Gary Cutter, Laura Perkins, L y n n e W a g e n k n e c h t , D a v i d Martin, a n d S a m S h a n n o n for the C A R D I A S t u d y University of Alabama at Birmingham, Birmingham, Alabama (70) Low-cost PC hardware and analysis software has created the opportunity for collaborative studies to release the analysis process from centralized control. CARDIA has implemented a distributed data analysis system that permits each clinical center and coordinating center to perform primary analyses. This process has removed the coordinating center as the bottleneck in the initial writing phase. Responsibilities usually resident within a coordinating center have been distributed, such as outlier detection and documentation, choice of proper algorithms and statistical procedures, and complete programming documentation. Verification of final results has remained with the coordinating center using the official study database. Evolution of the process has led us to create a system that defines the degree of verification and the administrative process of conducting verification.
POSTER SESSION (P01-P81) A Geocentric Approach to Sponsor Conducted C l i n i c a l Trials L e o n a r d Jacob a n d Rita C a r e y
SK&F Labs, Philadelphia, Pennsylvania (POD A geocentric approach to clinical trials is being used to conduct drug development worldwide (WW). A WW Clinical R&D administration manages, coordinates, and supports all activities. Three units were established from which clinical investigations are conducted. These are North America, United Kingdom (Britain, Scandinavia, Australasia, South Africa), and the European Continent. A centrally located operations unit supports the clinical trials by preparing protocols and case report forms, managing clinical data, providing statistical services, monitoring safety information, and preparing clinical reports for registration to WW regulatory agencies. A WW Clinical R&D Executive Committee, composed of VPs from each clinical investigation unit and the VP of central operations, review Clinical Development Plans, monitor the progress of trials,