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Bounds on correlation coefficients between dichotomous variables and their influence on variable selection
Abs~a~s
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graft was the unit and each observation was assumed to be independent. The assumption of independence of grafts was checked using a goodness-of-fit chi-square test. Data from a study of 120 patients at Wadsworth VA hospital and the VA coronary artery bypass graft study did not violate the assumption of independence.
Evaluation of Clinical Laboratory Data J.J. Tiede a n d W.A.T. Archambault, Jr., Bristol Laboratories, Syracuse, NY (55) The evaluation of laboratory data from clinical trials poses special problems to the statistician and clinician. The usual parametric methods of analysis (e.g., ANOVA of differences from baseline or log-differences from baseline) can yield results that are statistically significantbut not biologically meaningful, or vice versa. Long-term multiple dosing studies in which frequent laboratory samples are taken for analysis present additional problems. Repeated measures analysis of this large volume of data are often difficult to perform computationally and can lead to results which can be difficult to interpret. In this paper, a multiple evaluative approach to the analysis of clinical laboratory data is proposed. Included in this approach are procedures based on trends in individual patient data, evaluations of relevant extreme values, and endpoint analyses. The methodology is readily applied to large sets of data and leads to results that are interpretable by both statistician and clinician. In addition to outlining the proposed procedure, an example of the application of the procedure is presented.
R a n d o m i z a t i o n with Stratification and Institution Balance for M u l t i i n s t i t u t i o n Clinical Trials Madeline Bauer, Elbert Walker, a n d Carol Redmond, University of Pittsburgh, Pittsburgh, PA (56) Randomization procedures for multiinstitutionclinical trials are often designed to maintain treatment balance within institutions as well as within patient subgroups (strata) defined by several prognostic variables. Plans that produce the best balance within each institutionare deterministic. This lack of randomness invalidates the statistical basis of tests as well as permits bias in the selection of patients. A method of stratified randomization has been developed for use by the NSABP Statistical Unit, which achieves the desired treatment balance while preserving the randomness required to permit valid statistical tests and eliminate bias. This method can be implemented without the use of a computer. It can also be programmed to be used on a computer in time-sharing mode with simple procedures for manual back-up. Results comparing this method with other designs (e.g., Pocock and Simon, Biometrics, 1975) will be presented.
B o u n d s on Correlation Coefficients Between D i c h o t o m o u s Variables and Their Influence on Variable Selection Peter Peduzzi a n d Katherine Detre, VA Medical Center, Cooperative Studies Program Coordinating Center, West Haven, CT (57) The selection of prognostic factors from natural history data is routinely done in most clinical trials. Univariate screening methods based on the Pearson correlation coefficient (r) and chisquare are often performed before implementation of multivariate variable selection procedures. Methods based on the correlation coefficient must be interpreted with caution when the factors are dichotomous. In this case, the upper and lower bounds are not always + 1 and - 1 . The limits depend on the prevalence rates of the two factors in the study population. In general, the upper bound decreases as the prevalence rates become more discrepant. The limits of + 1 and - 1 occur for the special case when both prevalence rates are 0.5. Thus, for a fixed sample size, univariate selection based on the magnitude of the chi-square statistic may be misleading because chi-square is a function of r. These results may influence stepwise multiple regression programs where selection is based on the correlation with the dependent variable or its derivatives.
151
graft was the unit and each observation was assumed to be independent. The assumption of independence of grafts was checked using a goodness-of-fit chi-square test. Data from a study of 120 patients at Wadsworth VA hospital and the VA coronary artery bypass graft study did not violate the assumption of independence.
Evaluation of Clinical Laboratory Data J.J. Tiede a n d W.A.T. Archambault, Jr., Bristol Laboratories, Syracuse, NY (55) The evaluation of laboratory data from clinical trials poses special problems to the statistician and clinician. The usual parametric methods of analysis (e.g., ANOVA of differences from baseline or log-differences from baseline) can yield results that are statistically significantbut not biologically meaningful, or vice versa. Long-term multiple dosing studies in which frequent laboratory samples are taken for analysis present additional problems. Repeated measures analysis of this large volume of data are often difficult to perform computationally and can lead to results which can be difficult to interpret. In this paper, a multiple evaluative approach to the analysis of clinical laboratory data is proposed. Included in this approach are procedures based on trends in individual patient data, evaluations of relevant extreme values, and endpoint analyses. The methodology is readily applied to large sets of data and leads to results that are interpretable by both statistician and clinician. In addition to outlining the proposed procedure, an example of the application of the procedure is presented.
R a n d o m i z a t i o n with Stratification and Institution Balance for M u l t i i n s t i t u t i o n Clinical Trials Madeline Bauer, Elbert Walker, a n d Carol Redmond, University of Pittsburgh, Pittsburgh, PA (56) Randomization procedures for multiinstitutionclinical trials are often designed to maintain treatment balance within institutions as well as within patient subgroups (strata) defined by several prognostic variables. Plans that produce the best balance within each institutionare deterministic. This lack of randomness invalidates the statistical basis of tests as well as permits bias in the selection of patients. A method of stratified randomization has been developed for use by the NSABP Statistical Unit, which achieves the desired treatment balance while preserving the randomness required to permit valid statistical tests and eliminate bias. This method can be implemented without the use of a computer. It can also be programmed to be used on a computer in time-sharing mode with simple procedures for manual back-up. Results comparing this method with other designs (e.g., Pocock and Simon, Biometrics, 1975) will be presented.
B o u n d s on Correlation Coefficients Between D i c h o t o m o u s Variables and Their Influence on Variable Selection Peter Peduzzi a n d Katherine Detre, VA Medical Center, Cooperative Studies Program Coordinating Center, West Haven, CT (57) The selection of prognostic factors from natural history data is routinely done in most clinical trials. Univariate screening methods based on the Pearson correlation coefficient (r) and chisquare are often performed before implementation of multivariate variable selection procedures. Methods based on the correlation coefficient must be interpreted with caution when the factors are dichotomous. In this case, the upper and lower bounds are not always + 1 and - 1 . The limits depend on the prevalence rates of the two factors in the study population. In general, the upper bound decreases as the prevalence rates become more discrepant. The limits of + 1 and - 1 occur for the special case when both prevalence rates are 0.5. Thus, for a fixed sample size, univariate selection based on the magnitude of the chi-square statistic may be misleading because chi-square is a function of r. These results may influence stepwise multiple regression programs where selection is based on the correlation with the dependent variable or its derivatives.