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On the design and analysis of randomized clinical trials with multiple end-points
Abs~ads
665 A123 ON THE DESIGN AND ANALYSIS OF RANDOMIZED CLINICAL TRIALS WITH MULTIPLE END-POINTS Nancy L. Geller, Del4n Tang, and Stuart J. Pocock National Heart, Lung, and Blood Institute National Institutes of Health Bethesda, Maryland
This paper considers some methods for reducing the number of significance tests undertaken when analyzing and reporting results of clinical tdals. Emphasis is placed on designing and analyzing clinical trials to examine several end-points simultaneously and combining this multiple end-point methodology with group sequential methodology. Four methods for multiple end-points are considered, an ordinary least squares and a generalized least squares approach beth due to O'Brien (Biometrics, 1984), a new modification of these and an approximate likelihood ratio test, due to Tang, Gnecco and Geller (Biometrika, 1989). These are extended for group sequential use. In particular, simulation is used to generate critical values and sequences of nominal significance levels for the approximate likelihood ratio test, which is not normally distributed. The relative merits of the suggested approaches are discussed.
A124 VISUALIZATION OF CLINICAL DATA WITH THE SAS(R) SYSTEM Gerhard Held SAS Institute GmbH Heidelberg, Germany Handling of clinical trials data involves easy access to the multitude of data, powerful facilities for data management, versatile capabilities in data analysis, and presentation of results in an informative way. There are standard tools available for analyzing clinical tdals data. These tools concentrate primarily on descriptive statistics of patients' data or on testing hypotheses on the safety and effectiveness of drugs. Whereas these tools cover an essential spectrum of clinical data analysis, they mostly fail to provide more in-depth understanding of patients' data, which can be attained by graphical data analysis techniques, a way to concurrently visualize multiple characteristics of patients' data. As part of a more general solution for clinical data processing this paper will discuss how a new highly interactive component of the SAS System can be employedto visualize clinical data. Techniques demonstrated will include identifying and labeling patients, brushing techniques for subgroups of patients, outlier detection, and viewing multivariate data in scatterplot matrices and three-dimensional rotating graphs.
A125 24-HOUR BLOOD PRESSURE MEASUREMENT; METHODS OF ANALYSIS AND DATA REQUIREMENTS Dorothy Dickson end Joerg Hasford Biometn'c Centre for Therapeutic Studies Munich, Germany Quasi-continuous ambulatory monitoring of blood pressure is frequently carried out. It avoids the bias usually present in occasional repeated recordings in a clinical setting ("White Coat Hypertension") and provides new means for evaluating prognostic factors and antihypertensive treatments. Areas of interest include quantifying the overall effect of a treatment or the comparison of treatments, which can be addressed by means of niveau-tests, in which 24-hour data is reduced to one parameter per patient (24h-mean or median or area under the 24h-profile). Missing data can be handled relatively easily and groups of patients can be compared with the usual statistical tests for continuous variables. The influence of antihypertensive treatments on circadian blood pressure rhythm is of additional interest. Methods which have been most often applied include fitting sine and cosine waves of specific frequencies in regression models and a non-parametric approach based on spline models. A restriction with the regression model is the assumption that only the a pnori chosen frequencies are present for all patients and the spline model requires optimization of knot sequences. No allowance is made for sedal correlations in these models. Spectral analysis offers a non-parametric time series approach to analyzing individual patient profiles without pdor assumption of specific frequencies in the data. Tests of white noise can also be applied. Requirements for the data are however considerable and include a large number of evenly spaced timepoints. Problems
665 A123 ON THE DESIGN AND ANALYSIS OF RANDOMIZED CLINICAL TRIALS WITH MULTIPLE END-POINTS Nancy L. Geller, Del4n Tang, and Stuart J. Pocock National Heart, Lung, and Blood Institute National Institutes of Health Bethesda, Maryland
This paper considers some methods for reducing the number of significance tests undertaken when analyzing and reporting results of clinical tdals. Emphasis is placed on designing and analyzing clinical trials to examine several end-points simultaneously and combining this multiple end-point methodology with group sequential methodology. Four methods for multiple end-points are considered, an ordinary least squares and a generalized least squares approach beth due to O'Brien (Biometrics, 1984), a new modification of these and an approximate likelihood ratio test, due to Tang, Gnecco and Geller (Biometrika, 1989). These are extended for group sequential use. In particular, simulation is used to generate critical values and sequences of nominal significance levels for the approximate likelihood ratio test, which is not normally distributed. The relative merits of the suggested approaches are discussed.
A124 VISUALIZATION OF CLINICAL DATA WITH THE SAS(R) SYSTEM Gerhard Held SAS Institute GmbH Heidelberg, Germany Handling of clinical trials data involves easy access to the multitude of data, powerful facilities for data management, versatile capabilities in data analysis, and presentation of results in an informative way. There are standard tools available for analyzing clinical tdals data. These tools concentrate primarily on descriptive statistics of patients' data or on testing hypotheses on the safety and effectiveness of drugs. Whereas these tools cover an essential spectrum of clinical data analysis, they mostly fail to provide more in-depth understanding of patients' data, which can be attained by graphical data analysis techniques, a way to concurrently visualize multiple characteristics of patients' data. As part of a more general solution for clinical data processing this paper will discuss how a new highly interactive component of the SAS System can be employedto visualize clinical data. Techniques demonstrated will include identifying and labeling patients, brushing techniques for subgroups of patients, outlier detection, and viewing multivariate data in scatterplot matrices and three-dimensional rotating graphs.
A125 24-HOUR BLOOD PRESSURE MEASUREMENT; METHODS OF ANALYSIS AND DATA REQUIREMENTS Dorothy Dickson end Joerg Hasford Biometn'c Centre for Therapeutic Studies Munich, Germany Quasi-continuous ambulatory monitoring of blood pressure is frequently carried out. It avoids the bias usually present in occasional repeated recordings in a clinical setting ("White Coat Hypertension") and provides new means for evaluating prognostic factors and antihypertensive treatments. Areas of interest include quantifying the overall effect of a treatment or the comparison of treatments, which can be addressed by means of niveau-tests, in which 24-hour data is reduced to one parameter per patient (24h-mean or median or area under the 24h-profile). Missing data can be handled relatively easily and groups of patients can be compared with the usual statistical tests for continuous variables. The influence of antihypertensive treatments on circadian blood pressure rhythm is of additional interest. Methods which have been most often applied include fitting sine and cosine waves of specific frequencies in regression models and a non-parametric approach based on spline models. A restriction with the regression model is the assumption that only the a pnori chosen frequencies are present for all patients and the spline model requires optimization of knot sequences. No allowance is made for sedal correlations in these models. Spectral analysis offers a non-parametric time series approach to analyzing individual patient profiles without pdor assumption of specific frequencies in the data. Tests of white noise can also be applied. Requirements for the data are however considerable and include a large number of evenly spaced timepoints. Problems