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P114 Graphical presentation of longitudinal data analyses: The nhlbi growth and health study (NGHS)
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Abstracts
P114 GRAPHICAL PRESENTATION OF LONGITUDINAL DATA ANALYSES: THE NHLBI GROWTH AND HEALTH STUDY (NGHS) Shari L. Similo, Robert P. McMahon, Bruce A. Barton and Sue Y.S. Kimm Maryland Medical Research Institute Baltimore, Maryland Effective communication of results from longitudinal data analyses in clinical trials In and epidemiological studies can often be enhanced by appropriate graphical displays. NGHS, a cohort study of black and white girls followed from ages 9-10 through adolescence, the generalized estimating equation (GEE) method has been used to examine the effects on continuous dependent variables of: 1) age; 2) factors whose effects do not vary with age, and 3) factors whose effects change with age. Coefficients from regression models for such effects may be difficult to interpret. In the graphical display these coefficients can be expressed in terms of means and confidence intervals. Examples from a longitudinal analysis of the effects of race, sexual maturation and age upon body mass index will be presented. SAS macros to supplement GEE for producing such displays will also be discussed. P115 A REVIEW OF RECENT STATISTICAL METHODS FOR THE ANALYSIS OF THE 2X2 CROSSOVER DESIGN IN CLINICAL TRIALS Michael Stepanavage Merck Research Laboratories Rahway, New Jersey A wide array of statistical methodologies has been suggested in the analysis and interpretation of the two-period, two-treatment, crossover design in clinical trials. Various analysis strategies have focused on the inclusion of singular and multiple baseline measurements to use as a preliminary test for carryover effect, use of first period baseline measurements as potential covariate terms, and ignoring the test for carryover effects altogether. Additionally, multiple stage analyses that investigate carryover effects at the first stage tests and sequentially test for significant main effect terms at the second stage have also been proposed. A recurring problem across many of the above analysis strategies is the lack of power associated with the test for significant treatment x period interactions and the interpretation of treatment effects in the presence of significant carryover effect; methodologies addressing these problems wilI also be summarized. This paper will summarize and contrast an array of analysis strategies for the 2x2 crossover design and apply the varying techniques to an actual 2x2 clinical study.
Abstracts
P114 GRAPHICAL PRESENTATION OF LONGITUDINAL DATA ANALYSES: THE NHLBI GROWTH AND HEALTH STUDY (NGHS) Shari L. Similo, Robert P. McMahon, Bruce A. Barton and Sue Y.S. Kimm Maryland Medical Research Institute Baltimore, Maryland Effective communication of results from longitudinal data analyses in clinical trials In and epidemiological studies can often be enhanced by appropriate graphical displays. NGHS, a cohort study of black and white girls followed from ages 9-10 through adolescence, the generalized estimating equation (GEE) method has been used to examine the effects on continuous dependent variables of: 1) age; 2) factors whose effects do not vary with age, and 3) factors whose effects change with age. Coefficients from regression models for such effects may be difficult to interpret. In the graphical display these coefficients can be expressed in terms of means and confidence intervals. Examples from a longitudinal analysis of the effects of race, sexual maturation and age upon body mass index will be presented. SAS macros to supplement GEE for producing such displays will also be discussed. P115 A REVIEW OF RECENT STATISTICAL METHODS FOR THE ANALYSIS OF THE 2X2 CROSSOVER DESIGN IN CLINICAL TRIALS Michael Stepanavage Merck Research Laboratories Rahway, New Jersey A wide array of statistical methodologies has been suggested in the analysis and interpretation of the two-period, two-treatment, crossover design in clinical trials. Various analysis strategies have focused on the inclusion of singular and multiple baseline measurements to use as a preliminary test for carryover effect, use of first period baseline measurements as potential covariate terms, and ignoring the test for carryover effects altogether. Additionally, multiple stage analyses that investigate carryover effects at the first stage tests and sequentially test for significant main effect terms at the second stage have also been proposed. A recurring problem across many of the above analysis strategies is the lack of power associated with the test for significant treatment x period interactions and the interpretation of treatment effects in the presence of significant carryover effect; methodologies addressing these problems wilI also be summarized. This paper will summarize and contrast an array of analysis strategies for the 2x2 crossover design and apply the varying techniques to an actual 2x2 clinical study.