Statistics Applied to Clinical Trials pp 141-150 | Cite as
Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, Synergism
Chapter
Abstract
When the size of the study permits, important demographic or baseline value-defined subgroups of patients can be studied for unusually large or small efficacy responses; e.g. comparison of effects by age, sex; by severity or prognostic groups. Naturally, such analyses are not intended to “salvage” an otherwise negative study, but may be may be helpful in refining patient or dose selection for subsequent studies.1
Keywords
Linear Regression Line Efficacy Variable Residual Term Residual Standard Deviation Accelerate Failure Time Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Department of Health and Human Services, Food and Drug Administration. International Conference on Harmonisation; Guidance on Statistical Principles for Clinical Trials Availability. Federal Register, 63 (179), 1998: 49583–49598.Google Scholar
- 2.Hosmer DW, Lemeshow S. Applied Logistic Regression. New York: Wiley, 1989.MATHGoogle Scholar
- 3.Box Cox, Statistical Software, University Leyden, Netherlands, 1999.Google Scholar
- 4.Jukema AJ, Zwinderman AH, et al for the REGRESS study group. Effects of lipid lowering by pravastatin on progression and regression of coronary artery disease in symptomatic men with normal to moderately elevated serum cholesterol levels. The Regression Growth Evaluation Statin Study (REGRESS). Circulation. 1995; 91: 2528–2540.CrossRefGoogle Scholar
- 5.Rao CR. Linear Statistical Inference and Its Applications. New York: Wiley, 1973.CrossRefMATHGoogle Scholar
Copyright information
© Springer Science+Business Media Dordrecht 2006