Open AccessJournal Article
Basic statistics for clinicians: 3. Assessing the effects of treatment: measures of association.
Roman Jaeschke,Gordon H. Guyatt,Harry S. Shannon,Stephen D. Walter,Deborah J. Cook,Nancy M. Heddle +5 more
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TL;DR: The calculation of measures of association are shown and their usefulness in clinical decision making is discussed and both the absolute risk reduction and the number needed to treat reflect both the baseline risk and the relative risk reduction.
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Abstract: In the third of a series of four articles the authors show the calculation of measures of association and discuss their usefulness in clinical decision making. From the rates of death or other "events" in experimental and control groups in a clinical trial, we can calculate the relative risk (RR) of the event after the experimental treatment, expressed as a percentage of the risk without such treatment. The absolute risk reduction (ARR) is the difference in the risk of an event between the groups. The relative risk reduction is the percentage of the baseline risk (the risk of an event in the control patients) removed as a result of therapy. The odds ratio (OR), which is the measure of choice in case-control studies, gives the ratio of the odds of an event in the experimental group to those in the control group. The OR and the RR provide limited information in reporting the results of prospective trials because they do not reflect changes in the baseline risk. The ARR and the number needed to treat, which tells the clinician how many patients need to be treated to prevent one event, reflect both the baseline risk and the relative risk reduction. If the timing of events is important--to determine whether treatment extends life, for example--survival curves are used to show when events occur over time.
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Clinical versus statistical significance: interpreting P values and confidence intervals related to measures of association to guide decision making.
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TL;DR: The goal is to provide a clear picture of the individual components of the immune system and provide a strategy for individualized treatment of these components according to their Kesslerian importance.
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TL;DR: Examining the architecture of the entire set of subgroups within a trial, analyzing similar subgroups across independent trials, and interpreting the evidence in the context of known biologic mechanisms and patient prognosis are recommended.
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A consumer's guide to subgroup analyses.
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TL;DR: Guidelines are provided in this paper that will assist clinicians in making decisions regarding whether to base a treatment decision on overall results or on the results of a subgroup analysis.
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Users' Guides to the Medical Literature: II. How to Use an Article About Therapy or Prevention B. What Were the Results and Will They Help Me in Caring for My Patients?
Gordon H. Guyatt,David L. Sackett,Deborah J. Cook,Gordon Guyatt,Eric Bass,Patrick Brill-Edwards,George P. Browman,Deborah Cook,Michael E. Farkouh,Hertzel C. Gerstein,Brian Haynes,Robert Hayward,Anne Holbrook,Roman Jaeschke,Elizabeth F. Juniper,Andreas Laupacis,Hui Lee,Mitchell Levine,Virginia Moyer,Jim Nishikawa,Andrew D Oxman,Ameen Patel,John Philbrick,W. Scott Richardson,Stephane Sauve,David L. Sackett,Jack Sinclair,K. S. Trout,Peter Tugwell,Sean Tunis,Stephen D. Walter,John W Williams,Mark C. Wilson +32 more
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