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Statistics in clinical research: Important considerations


Affiliated Researcher and Consulting Statistician, School of Public Health, University of California Berkeley, Berkeley, CA 94704.7380, Saybrook University, Oakland, CA 94612, USA

Correspondence Address:
Dr. Howard Barkan
1411 Arch Street, Berkeley, CA 94708
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0971-9784.148325

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Year : 2015  |  Volume : 18  |  Issue : 1  |  Page : 74-82

 

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Statistical analysis is one of the foundations of evidence-based clinical practice, a key in conducting new clinical research and in evaluating and applying prior research. In this paper, we review the choice of statistical procedures, analyses of the associations among variables and techniques used when the clinical processes being examined are still in process. We discuss methods for building predictive models in clinical situations, and ways to assess the stability of these models and other quantitative conclusions. Techniques for comparing independent events are distinguished from those used with events in a causal chain or otherwise linked. Attention then turns to study design, to the determination of the sample size needed to make a given comparison, and to statistically negative studies.






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Affiliated Researcher and Consulting Statistician, School of Public Health, University of California Berkeley, Berkeley, CA 94704.7380, Saybrook University, Oakland, CA 94612, USA

Correspondence Address:
Dr. Howard Barkan
1411 Arch Street, Berkeley, CA 94708
USA
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0971-9784.148325

Rights and Permissions

Statistical analysis is one of the foundations of evidence-based clinical practice, a key in conducting new clinical research and in evaluating and applying prior research. In this paper, we review the choice of statistical procedures, analyses of the associations among variables and techniques used when the clinical processes being examined are still in process. We discuss methods for building predictive models in clinical situations, and ways to assess the stability of these models and other quantitative conclusions. Techniques for comparing independent events are distinguished from those used with events in a causal chain or otherwise linked. Attention then turns to study design, to the determination of the sample size needed to make a given comparison, and to statistically negative studies.






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