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Therapy: Fundamental Concepts

  • Kameshwar Prasad
Chapter

Abstract

Health practitioners are rightly interested to know whether a new treatment works, whether it makes any difference, difference in what, in the outcome of his case or the prognosis of his patient. But there is a problem. The problem is that there are many factors which influence the outcome in a given patient. Some such factors are called prognostic factors, like age, sex, nature of the disease, disease severity, and co-morbidities. The other factors are biases and chance. If we can eliminate these factors and other treatments as the possible cause of a particular outcome, then we can be sure that the new treatment given to the patient has caused the outcome – whether beneficial or adverse. But it is impossible to eliminate the role of these factors. Hence it is difficult, if not impossible, to know for sure whether a given treatment makes a difference in the outcome. Researchers use several strategies to control these extraneous factors; one of which is to use a control group.

Keywords

Null Hypothesis Systolic Blood Pressure True Effect Risk Difference Malarial Parasite 
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.

Reference

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Further Reading

  1. Altman DG, Gore SM, Gardner MJ, Pocock SJ. Statistical guidelines for contributors to medical journals. In: Gardner MJ, Altman DG, editors. Statistics with confidence. Confidence intervals and statistical guidelines. London: Br Med J; 1989. p. 83–100.Google Scholar
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  4. Coronary Drug Project Research Group. Influence of adherence treatment and response of cholesterol on mortality in the Coronary Drug Project. N Engl J Med. 1980;303:1038–41.CrossRefGoogle Scholar
  5. Guyatt G, Rennie D, editors. User’s guides to the medical literature: a manual for evidence-based clinical practice. Chicago: AMA Press; 2002. (www.ama-assn.org).Google Scholar
  6. Kunz R, Oxman AD. The unpredictability paradox: review of empirical comparisons of randomised and non-randomised clinical trials. BMJ. 1998;317:1185–90.PubMedCrossRefGoogle Scholar
  7. Sacks H, Chalmers TC, Smith Jr H. Randomized versus historical controls for clinical trials. Am J Med. 1982;72:233–40.PubMedCrossRefGoogle Scholar
  8. Sacks HS, Chalmers TC, Smith Jr H. Sensitivity and specificity of clinical trials: randomized v historical controls. Arch Intern Med. 1983;143:753–5.PubMedCrossRefGoogle Scholar
  9. Yusuf S, Collins R, Peto R. Why do we need some large, simple randomized trials? Stat Med. 1984;3:409–22.PubMedCrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Kameshwar Prasad
    • 1
  1. 1.Department of Neurology Neurosciences Centre, and Clinical Epidemiology UnitAll India Institute of Medical SciencesNew Delhi DelhiIndia

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