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2011, Number 4

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Rev Med Inst Mex Seguro Soc 2011; 49 (4)

Clinical research IV. Relevancy of the stadistical test choosen

Talavera JO, Rivas-Ruiz R
Full text How to cite this article

Language: Spanish
References: 8
Page: 401-405
PDF size: 47.04 Kb.


Key words:

biomedical research, research design, statistics and numerical data.

ABSTRACT

When we look at the difference between two therapies or the association of a risk factor or prognostic indicator with its outcome, we need to evaluate the accuracy of the result. This assessment is based on a judgment that uses information about the study design and statistical management of the information. This paper specifically mentions the relevance of the statistical test selected. Statistical tests are chosen mainly from two characteristics: the objective of the study and type of variables. The objective can be divided into three test groups: a) those in which you want to show differences between groups or inside a group before and after a maneuver, b) those that seek to show the relationship (correlation) between variables, and c) those that aim to predict an outcome. The types of variables are divided in two: quantitative (continuous and discontinuous) and qualitative (ordinal and dichotomous). For example, if we seek to demonstrate differences in age (quantitative variable) among patients with systemic lupus erythematosus (SLE) with and without neurological disease (two groups), the appropriate test is the “Student t test for independent samples”. But if the comparison is about the frequency of females (binomial variable), then the appropriate statistical test is the χ2.


REFERENCES

  1. Armitage P, Berry G, Matthews JNS. Statistical methods in medical research. 4th ed. Massachusetts, MA: Blackwell Publishing; 2002.

  2. Bland M. Introduction to medical statistics. 3rd ed. Oxford: Oxford University Press; 2003.

  3. Feinstein AR. Clinical epidemiology. The architecture of clinical research. Philadelphia, PA: W.B. Saunders; 1985.

  4. Feinstein AR. Multivariable analysis: an introduction. New Haven, CT: Yale University Press; 1996.

  5. Feinstein AR. Principles of medical statistics. New York, NY: Chapman and Hall/CRC; 2002.

  6. Le Chap T. Introductory biostatistics. Hoboken, NJ: New Jersey: John Wiley and Sons; 2003.

  7. Peat J, Barton B. Medical statistics. A guide to data analysis and critical appraisal. Malden, MA: Blackwell Publishing; 2005.

  8. Portney LG, Watkins MP. Foundations of clinical research: applications to practice. 3rd ed. Saddle River, NJ: Pearson/Prentice Hall; 2009.




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C?MO CITAR (Vancouver)

Rev Med Inst Mex Seguro Soc. 2011;49