Biostatistics course Part 17 Non-parametric methods

1. Biostatistics coursePart 17Non-parametric methods Dr. C. Nicolas Padilla Raygoza Department of Nursing and Obstetrics Division of Health Sciences and Engineers Campus Celaya-Salvatierra University of Guanajuato

2. Biosketch • Médico Cirujano por la Universidad Autónoma de Guadalajara. • Pediatra por el Consejo Mexicano de Certificación en Pediatría. • Diplomado en Epidemiología, Escuela de Higiene y Medicina Tropical de Londres, Universidad de Londres. • Master en Ciencias con enfoque en Epidemiología, Atlantic International University. • Doctorado en Ciencias con enfoque en Epidemiología, Atlantic International University. • Profesor Asociado C, Department of Nursing and Obstetrics, Division of Health Sciences and Engineerings, Campus Celaya Salvatierra, University of Guanjuato. • padillawarm@gmail.com

3. Competencies • The reader will know the non-parametric methods and when he(she) can use them. • He (she) will apply non-parametric methods in an appropriate form. • He (she) can obtain a confidence interval in non-paramethric analysis • He (she) will apply Wilcoxon sum rank test • He (she) will apply Wilcoxon • He (she) will apply r Spearman.

4. Introduction • Parametric methods • They are base in means, standard deviations or probabilities. • The Normal distribution is not always appropriate • To study variables with a few observations, • Non-symmetrical distributions, or • Variables that can have more than two values

5. Introduction (contd…) • When this happens, we use other anaylisis methods • Non-parametric methods • They are not based in the same assumptions that parametric methods, but also have some assumptions.

6. Categories (ranking), means, medians • Some non-parametric methods use rankings en lugar de los real values. • Categories are use to compare data, more for their ranking that for their size.

7. Categories (ranking), means, median • Ranked in ascending order

8. Are mean and median equals? • To use mean and confidence interval is adequate, the distribution of values should be symmetric. • To the median and confidence intervals are adequate, no need for assumptions.

9. Are mean and median equals? • Using the order (ranking) instead of original values, reduces the need for assumptions about the distribution, the calculations are simpler and faster. • The disadvantage is that the original values are lost. • Thus, non-parametric methods are used only to test hypotheses, not for estimation purposes.

10. Non-parametric methods

11. Data of one sample • The table show data of glucose levels in blood from 11 patients. • We want to know if the mean is 100 mg/dl.

12. Data of one sample • Alternative no parametric test is Wilcoxon signed rank test. • It can be used to evaluate if the values in the sample are centered in 100 mg/dl. • This test does not require Normality of the distribution of data, but requires that the distribution is symmetrical, but not necessarily take the form of "bell" as Normal.

13. Data of one sample • Wilcoxon signed rank test is calculate by six steps: 1. To calculate the difference between each observation and the interest value, 100 mg/dl. 2. You should exclude any difference = 0. 3. To classify and order (ranking) differences by magnitude , not taken into accoun the sign. 4. Sum the rankings of positive differences. 5. Sum the rankings of negative differences. 6. Select the more little sums and call it T.

14. Data of one sample

15. Two independent groups • 30 teenagers with acute apendicitis, were distributed 15 to underwent traditional apendicectomia and 15 with laparoscopic apedicectomia. • For both groups, we evaluate post-surgical pain.

16. Two independent groups • To compate post-surgical pain in both groups, we can use Wilcoxon rank sum test. • We define the null hypothesis Ho: the two distributions overlap. • We define alternative hypothesis Hi: the two distributions are not overlap.

17. Two independent groups • Wilcoxon rank sum test has three steps: • We order the values in both groups in ascendant order. • To calculate T as the sum of rankings of more short sample or one of two if the sample size is equal. • To compare T-value in the critical values of Wilcoxon rank sum test.

18. Two independent groups

19. Two paired groups • The table show hours of improvement given by two analgesics in 12 patients with rheumatoid arthritis. • To test that the improvement is the same with both analgesics, we can use paired-t test or Wilcoxon signed ranking test. • With both methods, we calculate the difference of improvement in hours for each patient.

20. Two paired groups • With Wilcoxon signed rank test, it is no requirement the Normality, but the data should be symmetrical to both sides of the median. • Ho: difference in medians = 0 Hi= difference in medians≠ 0

21. Two paired groups • We calculate the Wilcoxon signed rank test for differences, making the following: 1.- Count how many differences non-zero. 2.- Order the differences by their magnitude, without take into account the sign. 3.- Sum rankings of positive differences. 4.- Sum rankings of negative differences. 5.- Select the more shor of the two sums and call it T. (Sum of negative differences = 59, sum of positive differences = 7, T=7). 6.- Compare the T-value in the critical values tables for Wilcoxon signed rank test. T=7, p<0.05.

22. Spearman’s correlation of ranks • Table and graphic show incidence of colon cancer and average of meat intake per capita in 13 countries.

23. Spearman ranks correlation • It is appropiate for monotonic relationships, non-lineal. • It is calculate at the same time that r’s Pearson, only using the rankings. • To calculate it, we need three steps: • To order the values of first variable, • To order the values of second variable, • To apply the formulae of r’s Pearson, using the rankings instead of original values.

24. Spearman ranks correlation

25. Comparison of methods

26. Bibliografy • 1.- Last JM. A dictionary of epidemiology. New York, 4ª ed. Oxford University Press, 2001:173. • 2.- Kirkwood BR. Essentials of medical ststistics. Oxford, Blackwell Science, 1988: 1-4. • 3.- Altman DG. Practical statistics for medical research. Boca Ratón, Chapman & Hall/ CRC; 1991: 1-9.