Biostatistics II


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Biostatistics II

  1. 1. 25-Jun-13 1 Biostatistics II Dr Fayssal M Farahat MBBCh, MSc, PhD Consultant Public Health Infection Prevention and Control Department Assist Professor, Public Health King Saud bin AbdulAziz University for Health Sciences King AbdulAziz Medical City, Jeddah, SA 2 Random Error Wrong result due to chance 20% Sample Size precision
  2. 2. 25-Jun-13 2 3 Measurement Observer Round down BP Leading Q Instrument Subject Recall bias (breast cancer and dietary fat) Calibration 4 Systematic Error Wrong result due to BIAS Sample (respondents) or Measurement (unclear Q) OR Accuracy Sample size
  3. 3. 25-Jun-13 3 5 Accuracy and Precision 6 Content validity Face validity Subjective judgment Sampling validity QOL: social, physical, emotional Construct validity Criterion- related validity Depressed and healthy Measure of depression if could predict suicide (future outcome)
  4. 4. 25-Jun-13 4 7 Confounding Variable Exposure Disease Confounding 8 Types of hypotheses NULL Ho NO association between predictor and outcome No difference between the aspirin and placebo The formal basis for testing statistical significance The association observed in a study is due to chance
  5. 5. 25-Jun-13 5 9 ALTERNATIVE H1 association between predictor and outcome Accepted by default, if test of significance rejects the null hypothesis Types of hypotheses 10 Truth in the population Association between predictor and outcome No Association between predictor and outcome Results in the study sample Reject null hypothesis Fail to Reject null hypothesis Correct Type I error Type II error Correct alpha beta False +ve False -ve
  6. 6. 25-Jun-13 6 11 False +ve  The investigators can reject the null hypothesis and conclude that there is a difference between the two treatment groups, when in fact there is no such difference exist. The probability of making such error is called p value ART 12 False negative  The investigators may fail to reject the null hypothesis that there is no difference between the two interventions, when in fact there is difference. The probability of making such error is called Beta BAF
  7. 7. 25-Jun-13 7 13 CI vs. P Value  Significance and precision 14 Statistic and clinical significance  Statistically sig results might not be clinically sig.  Statistically non sig results might be still clinically sig.  Effect of Bupropion on smoking cessation=  OR= 2.35 , P > 0.05  nothing to tell regarding clinical importance  OR= 2.35 (0.85, 6.47), CI lying in the side that favor treatment > 1 = there is a trend of positive effect of this medication  clinical sig although statistically non sig.
  8. 8. 25-Jun-13 8 15 Commonly used statistical tests  Chi-square test: To examine the relationship (association or difference) between two categorical variables.  2 by 2 or r by c Lung cancer control smokers A B Non-smokers C D McNemar’s test 16 Cont. statistical tests  Paired t test: used to compare the means of one variable in the same group (pre and post an event).  Wilcoxon’s matched pairs test
  9. 9. 25-Jun-13 9 17 Cont. statistical tests  Student’ t test: To evaluate the difference in means between two groups  Mann-Whitney test 18 Cont. statistical tests  ANOVA (F test): To test for the difference of means of the same variable between more than two groups.  Kruskall-Wallis test  LSD: To test for the difference of the means of the same variable between each two groups individually.  Following a significant F test
  10. 10. 25-Jun-13 10 19 Time Positive No relation Negative Variable X Y + - 0 20 Non parametric statistics  If sample size is very small “as small as 10”  Abnormally distributed data – Via histogram – Performing a normality test.  Scale of measurements (scores, titer).
  11. 11. 25-Jun-13 11 21  Statistically non significant findings are of the same importance as statistically significant findings.  Be sure of the distribution of your data before doing any statistical analysis. Student’s t test, Mann Whitney, Sign and Wilcoxon Signed Rank Tests
  12. 12. 25-Jun-13 12 • A single group of subjects and the goal is to compare an observed value or a norm or standard. • A single group that is measured twice and the goal is to estimate how much the mean in the group changes between measurements. • To determine if a difference exists between 2 independent groups. Group 1 Mean 1 SD 1 N 1 Group 2 Mean 2 SD 2 N 2
  13. 13. 25-Jun-13 13 Assumptions for the t distribution Assumption # 1 • The observations in each group follow a normal distribution. • Violating assumption of normality gives p values that are lower than they should be, making it easier to reject the null hypothesis and say there is difference when none exist. Assumption # 2 • SDs in the two samples are equal (homogenous variances). • The null hypothesis states that the two means are equal, or from the same population, so SDs are equal. • This assumption can be ignored when the sample sizes are equal. • t test is robust with equal sample sizes.
  14. 14. 25-Jun-13 14 Assumption # 3 • Independence= knowing observations in one group tells us nothing about the observations in the other group. • In the paired t test, we can expect that a subject with relatively low value at the first measurement to have a relatively low second measurement as well. • No statistical test can tell us about independence, so the best way is to design properly to ensure they are independent. Wilcoxon Signed Rank Test • No disadvantage in using Wilcoxon signed rank test in any situation with a small sample size, even when observations are normally distributed. • Non parametric statistic when paired t test is not the appropriate.
  15. 15. 25-Jun-13 15 Mann-Whitney-Wilcoxon rank test • Whether medians are different. • Rank all observations, then analyze the ranks as they were the original observations. • Mean and standard deviation of the ranks are calculated for the t test. • Test the hypothesis that the means of the ranks are equal in the two groups. Association between exposure of women to pesticides during pregnancy and birth defects in their offspring using data from a cohort study. Exposure Birth defects Yes Birth defects No Total Yes 20 980 1000 No 25 3975 4000 Total 45 4955 5000 Incidence (of birth defects) in exposed 20/1000= 0.02 Incidence (of birth defects) in unexposed 25/4000= 0.00625 Relative Risk 0.02/0.00625= 3.20 (1.78, 5.74) If you would like to calculate Odds Ratio? (20 X 3975) / (25 X 980) = 3.24 (1.79, 5.87)
  16. 16. 25-Jun-13 16 31 The most important is to understand the concepts to interpret the clinical research.