Presentation is loading. Please wait.

Presentation is loading. Please wait.

Biostatistics Unit 10 Categorical Data Analysis 1.

Similar presentations


Presentation on theme: "Biostatistics Unit 10 Categorical Data Analysis 1."— Presentation transcript:

1 Biostatistics Unit 10 Categorical Data Analysis 1

2 Categorical data analysis deals with discrete data that can be organized into categories. The data are organized into a contingency table. The basic structure consists of two columns and two rows. The 2 distribution is used in categorical data analysis. 2

3 Basic Contingency Table Structure Basic structure of a 2X2 contingency table has two columns and two rows. 3

4 Structure of Contingency Tables Cells are labeled A through D. Columns and rows are added for labels. 4

5 Using the contingency table as a comparison table Comparison of outcomes in laboratory tests is studied using contingency tables. 5

6 Absolute and Relative Risk Relative risk is the ratio of two proportions. In each row is an absolute risk of getting the disease. The ratio of these two proportions is the relative risk. 6

7 Absolute Risk 7

8 Relative Risk 8

9 Example A total of 452 children in elementary schools in Georgia and Florida were served burritos for lunch. Among these, 304 children reported eating the burritos. Among those who ate burritos, 155 reported getting sick from bacterial contamination. There were also 148 children who did not eat burritos. Among these, 10 cases of illness were reported. A case of disease was defined as gastrointestinal upset, fever and other symptoms. The CDC studied this event using categorical data analysis. They reported relative risk, significance and a confidence interval. 9

10 Contingency Table Data from the reports of the incident were entered into a contingency table. 10

11 Absolute riskate burritos 11

12 Absolute riskdid not eat burritos 12

13 Relative Risk Relative risk is the ratio of the two absolute risk probabilities. Conclusion: A child who ate burritos had 7.06 times the probability of getting sick as one who did not. 13

14 Significance in relative risk Significance in relative risk is found using the 2 distribution. The general formula is below. 14

15 Significance in relative risk In contingency table calculations, the values from the table are used to give a 2 value according to the formula below. 15

16 Find significance using the TI-83 A. Matrix setup 16

17 Find significance using the TI-83 B.Calculation results Conclusion: With p this small, the result is highly significant. 17

18 CI for a Relative Risk Calculation The confidence interval consists of the usual components of estimator, reliability coefficient and standard error. Standard error is found using the formula 18

19 CI for a Relative Risk Calculation Logarithmic transformation is used because of the shape of the 2 curve 1 df which is hyperbolic. The antilog gives the boundaries of the confidence interval. 19

20 CI for a Relative Risk Calculation 20

21 CI for a Relative Risk Calculation 21

22 CI for a Relative Risk Calculation Take antilog to complete the calculation. Conclusion: The relative risk is We are 95% confident that the true value lies between and

23 Odds Ratio The odds come from the ratio of two proportions. The odds ratio is the ratio of these two odds. Odds ratio is generally calculated from data in a case control study. The following gives the theoretical basis for the calculation of odds ratio. The outcome is determined as the cross-product. 23

24 Contingency Table 24

25 Odds ratio and the contingency table The probability of being exposed and getting sick (success) is P(E). The probability of being exposed and not getting sick (failure) is 1 – P(E). The probability of getting sick when not exposed is P(E) while the probability of not getting sick when not exposed is 1 – P(E). 25

26 Determining Odds Ratio Odds of getting sick when exposed Odds of getting sick when not exposed 26

27 Determining Odds Ratio Odds ratio is the ratio of these two odds The probability values are related to the cells in the contingency table. 27

28 Determining Odds Ratio The final ratio of cells to find odds ratio This calculation of odds ratio is the cross- product of AD divided by BC. 28

29 Case study for odds ratio In the case control study, 52 children were involved. There were 13 children who ate the burritos among which 8 got sick. There were also 39 children who did not eat the burritos among which 6 reported symptoms of the illness. The odds ratio was calculated. 29

30 Odds Ratio Calculation Conclusion: The odds ratio is

31 Find significance using the TI-83 A.Matrix setup 31

32 Find significance using the TI-83 B.Calculation results Conclusion: p <

33 CI for an Odds Ratio Calculation Calculation for SE after logarithmic transformation 33

34 CI for an Odds Ratio Calculation 34

35 CI for an Odds Ratio Calculation Take antilog to complete the calculation. Conclusion: The odds ratio is 8.8. We are 95% confident that the true value lies between 2.14 and

36 fin 36


Download ppt "Biostatistics Unit 10 Categorical Data Analysis 1."

Similar presentations


Ads by Google