1. Data Management & Basic Analysis Interpretation of Diagnostic test

2. Content of Today’s Presentation  Data Management –How to design questionnaire in Epi Info 3.4.3? –How to do data entry in Epi Info? –Data Cleaning  Basic Data Analysis  Validation of a Diagnostic test  Hands-on using Epi Info

3. Data Management: Designing Questionnaire in Epi Info

4. How to design a questionnaire using Epi Info  Epi Info – free, downloadable software provided by the CDC.  Website address is www.cdc.gov/epiinfo/www.cdc.gov/epiinfo/

5. How to design a questionnaire using Epi Info  Field or variable Types –Label/Title –Number –Text –Multiline –Phone number –Date

6. How to design a questionnaire using Epi Info  Data entry check code options –Required: prevents missing values –Repeat Last: automatically repeat the last value entered in that field –Range: sets minimum and maximum values –Legal Values: acceptable values, used with text fields –Comment Legal Values: similar to legal values, but only a code is saved during data entry and displayed in data analysis.

7. How to design a questionnaire using Epi Info  Steps to create a new questionnaire in Epi Info –Please follow the instructions from the hand-out given Overview of Make view

8. Example: Questionnaire

9. Hands-on for creating a questionnaire using Epi info

10. Data Management: Entering Data in Epi Info

11. Hands-on for entering data using Epi info

12. Exercise 1: design a questionnaire Participant’s General and Demographic Information

13. Data Management: Data Checking/Cleaning

14. Types of Variables Variables Qualitative Quantitative NOMINAL ORDINALDISCRETECONTINUOUS

15. Categorical Data  For example: Blood group (A=1, B=2, O=3, AB=4) –Data should consist values of only 1, 2, 3 or 4. –Missing values are coded as 9. –Other coding for Blood group, i.e., 0, 5, 6, 7 or 8 is clearly wrong.

16. Continuous Data  Cannot usually identify precisely which values are plausible and which are not.  Possible to specify lower and upper limits on what is reasonable for the variable concerned – range checking.

17. Continuous Data  Range Checking –for example, in a study of pregnancy, limits for maternal age might be 14 to 45 years. –for example, in a study of adult males, limit for systolic BP might be 70-250 mmHg.  Common cause of error: misplacing the decimal, may because of confusion or transcription error. –If the recorded value is plausible a misplaced decimal point may well go undetected. –Plausible but unlikely values should be corrected only if there is evidence of a mistake.

18. Logical checks  When the value of a variable that are reasonable but depend on the value of some other variable – logical checks. –For example, –7a. Are you studying currently? (No=1, yes=2) –7b. If ‘No’, what is your highest attained qualification?

19. Dates  Check that all dates are within a reasonable time span.  Check that all dates are valid  Check that dates are correctly sequenced  Check that ages and time intervals

20. Outliers  Data for continuous variables may reveal of outlying values.  Few variables may have outliers but most variables will not have any.  Suspicious values should be carefully checked. –No evidence of a mistake and the value is plausible, then it should not be altered.

21. General Guidelines in Data Management  Rows in the datasheet should contain individual information - Record.  Each column should contain values of a single entity of all the individuals – Variable.  Variable name should not exceed more than eight characters.  Variables can be either numeric or string or alphanumeric.  A numeric variable must posses only numbers.  In any datasheet, identification number is must.

22.  Opening analysis screen  Reading/opening a project to analyze  Listing, sorting and selecting records  Defining new variables  Assigning values to new variables  Recoding existing variable into a new variable  Saving changes into a new data table Data Management using EPI Info

23. Opening Analysis Screen

24. Opening Analysis Screen …contd

25. Reading/Opening Analysis Screen …contd

26. Listing the records

27. Sorting the records

28. Selecting a subset the records

29. Defining new variables

30. Assigning values to new variables

31. Introduction to Basic Data Analysis

32. Descriptive Statistics

33. Descriptive Analysis Quantitative Mean Median Range/IQ Range SD Categorical CategoricalFrequencypercentage

34. Frequency and percentage

35. Means

36. Interpretation of Diagnostic test

37. How to assess the ability of Stress testing against angiography for coronary artery disease? Angiography Stress testing TrueFalseTotal Positive 65 1176 Negative 3589124 Total 100 200

38. 2 x 2 Tables in Clinical Epidemiology Used to assess the ability of a Diagnostic test Disease Status by a gold standard test New Test TrueFalseTotal Positive aba + b Negative cdc + d Total a + cb + ca + b + c + d

39. Sensitivity and Specificity  Sensitivity: proportion of actual positives which are correctly identified as such  Specificity: proportion of negatives which are correctly identified

40. Interpretation of Diagnostic test

41. How to assess the ability of Stress testing against angiography for coronary artery disease? Angiography Stress testing TrueFalseTotal Positive 65 1176 Negative 3589124 Total 100 200 Sensitivity = 65/100 = 65% Specificity = 89/100 = 89%

42. Positive and Negative Predictive Values  Positive predictive value: proportion of patients with positive test results who are correctly diagnosed  Negative predictive value : proportion of patients with negative test results who are correctly diagnosed  Depends upon the prevalence of the disease

43. Interpretation of Diagnostic test How to assess the ability of Stress testing against angiography for coronary artery disease? Angiography Stress testing TrueFalseTotal Positive 65 11 76 Negative 3589124 Total 100 200 PPV = 65/76 = 83.5% NPV = 89/124 = 71.8%

44. Likelihood Ratios  Likelihood ratio is independent of disease prevalence  Positive LR = 0.65/(1-0.89) = 5.9  Likelihood of a patient having disease has increased by six-fold given the positive test result.  Larger the positive LR, greater the likelihood of disease

45. Likelihood Ratios  Likelihood ratio is independent of disease prevalence  Negative LR = (1-0.65)/0.89 = 0.39  smaller the negative LR, lesser the likelihood of disease

46. Interpretation of a diagnostic test

47. Exercise: 2 How to assess the ability of PCR against culture for TB? Culture PCR TrueFalseTotal Positive 65 1176 Negative 3589124 Total 100 200 Sensitivity = Specificity = Positive LR = Negative LR =