1 / 48

Data Management & Basic Analysis Interpretation of Diagnostic test

Data Management & Basic Analysis Interpretation of Diagnostic test. 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.

darius-mccall
Download Presentation

Data Management & Basic Analysis Interpretation of Diagnostic test

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Management & Basic AnalysisInterpretation 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/

  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 Quantitative Qualitative ORDINAL DISCRETE CONTINUOUS NOMINAL

  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 Frequency percentage

  34. Frequency and percentage

  35. Means

  36. Interpretation of Diagnostic test

  37. Angiography True False Total Stress testing Positive 11 76 65 Negative 35 89 124 200 Total 100 100 Interpretation of Diagnostic test How to assess the ability of Stress testing against angiography for coronary artery disease?

  38. 2 x 2 Tables in Clinical Epidemiology Used to assess the ability of a Diagnostic test Disease Status by a gold standard test True False Total New Test Positive a b a + b Negative c d c + d Total a + c b + c a + 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. Angiography True False Total Stress testing Positive 11 76 65 Negative 35 89 124 200 Total 100 100 Interpretation of Diagnostic test How to assess the ability of Stress testing against angiography for coronary artery disease? 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. Angiography True False Total Stress testing Positive 11 76 65 Negative 35 89 124 200 Total 100 100 Interpretation of Diagnostic test How to assess the ability of Stress testing against angiography for coronary artery disease? 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. Culture True False Total PCR Positive 11 76 65 Negative 35 89 124 200 Total 100 100 Exercise: 2 How to assess the ability of PCR against culture for TB? Sensitivity = Specificity = Positive LR = Negative LR =

  48. Thank you

More Related