1. Performing the Study Data Collection
Paula Peyrani, MD
Director Clinical Research Program
Infectious Diseases Division, University of Louisville
Louisville, Kentucky, USA

2. Objectives To review important tools for the collection
of clinical data.
Data collection form
Pretesting
Data entry
Data management
Study database

3. Objectives To review important tools for the collection
of clinical data.
Data collection form
Pretesting
Data entry
Data management
Study database

4. Data Collection Form (CRF) Confounding Variables
Performing the Study
Data Collection Form
Data Collection Form (CRF)
Inclusion Criteria
Exclusion Criteria
Predictor Variables
Outcome Variables
Confounding Variables

5. Performing the Study Data Collection Form Data Collection Form (CRF)
Entries that involve
judgment should be summarized briefly
on the data collection
form and explain
with more detail on
the study manual
Inclusion Criteria
Exclusion Criteria
Predictor Variables
Outcome Variables
Confounding Variables

6. with more detail on the study manual Criteria for diagnosis of CAP:
Performing the Study
Data Collection Form
Entries that involve judgment should be summarized briefly on the data collection form and explain
with more detail on the study manual
Criteria for diagnosis of CAP:
New pulmonary infiltrate
New or increased cough
Fever or hypothermia
Changes in WBC

7. with more detail on the study manual Criteria for diagnosis of CAP:
Performing the Study
Data Collection Form
Entries that involve judgment should be summarized briefly on the data collection form and explain
with more detail on the study manual
Criteria for diagnosis of CAP:
New pulmonary infiltrate (at time of hospitalization)
New or increased cough
Fever >37.8o C (100.0o F) or hypothermia <35.6o C (96.0o F)
Changes in WBC (leukocytosis, left shift, or leukopenia)
Mention the problem with the European group with the ABG
Other countries where they use a period for commas and viceversa

8. Performing the Study Data Collection Form Data Collection Form (CRF)
Entries that involve
judgment should be summarized briefly
on the data collection
form and explain
with more detail on
the study manual
Inclusion Criteria
Exclusion Criteria
Predictor Variables
Outcome Variables
Confounding Variables

9. Performing the Study
Data Collection Form
PREDICTOR VARIABLE

10. Performing the Study Data Collection Form EMPIRIC THERAPY
PREDICTOR VARIABLE
EMPIRIC THERAPY

11. Empiric antibiotic therapy for CAP ■ Patients admitted to ward
Performing the Study
Data Collection Form
PREDICTOR VARIABLE
EMPIRIC THERAPY
Empiric antibiotic therapy for CAP
■ Patients admitted to ward

12. Performing the Study Data Collection Form EMPIRIC THERAPY
PREDICTOR VARIABLE
EMPIRIC THERAPY
Empiric antibiotic therapy for CAP
■ Patients admitted to ward
Beta-lactam plus macrolide Or
Respiratory quinolone

13. Performing the Study Data Collection Form EMPIRIC THERAPY
PREDICTOR VARIABLE
EMPIRIC THERAPY
Empiric antibiotic therapy for CAP
■ Patients admitted to ward
Beta-lactam plus macrolide or Respiratory quinolone
■ Patients admitted to ICU

14. Performing the Study Data Collection Form EMPIRIC THERAPY
PREDICTOR VARIABLE
EMPIRIC THERAPY
Empiric antibiotic therapy for CAP
■ Patients admitted to ward
Beta-lactam plus macrolide or Respiratory quinolone
■ Patients admitted to ICU
Beta-lactam plus macrolide Or
Beta-lactam plus respiratory quinolone

15. Performing the Study Data Collection Form EMPIRIC THERAPY
PREDICTOR VARIABLE
EMPIRIC THERAPY

16. Performing the Study
Data Collection Form

17. Data Collection Form (CRF) Confounding Variables
Performing the Study
Data Collection Form
Data Collection Form (CRF)
Inclusion Criteria
Exclusion Criteria
Predictor Variables
Outcome Variables
Confounding Variables

18. Performing the Study
Data Collection Form
OUTCOME VARIABLE

19. Performing the Study Data Collection Form IN-HOSPITAL OUTCOME VARIABLE
MORTALITY

20. ■ Death of any cause during hospitalization
Performing the Study
Data Collection Form
OUTCOME VARIABLE
IN-HOSPITAL
MORTALITY
■ Death of any cause during hospitalization

21. Data Collection Form (CRF) Confounding Variables
Performing the Study
Data Collection Form
Data Collection Form (CRF)
Inclusion Criteria
Exclusion Criteria
Predictor Variables
Variable associated with the predictor variable and is a cause of the outcome variable
Outcome Variables
Confounding Variables

22. Performing the Study Data Collection Form CONFOUNDING VARIABLE
Variable associated with the predictor variable and is a cause of the outcome variable

23. Performing the Study Data Collection Form CONFOUNDING VARIABLE
Variable associated with the predictor variable and is a cause of the outcome variable
SEVERITY OF DISEASE

24. ■ Pneumonia severity index score
Performing the Study
Data Collection Form
CONFOUNDING VARIABLE
Variable associated with the predictor variable and is a cause of the outcome variable
SEVERITY OF DISEASE
■ Pneumonia severity index score
■ CURB-65 score
■ ICU admission

25. Performing the Study Data Collection Form CONFOUNDING VARIABLE
Variable associated with the predictor variable and is a cause of the outcome variable
COMORBIDITIES

26. Performing the Study Data Collection Form CONFOUNDING VARIABLE
Variable associated with the predictor variable and is a cause of the outcome variable
COMORBIDITIES
■ CHF (defined as systolic or diastolic ventricular dysfunction
documented by history, physical examination, CXR, echocardiogram)
■ Diabetes (defined as a history of diabetes documented in the medical
records that requires treatment with insulin or oral hypoglycemic drugs)
■ Respiratory (defined as a history of COPD documented in the medical
records)

27. Objectives To review important tools for the collection
of clinical data.
Data collection form
Pretesting
Data entry
Data management
Study database

28. Performing the Study Pretesting
A problem-free protocol on paper usually has
important problems in practice

29. Performing the Study Pretesting
A problem-free protocol on paper usually has
important problems in practice
Pretesting can discover these problems
before the initiation of the study

30. Performing the Study Pretesting
A problem-free protocol on paper usually has
important problems in practice
Pretesting can discover these problems
before the initiation of the study
Small pilot studies that take only a few days or weeks
are very useful to guide appropriate changes in the
protocol before the study begins

31. Performing the Study Pretesting
A pilot study to evaluate the methods for recruiting the study subjects

32. Performing the Study Pretesting
A pilot study to evaluate the methods for recruiting the study subjects
A pilot study to evaluate if the desired information has been systematically recorded in the medical record

33. Performing the Study Pretesting
A pilot study to evaluate the methods for recruiting the study subjects
A pilot study to evaluate if the desired information has been systematically recorded in the medical record
A pilot study to develop the best approach to measuring predictor or outcome variables

34. Performing the Study Pretesting
A pilot study to evaluate the methods for recruiting the study subjects
A pilot study to evaluate if the desired information has been systematically recorded in the medical record
A pilot study to develop the best approach to measuring predictor or outcome variables
Pretesting helps the research team to finalize
the study protocol and study manual

35. Objectives To review important tools for the collection
of clinical data.
Data collection form
Pretesting
Data entry
Data management
Study database

36. Performing the Study Data Entry: CAPO Study
Keyboard data entry using various
interfaces in the computer screen
An interface can be constructed to
be identical to the paper case report
form
The operator clicks the box
displayed in the computer screen
and the entry is then automatically
converted by the program to the
correct code
Data Entry
CRF
e-CRF

37. Performing the Study

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43. Performing the Study

44. Objectives To review important tools for the collection
of clinical data.
Data collection form
Pretesting
Data entry
Data management
Study database

45. Performing the Study Data Management 1. Define each variable
2. Set up study database and data dictionary
3. Data editing
4. Back up the dataset regularly
5. Create a dataset for analysis

46. Performing the Study Data Management 1. Define each variable
2. Set up study database and data dictionary
3. Data editing
4. Back up the dataset regularly
5. Create a dataset for analysis

47. Performing the Study Data Management To define the variables:
1. Use the CRF to identify and name each variable
2. Use short, understandable, and consistent names
3. Use abbreviations consistently
4. Use lower case (check software programs!)

48. To define the variables:
Performing the Study
Data Management
To define the variables:

49. Performing the Study
Data Management

50. Performing the Study
Data Management

51. Performing the Study Data Management 1. Define each variable
2. Set up study database and data dictionary
3. Data editing
4. Back up the dataset regularly
5. Create a dataset for analysis

52. To set up the study database:
Performing the Study
Data Management
To set up the study database:
Simple Databases
Complex Databases
Spreadsheet Program
Interface Builder
Excel
Microsoft Access

53. Performing the Study Data Management To set up the data dictionary:
1. Do it before any data is collected!
2. Describe variable location (e.g. tables)
3. Describe variable name
4. Describe variable type and permitted ranges

54. Performing the Study
Data Management

55. The data dictionary establishes the basis for editing
Performing the Study
Data Management
The data dictionary establishes the basis for editing
Detection of potential errors in the data should generate a written or electronic query including the name of the variable and the reason that it may be incorrect
The response to a query may be to accept the variable value, to correct it, or to confirm that it is missing

56. Performing the Study Data Management 1. Define each variable
2. Set up study database and data dictionary
3. Data editing
4. Back up the dataset regularly
5. Create a dataset for analysis

57. Performing the Study Data Management
To identify and correct errors in the data
Data editing includes checking to be sure that:
1. Variables are not missing
2. Variables are within the permitted range
3. Variables are logically consistent with other variables

58. Performing the Study Data Management 1. Define each variable
2. Set up study database and data dictionary
3. Data editing
4. Back up the dataset regularly
5. Create a dataset for analysis

59. Performing the Study Data Management 1. Define each variable
2. Set up study database and data dictionary
3. Data editing
4. Back up the dataset regularly
5. Create a dataset for analysis

60. Performing the Study Data Management To create a dataset for analysis:
Simple Databases
Complex Databases
Excel
Microsoft Access
Data Transfer to Statistical Analysis Software
Statistical Analysis System (SAS)
Statistical Package for the Social Sciences (SPSS)

61. Performing the Study
Data Management

62. Performing the Study
Data Management

63. Performing the Study
Data Management

64. Objectives To review important tools for the collection
of clinical data.
Data collection form
Pretesting
Data entry
Data management
Study database

65. Performing the Study Database: CAPO Study Data Entry Data Editing CRF
e-CRF
e-CRF
Final
Database
Database

66. Performing the Study
Study Database

67. Objectives To review important tools for the collection
of clinical data.
Data collection form
Pretesting
Data entry
Data management
Study database