paula peyrani, md director clinical research program infectious diseases division, university of...

Paula Peyrani, MDDirector Clinical Research Program

Infectious Diseases Division, University of Louisville

Louisville, Kentucky, USA

Performing the StudyPerforming the StudyData CollectionData Collection

Objectives

To review important tools for the collection

of clinical data.•Data collection form•Pretesting•Data entry•Data management•Study database

Data Collection Form (CRF)

Data Collection Form Data Collection Form Data Collection Form Data Collection Form

Inclusion Criteria

Performing the StudyPerforming the Study

Exclusion Criteria

Predictor Variables

Outcome Variables

Confounding Variables



Inclusion Criteria


Exclusion Criteria

Predictor Variables

Outcome Variables


Entries that involve judgment should be summarized briefly

on the data collection form and explain

with more detail on the study manual


Entries that involve judgment should be summarized briefly on the data collection form and explain



Criteria for diagnosis of CAP:

1. New pulmonary infiltrate 2. New or increased cough3. Fever or hypothermia 4. Changes in WBC


Entries that involve judgment should be summarized briefly on the data collection form and explain



Criteria for diagnosis of CAP:

1. New pulmonary infiltrate (at time of hospitalization)2. New or increased cough3. Fever >37.8o C (100.0o F) or hypothermia <35.6o C (96.0o F) 4. Changes in WBC (leukocytosis, left shift, or leukopenia)


Outcome Variables


Entries that involve judgment should be summarized briefly

on the data collection form and explain


Inclusion Criteria

Exclusion Criteria

Predictor Variables




PREDICTOR VARIABLE



PREDICTOR VARIABLE EMPIRIC THERAPY



Empiric antibiotic therapy for CAP

■ Patients admitted to ward





■ Patients admitted to ward


Beta-lactam plus macrolideOr

Respiratory quinolone




■ Patients admitted to wardBeta-lactam plus macrolide or Respiratory quinolone

■ Patients admitted to ICU





■ Patients admitted to wardBeta-lactam plus macrolide or Respiratory quinolone

■ Patients admitted to ICU


Beta-lactam plus macrolideOr

Beta-lactam plus respiratory quinolone




Inclusion Criteria

Exclusion Criteria

Predictor Variables

Outcome Variables




OUTCOME VARIABLE



OUTCOME VARIABLE IN-HOSPITAL MORTALITY



■ Death of any cause during hospitalization

OUTCOME VARIABLE IN-HOSPITAL MORTALITY



Variable associated with the predictor

variable and is a cause of the outcome

variable

Inclusion Criteria

Exclusion Criteria

Predictor Variables

Outcome Variables





CONFOUNDING VARIABLE


Variable associated with the predictor variable and is a cause of the outcome

variable



CONFOUNDING VARIABLE Variable associated with the predictor variable and is a cause of the outcome

variable

SEVERITY OF DISEASE


SEVERITY OF DISEASE

■ Pneumonia severity index score

■ CURB-65 score

■ ICU admission



variable


COMORBIDITIES



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)



variable

Objectives



Pretesting Pretesting Pretesting 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



Pretesting can discover these problems before the initiation of the study



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





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

Pretesting helps the research team to finalize the study protocol and study manual


Objectives



Data Entry: CAPO StudyData Entry: CAPO StudyData Entry: CAPO StudyData Entry: CAPO Study

CRFCRFCRFCRF e-CRFe-CRFe-CRFe-CRF

Data Entry

Keyboard data entry using variousinterfaces 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 automaticallyconverted by the program to the

correct code


www.caposite.comwww.caposite.comwww.caposite.comwww.caposite.com


Objectives



3. Data editing

Data Management Data Management Data Management Data Management

4. Back up the dataset regularly

5. Create a dataset for analysis

2. Set up study database and data dictionary

1. Define each variable


2. Use short, understandable, and consistent names


3. Use abbreviations consistently

4. Use lower case (check software programs!)

1. Use the CRF to identify and name each variable

To define the variables: To define the variables: To define the variables: To define the variables:


To define the variables: To define the variables: To define the variables: To define the variables:



3. Data editing







Spreadsheet Program

Simple Databases

Excel

Interface Builder

Microsoft Access

Complex Databases


To set up the study database: To set up the study database: To set up the study database: To set up the study database:



To set up the data dictionary: To set up the data dictionary: To set up the data dictionary: To set up the data dictionary:

2. Describe variable location (e.g. tables)

3. Describe variable name

4. Describe variable type and permitted ranges

1. Do it before any data is collected!


Data ManagementData Management Data ManagementData Management

The response to a query may be to accept the variable value, to correct it, or to confirm that it is missing

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 data dictionary establishes the basis for editing The data dictionary establishes the basis for editing The data dictionary establishes the basis for editing The data dictionary establishes the basis for editing







3. Data editing


1. Variables are not missing


2. Variables are within the permitted range

3. Variables are logically consistent with other variables

Data editing includes checking to be sure that:

To identify and correct errors in the data To identify and correct errors in the data To identify and correct errors in the data To identify and correct errors in the data





3. Data editing




Simple Databases

Excel Microsoft Access

Complex Databases


To create a dataset for analysis: To create a dataset for analysis: To create a dataset for analysis: To create a dataset for analysis:

Statistical Analysis System (SAS)

Data Transfer to Statistical Analysis Software

Statistical Package for the Social Sciences (SPSS)


Objectives



Database: CAPO StudyDatabase: CAPO StudyDatabase: CAPO StudyDatabase: CAPO Study

CRFCRFCRFCRF e-CRFe-CRFe-CRFe-CRF

Data Entry Data Editing

e-CRFe-CRFe-CRFe-CRF

Database Database Database Database

Final Final DatabaseDatabase

Final Final DatabaseDatabase


Study DatabaseStudy DatabaseStudy DatabaseStudy Database


Objectives



paula peyrani, md director clinical research program infectious diseases division, university of...

Documents

collection of clinical

study data collection

study criteria

study manual slide

variables entries

wbc slide

new pulmonary infiltrate

important tools