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DATA COLLECTION. Dr. Vladimir Ba cârea. MEDICAL DATA COLLECTION. Defining medical data Means of medical data collection Methods of medical data collection Research instruments Data analysis plan. VARIABLES. Medical data = variables - PowerPoint PPT Presentation
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• Defining medical data
• Means of medical data collection
•Methods of medical data collection
•Research instruments
• Data analysis plan
• Medical data = variables
• A variable = function (it can take different values
for each sample or target population element)
•Establishing variable type
• Variables are classified in two groups:
Quantitative variables (which can be measured)
Qualitative variables (which can’t be measured)
Qualitative variables are:
•Nominal variables = groups of elements which can’t be
organized (hair color)
•Ordered nominal variables = the conclusions can be grouped
e.g.: the treatment efficiency: very good/good/bad
• Binary variables = there are only two possibilities: ill/healthy,
YES/NO
VARIABLES TYPES
Quantitative variables are:
Continuous variables = measurable variables which can take
an infinite number of values, usually placed in an interval
e.g.: values of cholesterol, values of blood pressure
Discontinuous variables = variables which can only take
integer values
e.g.: APGAR score
VARIABLES TYPESSurvival variables
It corresponds to the time passed between a subject
inclusion in a study and a predefine element turn up
(death, metastasis, complication)
MEAN OF DATA COLLECTION
Regarding studied elements:
Exhaustive collection. All population subjects that we desire
to study. Hard to accomplish due to high costs or study
population alteration.
By sampling. Is the method used in medical studies.
MEAN OF DATA COLLECTION
Regarding the length of collection:
Transverse. A group is studied in a precise moment in time.
Longitudinal (extended in time):
Retrospective – based on medical registers
Prospective – data collected on pre-established time intervals.
METHODS OF MEDICAL DATA COLLECTION
The interview Individual interview Group interview
It involves similar methodological steps with the observation
METHODS OF MEDICAL DATA COLLECTION
The questionnaire Introduction Body Questions statement Questionnaire graphics
METHODS OF MEDICAL DATA COLLECTION
Existing records Hospital observation papers Consultation records Laboratory records Operating room records
Research instruments
Choosing the research instrument depends on:Study objectiveThe researched diseaseThe population to study
Research instruments
Library study The computer Experimental determinations Statistics Human mind Language and communication facilities
Research instruments
Library studyLibrary catalogsIndexes and abstractsLibrarian referencesThe search through library book shelves
Research instruments
The computerThe Internet and World Wide Web (WWW)Medical data search and selection enginesElectronic mail (e-mail)
Research instruments
Experimental determinationsQualitative and quantitative phenomenon
quatificationVariables standardization (nominals ordinals, etc.)Method validationMethod reproducibility
Research instruments
Statisticso Descriptive statisticso Inferential statistics
Statistical tests – statistical significance
Data analysis plan
Defining the purposeDefining the objectivesDefining the working hypothesesSamplingEnsuring data qualityTesting the hypotheses
Data analysis plan
Defining the purpose:To describe a health issue (to evaluate the
pulmonary tuberculosis in Mures county)To evaluate a diagnostic procedure (to
establish the quality of ultrasonography in diagnosing gallstones)
Data analysis plan
Defining the purpose:To evaluate a therapeutic approach (to
demonstrate the efficiency of laparoscopic cholecystectomy for gallstones)
Risk and/or prognosis factor research (to demonstrate the role of heptavalent chromium in the etiology of chronic obstructive pulmonary disease)
Data analysis plan
Defining the study objectives:To describe a health issue
Main objective (to calculate the prevalence of pulmonary TB in target population)
Secondary objectives (setting the target population, choosing the diagnose method, etc.)
Data analysis plan
Defining the study objectives:To evaluate a diagnostic procedure
Main objective (to calculate the performance parameters of ultrasonography, sensibility, specificity)
Secondary objectives (setting the target population, defining the “golden standard”, etc.)
Data analysis plan
Defining the study objectives:To evaluate a therapeutic approach
Main objective (to compare the efficiency of laparoscopic cholecystectomy to the classical one)
Secondary objectives (setting the target population, setting the comparison criteria, etc.)
Data analysis plan
Defining the study objectives:The research of risk and/or prognosis factors
Main objective (to calculate the role of chromium in the etiology of pulmonary disease)
Secondary objectives (setting the target population, ensuring the compatibility between the study groups, etc.)
Data analysis plan
Defining the working hypotheses:To describe a health issue
The prevalence of pulmonary TB in Mures county is a public health problem
To evaluate a diagnostic procedureUltrasonography in gallstones diagnose is
more sensitive and more specific than the clinical criterias.
Data analysis plan
Defining the working hypotheses:To evaluate a therapeutic approach
Laparoscopic cholecystectomy is easier supported by the patient than the classical one
The research of risk and/or prognosis factorsChromium is a risk factor for pulmonary
disease
Data analysis plan
SamplingTo describe a health issue
Representative sample (qualitative and quantitative, transverse)
To evaluate a diagnostic procedureUnrepresentative sample (qualitative and
quantitative, transverse)
Data analysis plan
Sampling:To evaluate a therapeutic approach
Case-control data collection (retrospective, longitudinal)
The research of risk and/or prognosis factorsCase-control data collection (retrospective,
longitudinal)Exposed-unexposed data collection
(prospective, longitudinal)
Data analysis plan
Ensuring data quality:Initial training of the data collectors
(investigators)Periodic verification of the data collection
methodsParallel data collection (if the data collection
instrument allows it)Investigators retraining
Data analysis plan
Ensuring data quality:Database developmentOperator trainingData input into two parallel databases for
comparison reasonsThe development of validation programs for
incorrect, extreme or missing values (aberrant, outliers, missing data)
Data analysis plan
Hypotheses testingSetting the data to compareSetting the variable type which express medical
data to compareThe correct choosing of statistical testsThe elaboration of “dummy tables” for each
hypothesis to test
Data type
Purpose Gaussian distribution Non-Gaussian distribution Binomial
Single group description Mean, standard deviation Median Proportion
Comparing a single group to a hypothetical value
One sample Student test Wilcoxon test Chi – square
Comparing two unpaired samples
Student test for unpaired data
Mann – Whitney test Fisher test (chi square for large samples)
Comparing two paired samples
Student test for paired data Wilcoxon test McNemar test
Comparing two or more samples
ANOVA Kruskal Wallis test Cox regression
Choosing statistical tests