Download ppt - DATA COLLECTION

Dr. Vladimir Bacârea

• 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


The questionnaire Introduction Body Questions statement Questionnaire graphics


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


Library study The computer Experimental determinations Statistics Human mind Language and communication facilities


Library studyLibrary catalogsIndexes and abstractsLibrarian referencesThe search through library book shelves


The computerThe Internet and World Wide Web (WWW)Medical data search and selection enginesElectronic mail (e-mail)


Experimental determinationsQualitative and quantitative phenomenon

quatificationVariables standardization (nominals ordinals, etc.)Method validationMethod reproducibility


Statisticso Descriptive statisticso Inferential statistics

Statistical tests – statistical significance


Human mind

oStatistically significant

vs.oScientific significant


Language and communicationo Stating facts

• Oral• In writing

o Verbal nuance

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