““Victor Babes”Victor Babes”UNIVERSITY OF UNIVERSITY OF

MEDICINE AND PHARMACY MEDICINE AND PHARMACY TIMISOARATIMISOARA

““Victor Babes”Victor Babes”UNIVERSITY OF UNIVERSITY OF

MEDICINE AND PHARMACY MEDICINE AND PHARMACY TIMISOARATIMISOARA

DEPARTMENT OFDEPARTMENT OF

MEDICAL INFORMATICS MEDICAL INFORMATICS

2005 / 20062005 / 2006

““Medical Scientific Research Methodology”Medical Scientific Research Methodology”“MCS”“MCS”

22ndnd Module Module

Course 2Course 2

1.1. RISK AND RISK AND

PROGNOSTIC FACTORSPROGNOSTIC FACTORS

ANALYSISANALYSIS

1.1. RISK FACTORS1.1. RISK FACTORS

• a) DEFINITION : a) DEFINITION : – Hypothetical cause for disease Hypothetical cause for disease

occurrence or facilitationoccurrence or facilitation– b) CLASSIFICATION :b) CLASSIFICATION :

• Environmental factorsEnvironmental factors• SocialSocial• BehaviorialBehaviorial• BiologicalBiological

1.2. METHODS1.2. METHODS

– A- EXPERIMENTAL A- EXPERIMENTAL • RISK FACTOR CONTROLRISK FACTOR CONTROL

• DISADVANTAGE: ETHICAL DISADVANTAGE: ETHICAL REASONSREASONS

– B- OBSERVATION-BASEDB- OBSERVATION-BASED

• a) TRANSVERSAL (CROSS – SECTIONAL) a) TRANSVERSAL (CROSS – SECTIONAL) – Moment situation in a large sampleMoment situation in a large sample

• b) LONGITUDINAL (Evolution in time)b) LONGITUDINAL (Evolution in time)– COHORT COHORT – Two groups: Exposed / Unexposed Two groups: Exposed / Unexposed – ProspectiveProspective– [Cohort retrospective][Cohort retrospective]– CASE - CONTROLCASE - CONTROL– Two groups: Disease / No-diseaseTwo groups: Disease / No-disease– RetrospectiveRetrospective

Comparison:Comparison:– EXP > COH.pr. > COH.ret. > CASE-C. > CR.S.EXP > COH.pr. > COH.ret. > CASE-C. > CR.S.

1.3. CONTINGENCY TABLES1.3. CONTINGENCY TABLESa) Cross sectional, Cohort and Case-Control a) Cross sectional, Cohort and Case-Control

independent samples (unpaired)independent samples (unpaired)

D+ (disease)

D- (no disease)

Total rows

E+ (exposed) N11 N12 L1

E- (nonexposed) N21 N22 L2

Total columns

C1 C2 N

b) Paired (matched) groups – b) Paired (matched) groups – cohortcohort

N11, N12, N21, N22 = pairs (expl)N11, N12, N21, N22 = pairs (expl)

E- Non-exposed D+ (disease) D- (no disease)

Total rows

D+ N11 N12 L1

E+

Exposed D- N21 N22 L2

Total columns

C1 C2 N

c) Paired (matched) groups – c) Paired (matched) groups – case-controlcase-control

N11, N12, N21, N22 = pairs (expl)N11, N12, N21, N22 = pairs (expl)

D- (no disease) E+ Exposed E- Non-exposed

Total rows

E+ Exposed N11 N12 L1

D+

(disease) E- Non-exposed N21 N22 L2

Total columns

C1 C2 N

1.4. FONDAMENTAL PARAMETERS IN 1.4. FONDAMENTAL PARAMETERS IN EPIDEMIOLOGYEPIDEMIOLOGY

• ABSOLUTE RISK (‘success’ rate):ABSOLUTE RISK (‘success’ rate):

R (E+) = p(D+/E+) = N11 / L1R (E+) = p(D+/E+) = N11 / L1

R (E-) = p(D+/E-) = N21 / L2R (E-) = p(D+/E-) = N21 / L2

• RELATIVE RISK (RR):RELATIVE RISK (RR):

RR = R(E+) / R(E-)RR = R(E+) / R(E-)

RR = N11 . L2 / N21 . L1RR = N11 . L2 / N21 . L1

ATTRIBUTABLE RISK :ATTRIBUTABLE RISK : (EXCESS OF RISK DUE TO EXPOSURE)(EXCESS OF RISK DUE TO EXPOSURE)

AR = P(D+/E+) – P(D+/E-)AR = P(D+/E+) – P(D+/E-)

POPULATION ATTRIBUTABLE RISK :POPULATION ATTRIBUTABLE RISK : (EXCESS OF RISK IN POPULATION)(EXCESS OF RISK IN POPULATION)

PAR = AR PAR = AR xx P(E+) P(E+)

ATTRIBUTABLE FRACTION:ATTRIBUTABLE FRACTION: (AR %, ETIOLOGICAL FRACTION)(AR %, ETIOLOGICAL FRACTION)

AFAFE E = AR/P(D+/E+) = (RR-1)/RR= AR/P(D+/E+) = (RR-1)/RR

POPULATION ATTRIBUTABLE FRACTION:POPULATION ATTRIBUTABLE FRACTION: (PAR %, TOTAL ETIOLOGICAL FRACTION)(PAR %, TOTAL ETIOLOGICAL FRACTION)

AFAFTT = PAR/P(D+) = PAR/P(D+)

• ‘‘ODD’ INDEX (‘success / failure’):ODD’ INDEX (‘success / failure’):

- for cohort:- for cohort: ODD (D+/E+) = P(D+/E+)/P(D-/E+) = N11 / N12ODD (D+/E+) = P(D+/E+)/P(D-/E+) = N11 / N12 ODD (D+/E-) = P(D+/E-)/P(D-/E-) = N21 / N22ODD (D+/E-) = P(D+/E-)/P(D-/E-) = N21 / N22

- for case-control:- for case-control: ODD (E+/D+) = P(E+/D+)/P(E-/D+) = N11 / N21ODD (E+/D+) = P(E+/D+)/P(E-/D+) = N11 / N21 ODD (E+/D-) = P(E+/D-)/P(E-/D-) = N21 / N22ODD (E+/D-) = P(E+/D-)/P(E-/D-) = N21 / N22

• ODDS RATIO (OR) – independent groupsODDS RATIO (OR) – independent groups– for cohortfor cohort

OR = ODD(D+/E+) / ODD(D+/E-)OR = ODD(D+/E+) / ODD(D+/E-)– for case-controlfor case-control

OR = ODD(E+/D+) / ODD(E+/D-)OR = ODD(E+/D+) / ODD(E+/D-)

OR = N11 . N22 / N21 . N12 OR = N11 . N22 / N21 . N12

• ODDS RATIO (OR) – matched groupsODDS RATIO (OR) – matched groups– for cohortfor cohort

OR = ODD(D+/E+) / ODD(D+/E-)OR = ODD(D+/E+) / ODD(D+/E-)– for case-controlfor case-control

OR = ODD(E+/D+) / ODD(E+/D-)OR = ODD(E+/D+) / ODD(E+/D-)

OR = N12 / N21 OR = N12 / N21

• Usually OR > RRUsually OR > RR• If OR > 1 (RR > 1) ==> RISK !If OR > 1 (RR > 1) ==> RISK !

Confidence IntervalsConfidence Intervals

• Limits for OR: UP = upper limit, LL = lower…Limits for OR: UP = upper limit, LL = lower…– for 95%:for 95%:

ln (UL & LL) = ln (OR) ± 1.96 x

• Accept RISK if the whole interval (LL,UL) >1Accept RISK if the whole interval (LL,UL) >1

22

1

21

1

12

1

11

1

NNNN

2. Biostatistics chapters2. Biostatistics chapters• Introduction: variables, samplesIntroduction: variables, samples• Statistical parameters (of a sample)Statistical parameters (of a sample)• Statistical estimation (for a population)Statistical estimation (for a population)• Statistical tests (comparison – differences)Statistical tests (comparison – differences)• Correlation and regression (association)Correlation and regression (association)• Epidemiological studies (risk, association)Epidemiological studies (risk, association)• Special applicationsSpecial applications

Survival analysis (time series)Survival analysis (time series)

Sequencial analysis (bioinformatics)Sequencial analysis (bioinformatics)

Health statisticsHealth statistics

2.1. STATISTICAL INFERENCE2.1. STATISTICAL INFERENCE

• A. GENERAL CONCEPTSA. GENERAL CONCEPTS– a) population, individuala) population, individual– b) definition: Biostatistics = b) definition: Biostatistics = science of science of

estimating population characteristics and estimating population characteristics and comparing populationscomparing populations

– c) methods:c) methods:• census - all individuals; the same timecensus - all individuals; the same time• screening - large number; selection criteriascreening - large number; selection criteria• sampling - subset of populationsampling - subset of population

– d) STATISTICAL INFERENCE d) STATISTICAL INFERENCE • EXTENDING PROPERTIES COMPUTED FOR EXTENDING PROPERTIES COMPUTED FOR

A SAMPLE TO A POPULATIONA SAMPLE TO A POPULATION

– e) REPRESENTATIVE SAMPLE e) REPRESENTATIVE SAMPLE • CRITERIA:CRITERIA:

– EQUIPROPBABILITYEQUIPROPBABILITY– INDEPENDENCEINDEPENDENCE

– f) SELECTION METHODSf) SELECTION METHODS• SIMPLE SELECTIONSIMPLE SELECTION

– RANDOM NUMBERS ASSOCIATEDRANDOM NUMBERS ASSOCIATED

• MULTIPLE LAYER SELECTIONMULTIPLE LAYER SELECTION• MIXED SELECTION MIXED SELECTION

– CLUSTERSCLUSTERS

• B. VARIABLESB. VARIABLES– a) DEFINITION:a) DEFINITION:

• a population characteristic which is studied and a population characteristic which is studied and measured on all sampled individualsmeasured on all sampled individuals

– b) STARTING A STUDYb) STARTING A STUDY• variable selectionvariable selection• measurement accuracymeasurement accuracy• sample sizesample size

– c) TYPES OF VARIABLES:c) TYPES OF VARIABLES:• NUMERICALNUMERICAL• ORDINAL (rank)ORDINAL (rank)• NOMINAL (qualitative, count data)NOMINAL (qualitative, count data)

• C. STUDY DESIGNC. STUDY DESIGN

– Setting scope (hypothesis, objectives, study plan)Setting scope (hypothesis, objectives, study plan)• variable selectionvariable selection• measurement accuracymeasurement accuracy• sample sizesample size• choosing the processing method and accepted limitschoosing the processing method and accepted limits

– Representative sampleRepresentative sample– Data collectionData collection– Preliminary data presentaionPreliminary data presentaion

• tablestables• graphs (graphs (histograms,“pie”, lines, scatter, maps)histograms,“pie”, lines, scatter, maps)

– DATA PROCESSING - CONCLUSIONSDATA PROCESSING - CONCLUSIONS– Writing the protocol and the paper (+ error estimation)Writing the protocol and the paper (+ error estimation)

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