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“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA

“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA. DEPARTMENT OF MEDICAL INFORMATICS 2005 / 2006. “Medical Scientific Research Methodology” “MCS” 2 nd Module Course 2. RISK AND PROGNOSTIC FACTORS ANALYSIS. 1.1. RISK FACTORS. a) DEFINITION :

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“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA

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  1. “Victor Babes”UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA DEPARTMENT OF MEDICAL INFORMATICS 2005 / 2006

  2. “Medical Scientific Research Methodology”“MCS” 2nd Module Course 2

  3. RISK AND PROGNOSTIC FACTORS ANALYSIS

  4. 1.1. RISK FACTORS • a) DEFINITION : • Hypothetical cause for disease occurrence or facilitation • b) CLASSIFICATION : • Environmental factors • Social • Behaviorial • Biological

  5. 1.2. METHODS • A- EXPERIMENTAL • RISK FACTOR CONTROL • DISADVANTAGE: ETHICAL REASONS • B- OBSERVATION-BASED

  6. a) TRANSVERSAL (CROSS – SECTIONAL) • Moment situation in a large sample • b) LONGITUDINAL (Evolution in time) • COHORT • Two groups: Exposed / Unexposed • Prospective • [Cohort retrospective] • CASE - CONTROL • Two groups: Disease / No-disease • Retrospective Comparison: • EXP > COH.pr. > COH.ret. > CASE-C. > CR.S.

  7. 1.3. CONTINGENCY TABLESa) Cross sectional, Cohort and Case-Control independent samples (unpaired)

  8. b) Paired (matched) groups – cohortN11, N12, N21, N22 = pairs (expl)

  9. c) Paired (matched) groups – case-controlN11, N12, N21, N22 = pairs (expl)

  10. 1.4. FONDAMENTAL PARAMETERS IN EPIDEMIOLOGY • ABSOLUTE RISK (‘success’ rate): R (E+) = p(D+/E+) = N11 / L1 R (E-) = p(D+/E-) = N21 / L2 • RELATIVE RISK (RR): • RR = R(E+) / R(E-) • RR = N11 . L2 / N21 . L1

  11. ATTRIBUTABLE RISK : • (EXCESS OF RISK DUE TO EXPOSURE) • AR = P(D+/E+) – P(D+/E-) • POPULATION ATTRIBUTABLE RISK : • (EXCESS OF RISK IN POPULATION) • PAR = AR x P(E+) • ATTRIBUTABLE FRACTION: • (AR %, ETIOLOGICAL FRACTION) • AFE = AR/P(D+/E+) = (RR-1)/RR • POPULATION ATTRIBUTABLE FRACTION: • (PAR %, TOTAL ETIOLOGICAL FRACTION) • AFT = PAR/P(D+)

  12. ‘ODD’ INDEX (‘success / failure’): • - for cohort: • ODD (D+/E+) = P(D+/E+)/P(D-/E+) = N11 / N12 • ODD (D+/E-) = P(D+/E-)/P(D-/E-) = N21 / N22 • - for case-control: • ODD (E+/D+) = P(E+/D+)/P(E-/D+) = N11 / N21 • ODD (E+/D-) = P(E+/D-)/P(E-/D-) = N21 / N22

  13. ODDS RATIO (OR) – independent groups • for cohort OR = ODD(D+/E+) / ODD(D+/E-) • for case-control • OR = ODD(E+/D+) / ODD(E+/D-) • OR = N11 . N22 / N21 . N12

  14. ODDS RATIO (OR) – matched groups • for cohort OR = ODD(D+/E+) / ODD(D+/E-) • for case-control • OR = ODD(E+/D+) / ODD(E+/D-) • OR = N12 / N21 • Usually OR > RR • If OR > 1 (RR > 1) ==> RISK !

  15. Confidence Intervals • Limits for OR: UP = upper limit, LL = lower… • for 95%: ln (UL & LL) = ln (OR) ± 1.96 x • Accept RISK if the whole interval (LL,UL) >1

  16. 2. Data Processing (repetition)

  17. 2. Biostatistics chapters • Introduction: variables, samples • Statistical parameters (of a sample) • Statistical estimation (for a population) • Statistical tests (comparison – differences) • Correlation and regression (association) • Epidemiological studies (risk, association) • Special applications Survival analysis (time series) Sequencial analysis (bioinformatics) Health statistics

  18. 2.1. STATISTICAL INFERENCE • A. GENERAL CONCEPTS • a) population, individual • b) definition: Biostatistics = science of estimating population characteristics and comparing populations • c) methods: • census - all individuals; the same time • screening - large number; selection criteria • sampling - subset of population

  19. d) STATISTICAL INFERENCE • EXTENDING PROPERTIES COMPUTED FOR A SAMPLE TO A POPULATION • e) REPRESENTATIVE SAMPLE • CRITERIA: • EQUIPROPBABILITY • INDEPENDENCE • f) SELECTION METHODS • SIMPLE SELECTION • RANDOM NUMBERS ASSOCIATED • MULTIPLE LAYER SELECTION • MIXED SELECTION • CLUSTERS

  20. B. VARIABLES • a) DEFINITION: • a population characteristic which is studied and measured on all sampled individuals • b) STARTING A STUDY • variable selection • measurement accuracy • sample size • c) TYPES OF VARIABLES: • NUMERICAL • ORDINAL (rank) • NOMINAL (qualitative, count data)

  21. C. STUDY DESIGN • Setting scope (hypothesis, objectives, study plan) • variable selection • measurement accuracy • sample size • choosing the processing method and accepted limits • Representative sample • Data collection • Preliminary data presentaion • tables • graphs (histograms,“pie”, lines, scatter, maps) • DATA PROCESSING - CONCLUSIONS • Writing the protocol and the paper (+ error estimation)

  22. 3. Choosing the study method

  23. ~ Pause ~

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