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“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA

“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA. DEPARTMENT OF MEDICAL INFORMATICS AND BIOPHYSICS Medical Informatics Division www.medinfo.umft.ro/dim 2007 / 2008. CORRELATION ANALYSIS EPIDEMIOLOGY. COURSE 6. 1. RELATIONS BETWEEN TWO QUANTITATIVE VARIABLES .

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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 AND BIOPHYSICS Medical Informatics Division www.medinfo.umft.ro/dim 2007 / 2008

  2. CORRELATION ANALYSISEPIDEMIOLOGY COURSE 6

  3. 1. RELATIONS BETWEEN TWO QUANTITATIVE VARIABLES . • 1.1. DEPENDENCY DEGREE . • STATE SPACE (DIAGRAM) • 1 INDIVIDUAL = 1 POINT

  4. a) INDEPENDENT VARIABLESHG = hemoglobin concentration h = height

  5. b) DEPENDENT VARIABLESCausal relation - mathematical model [O2] in the blood - atmospheric pO2

  6. c) CORRELATED VARIABLES G = weight, h = height

  7. 1.2. LINEAR CORRELATION • a) CORRELATION COEFFICIENT (Pearson) • rxy = sxy / sx sy • sxy = covariance • sx = variance of x • sy = variance of y

  8. b) PROPERTIES: • VALUES = [ -1, +1] • r > 0 ==> DIRECT CORRELATION • r < 0 ==> INVERSE CORRELATION • WEAK / STRONG CORRELATION • WEAK = CLOSE TO 0 • STRONG = CLOSE TO -1 OR +1 • TESTING r : WITH t TEST - significance

  9. Direct and inverse correlations

  10. 1.3. REGRESSION LINE(“best line” among the points) • a) EXAMPLE: • HEIGHT: CHILDREN - PARENTS • hc > hp • SLOPE < 1 ==> REGRESSION • TENDENCY TOWARDS MIDDLE REGION • b) LINE PARAMETERS: y = a + b x • a = INTERCEPT • b = SLOPE

  11. 1.5. NONLINEAR CORRELATIONS • a) EXPONENTIAL y = a . e b.x • Increasing (b > 0): ABSORBTION • Decreasing (b < 0): CLEARANCE

  12. b) LOGARITHMIC: y = a + b . log x • WEBER - FECHNER LAW (Sensation) • c) POWER: y = a . x b • STEVANS LAW (Neural frequency)

  13. d) HYPERBOLIC: (x - a) . (y - b) = k • HILL LAW (Muscular contraction), ABBEY • e) LOGISTIC: y = a . x / (k + x) • MICHAELIS - MENTEN (Enzymatic kinetics) • ARIENS (Dose - response curves)

  14. 2. CORRELATIONS FOR ORDINAL VARIABLES • 2.1. RANK CORRELATION COEFFICIENT • SPEARMAN “R” • Comparing two classifications • 2.2. KENDALL CORRELATION COEFFICIENT • Appl. for ordinal and nominal variables

  15. 2. EPIDEMIOLOGICAL BIOSTATISTICS

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

  17. 1.2. DATA TABLES (Contingency tables)

  18. 1.3. METHODS

  19. 1.3. METHODS • A- EXPERIMENTAL • RISK FACTOR CONTROL • DISADVANTAGE: ETHICAL REASONS • B- OBSERVATION-BASED

  20. a) CROSS - SECTIONAL • TRANSVERSAL: Moment situation in a large sample • b) COHORT - PROSPECTIVE • LONGITUDINAL • Two groups: Exposed / Unexposed • c) COHORT - RETROSPECTIVE • d) CASE - CONTROL • Two groups: Disease / No-disease • e) Comparison: • EXP > COH.pr. > COH.ret. > CASE-C. > CR.S.

  21. 1.4. FONDAMENTAL PARAMETERS IN EPIDEMIOLOGY • ‘ODD’ INDEX (success / fail): • ODD (E+) = N11 / N12 • ODD (E-) = N21 / N22 • ODDS RATIO (OR): • OR = ODD(E+) / ODD(E-) • OR = N11 . N22 / N12 . N21

  22. ‘ABSOLUTE’ RISK (success rate): • R (E+) = N11 / L1 • R (E-) = N21 / L2 • RELATIVE RISK (RR): • RR = R(E+) / R(E-) • RR = N11 . L2 / N21 . L1 • Usually OR > RR • IF OR > 1 (RR > 1) ==> RISK !

  23. 3. SURVIVALANALYSIS

  24. 1. CHARACTERISTICS • missing data, long duration of study • heterogenous conditions • several influencing factors • 2. DATA PROCESSING • life tables • actuarial method • Kaplan Mayer curves

  25. Data collection

  26. Actuarial method

  27. Kaplan Mayer plots

  28. 3.3. INDICATORS • Life Years (Survival years) • QoL Index = Quality of Life • Adjusting “Life Years” to QALY (Quality Adjusted Life Years)

  29. - e n d -

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