16: odds ratios [from case- control studies] case-control studies get around several limitations of...
TRANSCRIPT
16: Odds Ratios [from 16: Odds Ratios [from case-control studies]case-control studies]
Case-control studies get Case-control studies get around several limitations of around several limitations of
cohort studiescohort studies
Cohort Studies (Prior Cohort Studies (Prior Chapter)Chapter)
• Use incidences to assess riskUse incidences to assess risk• Exposed cohort Exposed cohort incidence incidence11
• Non-exposed cohort Non-exposed cohort incidence incidence00
• Compare incidences via risk ratio Compare incidences via risk ratio (())
0
1
incidence
incidenceˆ
Hindrances in Cohort Hindrances in Cohort StudiesStudies
• Long induction between exposure & Long induction between exposure & disease may cause delaysdisease may cause delays
• Study of rare diseases require large Study of rare diseases require large sample sizes to sample sizes to accrue sufficient numbersaccrue sufficient numbers
• When studying many people When studying many people information information by necessity can be limited in scope & by necessity can be limited in scope & accuracy accuracy
• Case-control studies were developed to Case-control studies were developed to help overcome some of these limitations help overcome some of these limitations
Levin et al. (1950) Levin et al. (1950) Historically important study (not in Reader)Historically important study (not in Reader)
• Selection criteriaSelection criteria• 236 lung cancer cases -- 156 (66%) 236 lung cancer cases -- 156 (66%)
smokedsmoked• 481 non-cancerous conditions 481 non-cancerous conditions
(“controls”) -- 212 (44%) smoked(“controls”) -- 212 (44%) smoked
• Although incidences of lung cancer Although incidences of lung cancer cannot be determined from data, we cannot be determined from data, we see an association between smoking see an association between smoking and lung cancerand lung cancer
How do we quantify risk How do we quantify risk from case-control data?from case-control data?
• Two article shed light on this questionTwo article shed light on this question• Cornfield, 1951Cornfield, 1951
Cornfield, J. (1951). A method of estimating comparative rates from clinical data. Application to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute, 11, 1269-1275.
• Miettinen, 1976Miettinen, 1976Miettinen, O. (1976). Estimability and estimation in case-referent studies. American Journal of Epidemiology, 103, 226-235.
Cornfield, 1951Cornfield, 1951
• Justified use of Justified use of odds ratioodds ratio as as estimate of relative risk estimate of relative risk
• Recognized potential bias in Recognized potential bias in selection of cases and controlsselection of cases and controls
Miettinen, 1976Miettinen, 1976
• Conceptualized case-control study Conceptualized case-control study as as nested in a populationnested in a population• all population cases studiedall population cases studied • sample of population non-cases sample of population non-cases
studiedstudied
Miettinen (1976) Density Miettinen (1976) Density SamplingSampling
• Imagine 5Imagine 5 people followed over timepeople followed over time• At time At time tt1 1 (shaded)(shaded), D occurs in person 1, D occurs in person 1• You select at random a non-cases at this timeYou select at random a non-cases at this time
Time
5
4
3
2
1
t1 t2
DD
Note: person #2 becomes a case later on but can still serve as a control at t1
How incidence density sampling How incidence density sampling worksworks
The ratio of exposed to non-exposed The ratio of exposed to non-exposed time in the controls estimates the ratio time in the controls estimates the ratio of exposed to non-exposed controls in of exposed to non-exposed controls in
the populationthe population(see EKS for details)(see EKS for details)
Data AnalysisData Analysis• Ascertain exposure status in cases and controlsAscertain exposure status in cases and controls• Cross-tabulate counts to form 2-by-2 tableCross-tabulate counts to form 2-by-2 table• Notation same as prior chapterNotation same as prior chapter
Disease Disease ++
Disease -Disease - TotalTotal
Exposed +Exposed + AA11 BB11 NN11
Exposed -Exposed - AA00 BB00 NN00
TotalTotal MM11 MM00 NN
Calculate Odds Ratio (Calculate Odds Ratio (^))
Disease Disease ++
Disease -Disease - TotalTotal
Exposed +Exposed + AA11 BB11 NN11
Exposed -Exposed - AA00 BB00 NN00
TotalTotal MM11 MM00 NN
01
01
AB
BA
0
11 cases odds, exposureA
Ao
0
10 controls odds, exposureB
Bo
01
01
01
01
0
1
/
/ratio odds
AB
BA
BB
AA
o
o
Cross-product Cross-product ratioratio
Illustrative Example Illustrative Example (Breslow & Day, 1980)(Breslow & Day, 1980)
• Dataset = bd1.sav Dataset = bd1.sav • Exposure variable (Exposure variable (alc2alc2) = Alcohol use dichotomized) = Alcohol use dichotomized• Disease variable (Disease variable (casecase) = Esophageal cancer) = Esophageal cancer
AlcoholAlcohol CaseCase ControlControl TotalTotal
80 g/day80 g/day 96 109 205
< 80 g/day< 80 g/day 104 666 770
TotalTotal 200 775 975
64.5)104)(109(
)666)(96(ˆ
Interpretation of Odds Interpretation of Odds RatioRatio
• Odds ratios are Odds ratios are relative risk relative risk estimatesestimates• Risk multiplierRisk multiplier
• e.g., odds ratio of 5.64 suggests 5.64× risk e.g., odds ratio of 5.64 suggests 5.64× risk with exposure with exposure
• Percent relative risk difference Percent relative risk difference = = (odds ratio – 1) × 100%(odds ratio – 1) × 100%• e.g., odds ratio of 5.64 e.g., odds ratio of 5.64 • Percent relative risk difference = (5.64 – 1) Percent relative risk difference = (5.64 – 1)
× 100% = 464%× 100% = 464%
95% Confidence 95% Confidence IntervalInterval
• CalculationsCalculations• Convert Convert ψψ^ to ln scale^ to ln scale• seselnlnψψ^ = sqrt( = sqrt(AA11
-1-1 + + AA00-1-1 + + BB11
-1-1 + + BB00-1-1))
• 95% CI for ln95% CI for lnψ ψ = (ln = (lnψψ^) ± (1.96)(se)^) ± (1.96)(se)• Exponentiate limitsExponentiate limits
• Illustrative exampleIllustrative example• ln(ln(ψψ^) = ln(5.640) = 1.730^) = ln(5.640) = 1.730• seselnlnψψ^ = sqrt(96 = sqrt(96-1-1 + 104 + 104-1-1 + 109 + 109-1-1 + 666 + 666-1-1) = 0.1752) = 0.1752• 95% CI for ln95% CI for lnψ = ψ = 1.730 ± (1.96)(0.1752) = (1.387, 1.730 ± (1.96)(0.1752) = (1.387,
2.073)2.073)• 95% CI for 95% CI for ψ = eψ = e(1.387, 2.073)(1.387, 2.073) = (4.00, 7.95) = (4.00, 7.95)
SPSS OutputSPSS Output
Ignore “For cohort” lines when data are case-control
Odds ratio point estimate and confidence limits
Interpretation of the Interpretation of the 95% CI95% CI
• Locates odds ratio parameter (Locates odds ratio parameter (ψψ) ) with 95% confidence with 95% confidence
• Illustrative example: 95% Illustrative example: 95% confident odds ratio confident odds ratio parameter parameter is is no less than 4.00 and no more no less than 4.00 and no more than 7.95than 7.95
• Confidence interval width provides Confidence interval width provides information about precisioninformation about precision
Testing Testing HH00: : ψψ = 1 with = 1 with the Confidence Intervalthe Confidence Interval
• 95% CI corresponds to 95% CI corresponds to = .05 = .05• If 95% CI for odds ratio excludes 1 If 95% CI for odds ratio excludes 1 odds odds
ratio is significant ratio is significant • e.g., (95% CI: 4.00, 7.95) is a significant positive e.g., (95% CI: 4.00, 7.95) is a significant positive
associationassociation• e.g., (95% CI: 0.25, 0.65) is a significant negative e.g., (95% CI: 0.25, 0.65) is a significant negative
associationassociation• If 95% CI includes 1 If 95% CI includes 1 odds ratio NOT odds ratio NOT
significantsignificant• e.g., (95% CI: 0.80, 1.15) is not significant (i.e., e.g., (95% CI: 0.80, 1.15) is not significant (i.e.,
cannot rule out odds ratio parameter of 1 with cannot rule out odds ratio parameter of 1 with 95% confidence95% confidence
p p valuevalue
• HH00: : ψψ = 1 (“no association”) = 1 (“no association”)
• Use chi-square test (Pearson’s or Use chi-square test (Pearson’s or Yates’) or Fisher’s test, as covered Yates’) or Fisher’s test, as covered in prior chaptersin prior chapters
i
iiPearson E
EO 22 )(
i
iiYates E
EO 22 )5.0|(|
Fisher’s exact test by computerFisher’s exact test by computer
Chi-Square, PearsonChi-Square, PearsonOBSEOBSERVEDRVED
D+D+ D-D- TotalTotal
E+E+ 96 109 205
E-E- 104 666 770
TotalTotal 200 775 975
EXPECEXPECTEDTED D+D+ D-D- TotalTotal
E+E+42.051
162.949
205
E-E-157.949
612.051
770
TotalTotal 200 775 975
2Pearson's = (96 - 42.051)2 / 42.051 + (109 – 162.949)2 / 162.949 +
(104 - 157.949)2 / 157.949 + (666 – 612.051)2 / 612.051 = 69.213 + 17.861 + 18.427 + 4.755 = 110.256
= sqrt(110.256) = 10.50 off chart (way into tail) p < .0001
Chi-Square, YatesChi-Square, YatesOBSEOBSERVEDRVED
D+D+ D-D- TotalTotal
E+E+ 96 109 205
E-E- 104 666 770
TotalTotal 200 775 975
EXPECEXPECTEDTED D+D+ D-D- TotalTotal
E+E+42.051
162.949
205
E-E-157.949
612.051
770
TotalTotal 200 775 975
2Pearson's = (|96 - 42.051| - ½)2 / 42.051 + (|109 – 162.949| - ½)2 /
162.949 + (|104 - 157.949| - ½)2 / 157.949 + (|666 – 612.051| - ½)2 / 612.051 = 67.935 + 17.532 + 18.087 + 4.668 = 108.221
= sqrt(108.22) = 10.40 p < .0001
SPSS OutputSPSS Output
Pearson = uncorrected Yates = continuity correctedFisher’s unnecessary here
Linear-by-linear not covered
Interpreting the Interpreting the p p valuevalue
• "If the null hypothesis were "If the null hypothesis were correct, the probability of correct, the probability of observing the data is observing the data is pp““
• e.g., e.g., p p = .000 suggests association = .000 suggests association is unlikely due to chance (we can is unlikely due to chance (we can be confident in rejecting be confident in rejecting HH00))
Validity! Validity!
• Before you get too carried away with Before you get too carried away with the odds ratio (or any other statistic), the odds ratio (or any other statistic), remember they assume validityremember they assume validity• No info bias (exposure and disease No info bias (exposure and disease
accurately classified)accurately classified)• No selection bias (cases and controls are No selection bias (cases and controls are
fair reflection of population analogues)fair reflection of population analogues)• No confoundingNo confounding
Matched-PairsMatched-Pairs• Matching can be employed to help Matching can be employed to help
control for confoundingcontrol for confounding• e.g., matching on age and sex e.g., matching on age and sex
• Each Each pairpair represents an observation represents an observation• Classify each Classify each pairpair
• Concordant pairsConcordant pairs• case is exposed & control is exposedcase is exposed & control is exposed• case is non-exposed & control is non-exposedcase is non-exposed & control is non-exposed
• Discordant pairsDiscordant pairs• case is exposed & control is non-exposedcase is exposed & control is non-exposed• case is non-exposed & control is exposedcase is non-exposed & control is exposed
Tabulation & Notation Tabulation & Notation
Control Control
exposedexposedControl Control
non-exposednon-exposedCase Case
exposedexposedt u
Case Case
non-non-exposedexposed
v w
v
u
Tabular display is optionalTabular display is optional
Odds ratio for matched Odds ratio for matched pair data:pair data:
Example (Matched Example (Matched Pairs)Pairs)
Control Control
exposedexposedControl Control
non-exposednon-exposedCase Case
exposedexposed5 30
Case Case
non-non-exposedexposed
10 5
00.310
30ˆ
Confidence Interval for Confidence Interval for Matched PairsMatched Pairs
3651.010
1
30
1ˆ se
)14.6 ,47.1(for CI%95 )8142.1 ,3838.0( e
00.310
30ˆ
)8142.1 ,3838.0(
7156.01.0986
651)(1.96)(0.3)00.3ln(
)(1.96)(ˆlnlnfor CI%95 ˆln
se
McNemar’s Test for Matched McNemar’s Test for Matched PairsPairs
HH00: : ψψ = 1 (“no association”) = 1 (“no association”)
025.91030
)1|1030(|)1|(| 222
vu
vuMcN
00.3025.9 0027.p chi-table chi-table
df = 1 for McNemar’sdf = 1 for McNemar’sOK to convert to chi-statisticOK to convert to chi-statistic