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8/13/2019 Studi Kasus Kontrol - Prof.dr.Dr. Siti Setiati, SpPD-KGer, M.epid, FINASIM
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CASE-CONTROL STUDY
Siti Setiati
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Major Types ofClinical Epidemiologic Research
Type of ResearchQuestion
Descriptive/Causal Aim
Diagnostic research DescriptivePredict the probability of presence of targetdisease from clinical and non-clinical profile
Prognostic research DescriptivePredict the course of disease from clinical and non-clinical profile
Etiologic research CausalCausally explain occurrence of target disease
from determinant
Intervention research Causal & Descriptive
(1) Causally explain the course of disease asinfluenced by treatment
(2) Predict the course of disease giventreatment (options) and clinical and non-clinical profile
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Etiologic research
The research question:
• Is there a relation between a determinant(risk factor) and a disease-outcome?
Research question for causal relation !
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HILL’S CRITERIA
• Is it clear that the exposure precede the onsetof the outcome ?( Temporality )
• Is there a dose-response gradient? ( BiologicalGradient )
• Is the association consistent from study tostudy ( Consistency )
• Does the association make biological sense?(Plausibility )
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Bradford Hill criteria The Bradford Hill criteria , otherwise known as Hill's criteria for causation , are a groupof minimal conditions necessary to provide adequate evidence of a causalrelationship between an incidence and a consequence, established by the English epidemiologist Sir Austin Bradford Hill (1897 –1991) in 1965.
1. Temporality : The effect has to occur after the cause (and if there is an expected
delay between the cause and expected effect, then the effect must occur afterthat delay).2. Biological gradient : Greater exposure should generally lead to greater incidence of
the effect. In other cases, an inverse proportion is observed: greater exposure leadsto lower incidence
3. Consistency : Consistent findings observed by different persons in different placeswith different samples strengthens the likelihood of an effect.
4. Coherence : Coherence between epidemiological and laboratory findingsincreases the likelihood of an effect.
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Bradford Hill criteria 5. Specificity : Causation is likely if a very specific population at a specific
site and disease with no other likely explanation. The more specific anassociation between a factor and an effect is, the bigger the probabilityof a causal relationship.
6. Strength : A small association does not mean that there is not a causaleffect, though the larger the association, the more likely that it is causal.
7. Plausibility : A plausible mechanism between cause and effect is helpful(but Hill noted that knowledge of the mechanism is limited by current
knowledge).
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Etiologic researchWhat study design?
Design of two observational studies to
distinguish between cause and effect:1. Cohort study2. Case-control study
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Case-control study
• Also called patient-control study
• Definition – Study in which patients with the disease-outcome and a
control group without the disease-outcome are selectedand in which it is determined how many people in bothgroups have been exposed to the determinant
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Case-control study
time
start study
disease +(patients)
disease – (controls)
determinant +
determinant +
determinant -
determinant -
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Creutzfeldt- Jakob’s Disease
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Creutzfeldt- Jakob’s Disease
• Fast, progressive form ofdementia
• In the 90s a new variant ofCreutzfeldt-Jakob wasdiscovered in Europe after anepidemic of mad-cow disease
• Caused by eating beef?
What research question?Why case control?
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Creutzfeldt- Jakob’s Disease
time
start study
patientswith CJD
controls fromhospital
beef +
beef +
beef -
beef -
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Case-control study
determinant-outcome relation
CJD + CJD -
beef +
beef -
a
c
b
d
a/c = oddsbeef+ incases
= a x d / b x cb/d = oddsbeef+ incontrols
Odds Ratio
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Case-control study
How do you find patients?• GP; hospital; cancer registration
How to select a control group?• GP; hospital; general population
Patients and controls have to comefrom the same ‘source’ population .
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Case-control study
How do you assess exposure todeterminant?
• Interview with participant• Interview with proxy• Medical file
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Case Control
Case
Control
+
Population
-+
-
Exposure Outcome Start here
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Case-control studysummary
determinant disease-outcome
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Measures of association:
Case Control approach• Research question: Does smoking increase
the risk of lung cancer ?
• Patient control study – select cases and controls – Estimate the frequency of smoking among cases
and controls
– prior: % smokers among cases > % smokersamong controls
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Measures of association:
Case Control approachDisease
Yes No
Yes a bDeterminant
No c d• RR?• Odds ratio = (a/c) / (b/d) = ad / bc
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OR and RR
• In a randomized trial or Cohort study: – Relative Risk (RR ) = [a/(a+b) / c/(c+d)]
• In a case-control study:
–Odds Ratio (OR) = (a/b) / (c/d) = ad/bc
• Odds ratios (OR) and relative risk (RR) greaterthan 1 indicate that there is an increased risk ofthe adverse outcome associated with theexposure.
• If OR = RR = 1 the adverse outcome is no morelikely to occur with than without exposure to thesuspected agent.
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Relative measures of effect• The relative risk
The relative risk can be calculated as ratio between two incidence proportions (riskratio, see Example 1) or two incidence rates (incidence rate ratio, see Example 2).
- Proportion of patients with CV events in the Ramipril group :651/ 4645=0.14 (14%).
- Proportion of patients with CV events in the placebo group :826/ 4652=0.18 (18%).
The Risk Ratio (RR) is: 0.14/0.18= 0.78
Examp le 1 (Risk Ratio)In the randomized prospective Heart Outcomes Prevention Evaluation (HOPE) study (1)the effect of Ramipril on the risk of cardiovascular (CV) events was investigated by
calculating the ratio between the incidence proportions of CV events in Ramipril treatedand in placebo treated patients.
With CV events Without CV eventsRamipril group (n=4645) 651 3994Placebo group (n=4652) 826 3826
The Heart Outcomes Prevention Evaluation Study Investigators. Effects of anangiotensin-converting-enzyme inhibitor, ramipril, on cardiovascular events inhigh-risk patients. N Engl J Med 2000; 342: 145-153
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Relative measures of effect
• The odds ratio
The odds are a way of representing probability , familiar to gamblers (for example, theodds that a single throw of a die produces a six are 1 to 5). In a case-control study theodds of exposure in cases and controls are calculated as the number of exposed individualsdivided by the number of unexposed individuals in each group. If we know the odds ofexposure in cases and controls we can calculate the odds ratio (OR), i.e. the ratio betweenthe odds of exposure in diseased and in non-diseased individuals.
Knoll GA, Wells PS, Young D, et al. Thrombophilia and the risk forhemodialysis vascular access thrombosis. J Am Soc Nephrol 2005;16:1108-1114.
Examp le 2 ( the odds r at io)Knoll et al. (3) investigated the association between vascular access thrombosis andthrombophilia. They considered 107 patients with access thrombosis (cases) and 312 patientswithout fistula thrombosis (controls). Overall, among the 107 patients with access thrombosis, 59had evidence of thrombophilia and 48 did not while among the 312 without access thrombosis 122had thrombophilia and 190 did not.
- Odds of thrombophilia in patients with vascular access thrombosis :59/48=1.229- Odds of thrombophilia in patients without vascular access thrombosis :122/190=0.642
The odds ratio (OR) is: 1.229/0.642= 1.91
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Measures of association:
Case Control approach• Smoking and lung cancer
(controls = 10% random sampling from cohort)Disease
Yes NoYes 440 300 740
DeterminantNo 212 350 562
• Odds ratio (440/212) / (300/350) = 2.42• RR = (440/740) / (212/562) = 1.57 (shouldn ’ t be
calculated)
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SOME IMPORTANT DISCOVERIES MADEIN CASE CONTROL STUDIES
1950's• Cigarette smoking and lung cancer
1970's• Diethyl stilbestrol and vaginal
adenocarcinoma• Post-menopausal estrogens and
endometrial cancer
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1980's• Aspirin and Reyes syndrome• Tampon use and toxic shock syndrome• L-tryptophan and eosinophilia-myalgia
syndrome• AIDS and sexual practices
1990's
• Vaccine effectiveness• Diet and cancer
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TWO CHARACTERISTICS OF CASES
1. REPRESENTATIVENESS:Ideally, cases are a random sample of allcases of interest in the source population(e.g. from vital data, registry data). Morecommonly they are a selection of available
cases from a medical care facility.(e.g. from hospitals, clinics)
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2. METHOD OF SELECTION
Selection may be from incident or prevalentcases:
• Incident cases are those derived fromongoing ascertainment of cases over time.
• Prevalent cases are derived from a cross-sectional survey.
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CHARACTERISTICS OF CONTROLS
• Who is the best control?• Where should controls come from?• If cases are a random sample of all cases in the
population, then controls should be a random sampleof all non-cases in the population sampled at thesame time (i.e. from the same study base)
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THREE QUALITIES NEEDED IN
CONTROLS• Key concept: Comparability is more important
than representativeness in the selection of
controls • The control must be at risk of getting the
disease.
• The control should resemble the case in allrespects except for the presence of disease
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STUDY BASE
Imagining the study base is a usefulexercise before deciding on control
selection.The study base is composed of apopulation at risk of exposure over a
period of risk of exposure .
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Cases emerge within a study base.Controls should emerge from thesame study base, except that theyare not cases.
For example, if cases are selectedexclusively from hospitalized
patients, controls must also beselected from hospitalized patients.
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It follows from the above that a poolof potential controls must be defined.
This pool must mirror the study base of the cases.
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• If cases must have gone through a certain ascertainment process (e.g. screening), controls must have also. (e.g. mammogram-detected breast cancer)
• If cases must have reached a certain age before they can becomecases, so must controls. (thus we always match on age)
• If the exposure of interest is cumulative over time, the controlsand cases must each have the same opportunity to be exposedto that exposure. (if the case has to work in a factory to beexposed to benzene, the control must also have worked wherehe/she could be exposed to benzene)
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Case-control study
How do you find cases/patients?
How to selecet a control group?
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Case-control study
How do you find patients?• GP; hospital; cancer registration
How to select a control group?• GP; hospital; general population
Patients and controls have to comefrom the same‘
source’
population.
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Case-control study
How do you assess exposure anddeterminant?
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Case-control study
How do you assess exposure anddeterminant?
• Interview with participant• Interview with proxy• Medical file
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Case-control studysummary
determinant disease-outcome
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Case Control Start
here
Case
Control
+
Population
-+
-
Exposure Outcome
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Prospective Cohort Start here
**
*
+
-
+-
t o t 1
Free ofoutcome
Exposure Outcome
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Historical Cohort Start here
**
*
+
-
+-
t o t 1
Free ofoutcome
Exposure Outcome
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Study Design
Direction of inquiry
CohortCase-control
Historical cohort
Survey / Cross Sectional
TODAY
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Measures of association:Case Control approach
• Research question: Does smoking increasethe risk of lung cancer ?
• Patient control study – select cases and controls – Estimate the frequency of smoking among cases
and controls
– prior: % smokers among cases > % smokersamong controls
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Measures of association:Case Control approach
DiseaseYes No
Yes a bDeterminantNo c d
• RR?• Odds ratio = (a/c) / (b/d) = ad / bc
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Measures of association:Case Control approach
• Smoking and lung cancer(controls = 10% random sampling from cohort)
DiseaseYes No
Yes 440 300 740Determinant
No 212 350 562
• Odds ratio (440/212) / (300/350) = 2.42• RR = (440/740) / (212/562) = 1.57 (shouldn ’ t becalculated)
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Point Estimates : Odds Ratios
• Example: is gender associated with use of standard adjuvanttherapy (SAT) for patients with newly diagnosed stage III colonor stage II/III rectal cancer? – 53% of men received SAT*
– 62% of women received SAT*• Ages, Sex and Racial Differences in the Use of Standard
Adjuvant Therapy for Colorectal Cancer”,
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Odds and Odds Ratios• Odds = p/(1-p)
• The odds of a man receiveng SAT is 0.53/(1-0.53) = 1.13
• The odds of women receiving SAT is 0.62/(1-0.62) = 1,63
• Odds Ratio = 1.63/1.13 = 1.44
• Interpretation : “a woman is 1.44 times morelikely to receive SAT than a man”
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Odds Ratio• Odds Ratio for comparing two proportions
OR = p1 / (1-p1) p2 / (1-p2)
= p1(1-p2)
p2(1-p1)
OR > 1 : increased risk of group 1 compared to 2OR = 1 : no difference in risk of group 1 compared to 2OR < 1 : lower risk (“protective”) in risk of group 1 compared to 2
• In our example,
– P1 = proportion of women receiving SAT
– P2 = proportion of men receiving SAT
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Odds Ratio from a 2x2 table
SAT No SATWomen a= 298 b= 252 550
Men a= 202 d= 248 450500 500 1000
OR = p1 / (1 - p1) ad p2 / (1 - p2) bc
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Why do we so often see OR and not other?
1) Logistic regression
– Allows us to look at association between two variables,adjusted for other variables
– “Output” is a log odds ratio – Ecamples: in the gender ~ SAT example, the odds ratios
were evaluated using logistic regression. In reality, thegender ~ SAT odds ratio is adjusted for age, race, year of
dx, region, marital status,..2) Can be more globally applied. Design of study does not
restrict usage
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Case-control study
Advantages and disadvantages
• What are the advantages of a case-control
study?
• What are the disadvantages of a case-controlstudy?
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ADVANTAGES AND DISADVANTAGES OF
CASE CONTROL STUDIES Advantages:
1. only realistic study design foruncovering etiology in rare diseases2. important in understanding new diseases3. commonly used in outbreak
investigation4. useful if induction period is long5. relatively inexpensive
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Disadvantages:
1. Susceptible to bias if not carefully designed (and matched)2. Especially susceptible to exposuremisclassification3. Especially susceptible to recall bias 4. Restricted to single outcome 5. Incidence rates not usually calculable
6. Cannot assess effects of matching variables
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Validity and bias • Validity:
– absence of systematic errors in design, conduct ordata-analysis of the research
• Bias: – degree of disruption of the determinant – outcomerelation caused by systematic errors – leads toreduced validity
• 3 types of bias in etiologic research: – selection bias, information bias, confounding
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Any trend in the collection, analysis, interpretation,publication or review of data that can lead to
conclusions that are systematically different fromthe truth (Last, 2001)
A process at any state of inference tending toproduce results that depart systematically fromthe true values (Fletcher et al, 1988)
Systematic error in design or conduct of a study(Szklo et al, 2000)
What is Bias?
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Selection bias
definition• Distortion of the determinant-outcome relation
caused by systematic errors in the selection of
study participants (cases and/or controls)• Determinant-outcome relation is different for
those that do and do not participate
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Information bias
• Distortion of the determinant-outcome relationcaused by systematic errors in the measurement ofthe determinant and/or outcome .
• Who knows an example?
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Information / Measurement / Misclassification Bias
Sources of information bias:
Subject variation
Observer variation
Deficiency of tools
Technical errors in measurement
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Information bias
• Misclassification of determinant – Self reporting more accurate for cases than
controls (or the other way around)
• Misclassification of outcome – Disease better diagnosed in people with
determinant
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Information / Measurement / Misclassification
BiasReporting bias:Individuals with severe disease tends to have completerecords, therefore more complete information aboutexposures and greater association found
Individuals who are aware of being participants of astudy behave differently (Hawthorne effect)
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How to prevent bias?
• Confounding – cannot be prevented – Measure and adjust in data analysis
• Information bias - prevent during design
– Disease status blind for determinant status – Medical files instead of self-reporting – Same way of reporting for cases and controls
• Selection bias - prevent during design – Control selection independent of determinant
status – Good definition of source population
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Controlling for Information Bias
- Blindingprevents investigators and interviewers from knowing case/control orexposed/non-exposed status of a given participant
- Form of surveymail may impose less “white coat tension ” than a phone or face-to-face
interview
- Questionnaireuse multiple questions that ask same informationacts as a built in double-check
- Accuracymultiple checks in medical recordsgathering diagnosis data from multiple source s
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Confounding
Randomisation, matching and restriction can be tried at the time ofdesigning a study to reduce the risk of confounding.
At the time of analysis:
Stratification and multivariable (adjusted) analysis can achieve the same.
It is preferable to try something at the time of designing the study.
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Case-control study
• Advantages – Efficient and relatively cheap – Appropriate for rare outcome – Can study several determinants
• Disadvantages – Cause is measured after effect
– Very sensitive to selection- and infobias – Not appropriate to study several outcomes
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ISSUES IN MATCHING CONTROLS IN CASE-CONTROL STUDIES
1. Identify the pool from which controls may come.This pool is likely to reflect the way controls were
ascertained (hospital, screening test, telephonesurvey).
2. Control selection is usually through matching.Matching variables (e.g. age), and matching criteria (e.g. control must be within the same 5 year agegroup) must be set up in advance.
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3. Controls can be individually matched orfrequency matched
INDIVIDUAL MATCHING : search for one (or more)controls who have the required MATCHING CRITERIA.PAIRED or TRIPLET MATCHING is when there is one or
two controls individually matched to each case.
FREQUENCY MATCHING : select a population of controlssuch that the overall characteristics of the group matchthe overall characteristics of the cases. e.g. if 15% ofcases are under age 20, 15% of the controls are also.
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4. AVOID OVER-MATCHING . match only on factors
known to be causes of the disease.
5. Obtain POWER by matching MORE THAN ONECONTROL PER CASE. In general, N of controls should
be < 4, because there is no further gain of power abovefour controls per case.
6. Obtain GENERALIZABILITY by matching more than
ONE TYPE OF CONTROL
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Validity
Absence of:• Systematic errors• Bias (distortion)
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4. AVOID OVER-MATCHING . match only on factors
known to be causes of the disease.
5. Obtain POWER by matching MORE THAN ONECONTROL PER CASE. In general, N of controls should
be < 4, because there is no further gain of power abovefour controls per case.
6. Obtain GENERALIZABILITY by matching more than
ONE TYPE OF CONTROL
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Precision (or accuracy)
The absence of• Random error
Depends on• Standardisation of measurements• Numbers
– Number of persons – Number of (repeated) observations /
measurements
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Selection bias
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Selection biasexample 1
• Medical circuit: 'oral anticonception could lead toDVT ’
• Women with DVT complaints who use oralanticonception will be more often referred than thosethat do not use oral anticonception
• Because of this selective r eferral all oralanticonception users will have a higher probability tocome into the study as a case and the effect of oralanticonception on DVT will be overestimated
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Point Estimates : Odds Ratios
• Ages, Sex and Racial Differences in the Use of StandardAdjuvant Therapy for Colorectal Cancer”, Potosku, Harlan,Kaplan, Johnson, Lynch, JCO, vol. 20 (5), March 2002, p.1192
• Example: is gender associated with use of standard adjuvant
therapy (SAT) for patients with newly diagnosed stage III colonor stage II/III rectal cancer? – 53% of men received SAT*
– 62% of women received SAT*
• Ages, Sex and Racial Differences in the Use of StandardAdjuvant Therapy for Colorectal Cancer”, Potosku, Harlan,Kaplan, Johnson, Lynch, JCO, vol. 20 (5), March 2002, p.1192
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More on the Odds Ratio
• Ranges from 0 to infinity
• Tends to be skewed (1.e not symmetric)
– “protective” odds ratios range from 0 to 1 – “increased risk” odds ratios range from 1 to
• Example:
• “Women are at 1.44 times the risk/ chance of men” • “Men are at 0.69 times the risk/chance of women”
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More on the Odds Ratio
• Sometimes, we see the log odds ratio insteadof the odds ratio
• Example:• “Women are at 1.44 times the risk/ chance of men”
• “Men are at 0.69 times the risk/chance of women”
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Related Measures of Risk• Relative Risk : RR = p1/p2
– RR = 0.62/0.53 = 1.17
– Different way of describing a similar idea of risk
– Generarlly, intepretation “in words” is the similar” “Women are at 1.17 times as likely as men to
receive SAT” – RR is appropriate in trials often
– But RR is not appropriate in many settings (e.g. case-controls studies)
– Need to be clear about RR versus OR:
• p1
= 0.50, p2 = 0.25• RR = 0.5/0.25 = 2
• OR = (0.5/0.5)/ (0.25/0.75) = 3
• Same results, but OR and RR give quite different magnitude
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Why do we so often see OR and not other?
1) Logistic regression
– Allows us to look at association between two variables,adjusted for other variables
– “Output” is a log odds ratio – Ecamples: in the gender ~ SAT example, the odds ratios
were evaluated using logistic regression. In reality, thegender ~ SAT odds ratio is adjusted for age, race, year of
dx, region, marital status,..2) Can be more globally applied. Design of study does not
restrict usage
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Point Estimates : Hazard RatiosRandomized Controlled Trial of Single – Agent Paclitaxel Versus Cyclophosphamide,Doxorubicin and Cisplatin in Patients with Recurrent Ovarian Cancer Who Responded ti First-line Platinum-Based Regimens”, Cantu, Parma, Rossi, Floriani, Bonazzi, Dell’Anna . Torri,Colombo, JCO, vol. 20(5) March 2002, p.1232
• What is the effect of CAP on overall
survival as compared to paclitaxel?” • Median survival in CAP group was 34.7months
• Median survival in paclitaxel group was25.8 months
• But, median survival doesn’t tell thewhole story…
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Hazard Ratio
• Compares risk of event intwo populations or samples
• Ratio of risk in group 1 torisk in group 2
• First things first• Kaplan – Meler Curves
(product-limit estimate)• Makes a “picture” of
survival
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Hazard Ratio
• Assumption : “Proportional hazards” • The risk doest not depend on time
• That is, “risk is constant over time”
• But that is still vague….
• Example: Assume hazard ratio is 0.7 – Patients in temsirolimus group are at 0.7 times the
risk of death as those in the interferin-alpha arm,at any given point in time
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Survival (S(t)) vs Hazard (h(t))
• Not the same thing• Hard to “envision” the difference
• Technically, it is the negative of the slope of the log of survivalcurve
• Yikes… don’t worry though
• The statisticians deal with this stuff
• Just remember: – The hazard ratio is not the ratio of the survival curves
– It is a ratio of some function of the survival curves
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Hazard Ratios
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More on the Odds Ratio
• Ranges from 0 to infinity
• Tends to be skewed (1.e not symmetric)
– “protective” odds ratios range from 0 to 1 – “increased risk” odds ratios range from 1 to
• Example:
• “Women are at 1.44 times the risk/ chance of men” • “Men are at 0.69 times the risk/chance of women”
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Hazard Ratio
• Compares risk of event intwo populations or samples
• Ratio of risk in group 1 torisk in group 2
• First things first• Kaplan – Meler Curves
(product-limit estimate)• Makes a “picture” of
survival
H d R i
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Hazard Ratio
• Assumption : “Proportional hazards” • The risk doest not depend on time
• That is, “risk is constant over time”
• But that is still vague….
• Example: Assume hazard ratio is 0.7 – Patients in temsirolimus group are at 0.7 times the
risk of death as those in the interferin-alpha arm,at any given point in time
S i l (S( )) H d (h( ))
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Survival (S(t)) vs Hazard (h(t))
• Not the same thing• Hard to “envision” the difference
• Technically, it is the negative of the slope of the log of survivalcurve
• Yikes… don’t worry though
• The statisticians deal with this stuff
•Just remember:
– The hazard ratio is not the ratio of the survival curves
– It is a ratio of some function of the survival curves