measuring covariate data in subsets of study populations: design options
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Measuring covariate data in subsets of study populations: Design options. Jean-François Boivin, MD, ScD McGill University 19 August 2007. 16 th International Conference on Pharmacoepidemiology Barcelona 2000. What about missing covariate data?. Option #1. Do not research that topic. - PowerPoint PPT PresentationTRANSCRIPT
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Measuring covariate data in subsets of study populations: Design optionsJean-Franois Boivin, MD, ScDMcGill University19 August 2007
Measuring covariate data_Presentation (November 14, 2007)
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16th International Conference on Pharmacoepidemiology Barcelona 2000
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What about missing covariate data?
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Do not research that topicOption #1
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Conduct study without covariatesScientifically reasonable for certain questionsExample: Sharpe et al. 2000Option #2
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British Journal of Cancer 2002The effects of tricyclic antidepressants on breast cancer riskGenotoxicity in Drosophila
Comparison of antidepressants:6 genotoxic vs 4 nongenotoxic Confounding unlikely
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Option #3Confounding by other determinants was studied in analyses with data obtained by interviewing samples of subjects
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List 4 - 6 different sampling strategies:Confounding by other determinants was studied in analyses with data obtained by interviewing samples of subjectsa) ?b) ?c) ?d) ?
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Two-stage sampling
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Entire population (=truth)OR=0.5OR=0.5OR=2.5ObeseNot obeseAllE+E-D+D+D+D-D-D-12,00014010,20010,40022,20010,54032,740
2,0004010,000100
20040010,00010,000
2,20044020,00010,100
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ObeseNot obeseAllE+E-D+D+D-D-22,20010,540not availablecomputerized databasesD+D-
2,20044020,00010,100
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Two-stage sampling
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ObeseNot obeseAllE+E-D+D-D+D-D+D-Two-stage samplingOR1 biasedOR2 biased250 x 250 250 x 250= 1
250/250/250/250/
2,200 440 20,000 10,100 32,740
227231252
23227125248
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White. AJE 1982Walker. Biometrics 1982Cain, Breslow. AJE 1988Weinberg, Wacholder. Biometrics 1990Weinberg, Sandler. AJE 1991Statistical analysis; further design issues
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Option 1:Option 2:Option 3:Option 4: No study No covariate measurement 2-stage sampling Case only measurement
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Ray et al.Archives of Internal Medicine 1991
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Cyclic antidepressants and the risk of hip fracture
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E+E-AllD+D-D+D-D+D-AllNot obeseObeseConfounding: Quick review
RR=0.5
RR=0.5
RR=
RR=0.5
N1=?N2=?
RR=0.5
N3=?N4=?
RR=
RR=0.5
N1=1,000 N2=1,000
RR=0.5
N3=1,000N4=1,000
RR=0.5
RR=0.5
N1=1,000 N2=1,000cross-product ratio =1
RR=0.5
N3=1,000N4=1,000
RR=
RR=0.5
N1=1,000 N2=1,000
RR=0.5
N3=1,000N4=1,000
RR=
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ObeseNot obeseAllD+D+D+D-D-D-E+E-Case-control study
OR=0.5
OR=0.5
OR=
OR=0.55001,500
OR=0.51,0003,000
OR=
OR=0.5
OR=0.5
OR=0.5
OR=0.5
cross-product ratio =1
OR=0.5
OR=
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Cyclic antidepressants and the risk of hip fracture
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E+E-D+ObeseNot obeseAllD-D+D-D+D-Covariate data on cases only
2,200440computerized database20,00010,10022,20010,540
medical record review
2,200440computerized database20,00010,10022,20010,540
2,000400??
20040??
2,20044020,00010,10022,20010,540
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E+E-D+ObeseNot obeseAllD-D+D-D+D-assume OR1 = OR2then: cross-product ratio =1 implies no confoundingCovariate data on cases only
2,000400??
20040??
2,20044020,00010,10022,20010,540
OR1
OR2
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What if confounding seems to be present?Extensions
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Option 1: No studyOption 2: No covariate measurementOption 3: 2-stage samplingOption 4: Case only measurements Suissa, Edwardes. 1997
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Confounder data on cases onlyObeseNot obeseE+E-D+D-Cross-product ratio =10Confounding plausibleD+D-
2,000220??
200220??
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Epidemiology 1997Extensions of Rays method to presence of confoundingRequires additional data from external sources
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SmokerNonsmokerAllE+E-D+D+D+D-D-D-TheophyllineConfounding; no interaction
1713309563,1544,080
14519
3811
14519 24% of 4,080
3811 76% of 4,080
14519 24% of 4,080obtained from population survey
3811 76% of 4,080
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Extensions of Rays method to presence of interactionRequires further additional data from external sourcesSuissa, Edwardes. 1997
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No interactionOR=0.5OR=0.5ObeseNot obeseE+E-D+D+D-D-12,00014010,20010,400
2,0004010,000100
20040010,00010,000
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Option 1: No studyOption 2: No covariate measurementOption 3: 2-stage samplingOption 4: Case only measurementsSuissa, Edwardes. 1997Multi-stage samplingPartial questionnairesPropensity score adjustmentsOthers:
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Monotone missingness
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Wacholder S, et al.
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Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
Cov 12345678Subject 12345678910n
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Wacholder S, et al.Restricted to a small number of discrete covariates
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Methodologic researchStrmer et al. AJE 2005, 2007Propensity score calibration
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Summarizes information about several covariates into a single number
Used for matching, stratification, regressionPropensity score
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Main cohort: selected covariates-error-prone scores estimated -regression coefficients estimated
Sample: additional covariates-gold standard scores-regression calibration
Advantage: multivariable techniqueStrmer et al. 2005
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Until the validity and limitation of [propensity score calibration] have been assessed in different settings, the method should be seen as a sensitivity analysis.Strmer et al. 2005
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Stage 1: 278 cases in 4561 pregnanciesStage 2: 244 cases + 728 non cases
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Relatively few examples of two-and three-phase sampling designs for case-control studies have appeared to date in the epidemiologic literature.This is unfortunate, because the stratified designs are easy to implement and can result in substantial savings.
NE Breslow (2000)
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Consent for second-stage interviews: Cases: 49% Controls: 39%