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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 Presentation
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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)
16th International Conference on Pharmacoepidemiology Barcelona 2000
What about missing covariate data?
Do not research that topicOption #1
Conduct study without covariatesScientifically reasonable for certain questionsExample: Sharpe et al. 2000Option #2
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
Option #3Confounding by other determinants was studied in analyses with data obtained by interviewing samples of subjects
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) ?
Two-stage sampling
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
ObeseNot obeseAllE+E-D+D+D-D-22,20010,540not availablecomputerized databasesD+D-
2,20044020,00010,100
Two-stage sampling
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
White. AJE 1982Walker. Biometrics 1982Cain, Breslow. AJE 1988Weinberg, Wacholder. Biometrics 1990Weinberg, Sandler. AJE 1991Statistical analysis; further design issues
Option 1:Option 2:Option 3:Option 4: No study No covariate measurement 2-stage sampling Case only measurement
Ray et al.Archives of Internal Medicine 1991
Cyclic antidepressants and the risk of hip fracture
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=
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=
Cyclic antidepressants and the risk of hip fracture
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
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
What if confounding seems to be present?Extensions
Option 1: No studyOption 2: No covariate measurementOption 3: 2-stage samplingOption 4: Case only measurements Suissa, Edwardes. 1997
Confounder data on cases onlyObeseNot obeseE+E-D+D-Cross-product ratio =10Confounding plausibleD+D-
2,000220??
200220??
Epidemiology 1997Extensions of Rays method to presence of confoundingRequires additional data from external sources
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
Extensions of Rays method to presence of interactionRequires further additional data from external sourcesSuissa, Edwardes. 1997
No interactionOR=0.5OR=0.5ObeseNot obeseE+E-D+D+D-D-12,00014010,20010,400
2,0004010,000100
20040010,00010,000
Option 1: No studyOption 2: No covariate measurementOption 3: 2-stage samplingOption 4: Case only measurementsSuissa, Edwardes. 1997Multi-stage samplingPartial questionnairesPropensity score adjustmentsOthers:
Monotone missingness
Wacholder S, et al.
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
Wacholder S, et al.Restricted to a small number of discrete covariates
Methodologic researchStrmer et al. AJE 2005, 2007Propensity score calibration
Summarizes information about several covariates into a single number
Used for matching, stratification, regressionPropensity score
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
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
Stage 1: 278 cases in 4561 pregnanciesStage 2: 244 cases + 728 non cases
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)
Consent for second-stage interviews: Cases: 49% Controls: 39%