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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.

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Measuring covariate data in subsets of study populations design options

Measuring covariate data in subsets of study populations: Design options

Jean-François Boivin, MD, ScD

McGill University

19 August 2007

Measuring covariate data_Presentation (November 14, 2007)


16th International Conference on Pharmacoepidemiology

Barcelona 2000



Option #1

Do not research that topic


Option #2

  • Conduct study without covariates

  • Scientifically reasonable for certain questions

  • Example: Sharpe et al. 2000


British journal of cancer 2002 the effects of tricyclic antidepressants on breast cancer risk
British Journal of Cancer2002The effects of tricyclic antidepressants on breast cancer risk

  • Genotoxicity in Drosophila

  • Comparison of antidepressants:

    • 6 genotoxic vs 4 nongenotoxic

  • Confounding unlikely


Option #3

“Confounding by other determinants was studied in analyses with data obtained by interviewing samples of subjects…”


“Confounding by other determinants was studied in analyses with data obtained by interviewing samples of subjects…”

List 4 - 6 different sampling strategies:

a) ?

b) ?

c) ?

d) ?


Two-stage sampling with data obtained by interviewing


Entire population (=truth) with data obtained by interviewing

E+

E-

Obese

D+

OR=0.5

D-

12,000

140

Not obese

D+

OR=0.5

D-

10,200

10,400

All

D+

OR=2.5

D-

22,200

10,540

32,740


E+ with data obtained by interviewing

E-

Obese

D+

D-

not available

Not obese

D+

D-

All

computerized databases

D+

D-

22,200

10,540


Two-stage sampling with data obtained by interviewing


Two-stage sampling with data obtained by interviewing

E+

E-

Obese

D+

OR1biased

D-

Not obese

D+

OR2 biased

D-

All

D+

250 x250250 x250

= 1

D-


Statistical analysis; further design issues with data obtained by interviewing

White. AJE 1982

Walker. Biometrics 1982

Cain, Breslow. AJE 1988

Weinberg, Wacholder. Biometrics 1990

Weinberg, Sandler. AJE 1991


Option 1: with data obtained by interviewing

Option 2:

Option 3:

Option 4:

No study

No covariate measurement

2-stage sampling

Case only measurement


Ray with data obtained by interviewinget al.

Archives of Internal Medicine 1991


Cyclic antidepressants and the risk of hip fracture with data obtained by interviewing


Confounding: Quick review with data obtained by interviewing

E+

E-

Obese

D+

D-

Not obese

D+

D-

All

All

D+

D-


Case-control study with data obtained by interviewing

E+

E-

Obese

D+

D-

Not obese

D+

D-

All

D+

D-


Cyclic antidepressants and the risk of hip fracture with data obtained by interviewing


Covariate data on cases only with data obtained by interviewing

E+

E-

Obese

D+

D-

Not obese

D+

D-

All

D+

D-


Covariate data on cases only with data obtained by interviewing

E+

E-

Obese

D+

D-

Not obese

D+

D-

  • assumeOR1= OR2

  • then: cross-product ratio=1 implies no confounding

All

D+

D-


Extensions with data obtained by interviewing

What if confounding seems to be present?


Option 1: with data obtained by interviewingNo study

Option 2: No covariate measurement

Option 3: 2-stage sampling

Option 4: Case only measurements

Suissa, Edwardes. 1997


Confounder data on cases only with data obtained by interviewing

E+

E-

Obese

D+

D-

Not obese

D+

D-

Cross-product ratio =10

Confounding plausible


Epidemiology 1997 with data obtained by interviewing

  • Extensions of Ray’s method to presence of confounding

  • Requires additional data from external sources


Confounding; no interaction with data obtained by interviewing

Theophylline

E+

E-

Smoker

D+

D-

Nonsmoker

D+

D-

All

D+

D-


Suissa, Edwardes. 1997 with data obtained by interviewing

  • Extensions of Ray’s method to presence of interaction

  • Requires further additional data from external sources


No interaction with data obtained by interviewing

E+

E-

Obese

D+

OR=0.5

D-

12,000

140

Not obese

D+

OR=0.5

D-

10,200

10,400


Option 1: with data obtained by interviewingNo study

Option 2: No covariate measurement

Option 3: 2-stage sampling

Option 4: Case only measurements

Suissa, Edwardes. 1997

Others:

Multi-stage sampling

Partial questionnaires

Propensity score adjustments


Monotone missingness with data obtained by interviewing


Wacholder S, with data obtained by interviewinget al.


Wacholder S, with data obtained by interviewinget al.

Restricted to a small number of discrete covariates


Methodologic research with data obtained by interviewing

Stürmer et al. AJE 2005, 2007

Propensity score calibration


Propensity score with data obtained by interviewing

  • Summarizes information about several covariates into a single number

  • Used for matching, stratification, regression


St with data obtained by interviewingürmer et al. 2005

  • Main cohort: selected covariates

    - “error-prone” scores estimated

    - regression coefficients estimated

  • Sample: additional covariates

    - gold standard scores

    - regression calibration

  • Advantage: multivariable technique


St with data obtained by interviewingürmer 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.”


Stage 1: with data obtained by interviewing278 cases in 4561 pregnancies

Stage 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: 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.”

  • Cases: 49%

  • Controls: 39%


Jean f boivin@mcgill ca

[email protected] 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.”


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