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.

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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)


Measuring covariate data in subsets of study populations design options

16th International Conference on Pharmacoepidemiology

Barcelona 2000


Measuring covariate data in subsets of study populations design options

What about missing covariate data?


Measuring covariate data in subsets of study populations design options

Option #1

Do not research that topic


Measuring covariate data in subsets of study populations design options

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


Measuring covariate data in subsets of study populations design options

Option #3

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


Measuring covariate data in subsets of study populations design options

“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) ?


Measuring covariate data in subsets of study populations design options

Two-stage sampling


Measuring covariate data in subsets of study populations design options

Entire population (=truth)

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


Measuring covariate data in subsets of study populations design options

E+

E-

Obese

D+

D-

not available

Not obese

D+

D-

All

computerized databases

D+

D-

22,200

10,540


Measuring covariate data in subsets of study populations design options

Two-stage sampling


Measuring covariate data in subsets of study populations design options

Two-stage sampling

E+

E-

Obese

D+

OR1biased

D-

Not obese

D+

OR2 biased

D-

All

D+

250 x250250 x250

= 1

D-


Measuring covariate data in subsets of study populations design options

Statistical analysis; further design issues

White. AJE 1982

Walker. Biometrics 1982

Cain, Breslow. AJE 1988

Weinberg, Wacholder. Biometrics 1990

Weinberg, Sandler. AJE 1991


Measuring covariate data in subsets of study populations design options

Option 1:

Option 2:

Option 3:

Option 4:

No study

No covariate measurement

2-stage sampling

Case only measurement


Measuring covariate data in subsets of study populations design options

Ray et al.

Archives of Internal Medicine 1991


Measuring covariate data in subsets of study populations design options

Cyclic antidepressants and the risk of hip fracture


Measuring covariate data in subsets of study populations design options

Confounding: Quick review

E+

E-

Obese

D+

D-

Not obese

D+

D-

All

All

D+

D-


Measuring covariate data in subsets of study populations design options

Case-control study

E+

E-

Obese

D+

D-

Not obese

D+

D-

All

D+

D-


Measuring covariate data in subsets of study populations design options

Cyclic antidepressants and the risk of hip fracture


Measuring covariate data in subsets of study populations design options

Covariate data on cases only

E+

E-

Obese

D+

D-

Not obese

D+

D-

All

D+

D-


Measuring covariate data in subsets of study populations design options

Covariate data on cases only

E+

E-

Obese

D+

D-

Not obese

D+

D-

  • assumeOR1= OR2

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

All

D+

D-


Measuring covariate data in subsets of study populations design options

Extensions

What if confounding seems to be present?


Measuring covariate data in subsets of study populations design options

Option 1: No study

Option 2: No covariate measurement

Option 3: 2-stage sampling

Option 4: Case only measurements

Suissa, Edwardes. 1997


Measuring covariate data in subsets of study populations design options

Confounder data on cases only

E+

E-

Obese

D+

D-

Not obese

D+

D-

Cross-product ratio =10

Confounding plausible


Measuring covariate data in subsets of study populations design options

Epidemiology 1997

  • Extensions of Ray’s method to presence of confounding

  • Requires additional data from external sources


Measuring covariate data in subsets of study populations design options

Confounding; no interaction

Theophylline

E+

E-

Smoker

D+

D-

Nonsmoker

D+

D-

All

D+

D-


Measuring covariate data in subsets of study populations design options

Suissa, Edwardes. 1997

  • Extensions of Ray’s method to presence of interaction

  • Requires further additional data from external sources


Measuring covariate data in subsets of study populations design options

No interaction

E+

E-

Obese

D+

OR=0.5

D-

12,000

140

Not obese

D+

OR=0.5

D-

10,200

10,400


Measuring covariate data in subsets of study populations design options

Option 1: No 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


Measuring covariate data in subsets of study populations design options

Monotone missingness


Measuring covariate data in subsets of study populations design options

Wacholder S, et al.


Measuring covariate data in subsets of study populations design options

Wacholder S, et al.

Restricted to a small number of discrete covariates


Measuring covariate data in subsets of study populations design options

Methodologic research

Stürmer et al. AJE 2005, 2007

Propensity score calibration


Measuring covariate data in subsets of study populations design options

Propensity score

  • Summarizes information about several covariates into a single number

  • Used for matching, stratification, regression


Measuring covariate data in subsets of study populations design options

Stürmer 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


Measuring covariate data in subsets of study populations design options

Stürmer 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.”


Measuring covariate data in subsets of study populations design options

Stage 1: 278 cases in 4561 pregnancies

Stage 2: 244 cases + 728 non cases


Measuring covariate data in subsets of study populations design options

“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)


Measuring covariate data in subsets of study populations design options

  • Consent for second-stage interviews:

  • Cases: 49%

  • Controls: 39%


Jean f boivin@mcgill ca

[email protected]


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