Matching in case control designs
Download
1 / 21

Matching in Case-Control Designs - PowerPoint PPT Presentation


  • 448 Views
  • Updated On :

Matching in Case-Control Designs. EPID 712 Lecture 13 02/23/00 Megan O’Brien. Objectives. Discuss methods of matching in case-control studies Discuss advantages and disadvantages of matching in case-control studies Discuss methods of analyzing matched case-control data.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Matching in Case-Control Designs' - aislinn


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Matching in case control designs l.jpg

Matching in Case-Control Designs

EPID 712

Lecture 13

02/23/00

Megan O’Brien


Objectives l.jpg
Objectives

  • Discuss methods of matching in case-control studies

  • Discuss advantages and disadvantages of matching in case-control studies

  • Discuss methods of analyzing matched case-control data


Slide3 l.jpg

  • Recommended reading: Szklo and Nieto pgs: 40-48 277 314-317 328-331

  • Further reading: See Rothman and Greenland. Modern Epidemiology


What is matching l.jpg
What is matching?

  • The process of making a study group and a comparison group comparable with respect to extraneous factors (Last JM. A Dictionary of Epidemiology. 3rd Ed. New York, NY: Oxford University Press; 1995)

  • In case-control studies, we match to make cases and controls as similar as possible with regard to potentially important confounding factors


Types of matching l.jpg
Types of matching

  • Individual (paired) matching: for each case, one (or more) controls with the relevant characteristics matching the case are chosen

    • For continuous variables such as age or weight, controls may be selected if they are within a specified range of the control value

      • Example: Age ± 2 years

      • Example: Weight ± 5 pounds


Slide6 l.jpg

  • When matching on a variety of characteristics, including continuous variables, it may be very difficult to individually match on all characteristics

  • Minimum Euclidean distance: identify the individual who is the closest match with regard to all of the variables.

    • We usually do this using mathematical modeling techniques


Slide8 l.jpg


Advantages of matching l.jpg
Advantages of Matching distribution of the relevant characteristic in the controls is similar to the distribution in the cases

  • May be the best way to control for a strong confounder when there is little overlap of the confounder between the cases and controls

    • Example: If the cases tend to be older (CHD, prostate cancer) and a random sample of controls would result in a much younger control group, then there may not be much overlap of age between cases and controls

    • This lack of overlap makes adjustment for confounding difficult. Why?

  • When the confounder is strong, matching increases the efficiency of the study (by decreasing the width of the confidence intervals around an estimate)


Slide10 l.jpg


Disadvantages of matching l.jpg
Disadvantages of Matching cases and controls are identified from a reference population for which there is no available sampling frame (list).

  • It may difficult (and expensive) to identify a matched control

  • When you match on a characteristic, you create an equal distribution in the cases and controls. Therefore, you cannot examine the association between the matched characteristic and the outcome


Slide12 l.jpg


Slide13 l.jpg

  • If you match on a characteristic that is a weak confounder, you may decrease the statistical power of your study

  • If you match on a characteristic that is strongly correlated with the exposure of interest, you may overmatch

  • If you categorize continous variables too broadly, you may still have residual confounding


Overmatching l.jpg
Overmatching you may decrease the statistical power of your study

  • Overmatching occurs when you match on a variable that is strongly correlated with the exposure of interest

  • By setting the distribution of the matching variable to be equal between cases and controls, you are effectively setting the distribution of the exposure variable to be equal between cases and controls

  • In doing so, you will be unable to detect a difference in exposure between cases and controls


Residual confounding l.jpg
Residual confounding you may decrease the statistical power of your study

  • Occurs when you categorize continuous variables

  • Ex. Create age categories for matching 20-25 25-30 30-35For each case between 20 and 25, select a control who is also between 20 and 25

  • Now your cases and controls are comparable with respect to age right?


Slide16 l.jpg

Are these two groups really comparable? you may decrease the statistical power of your study


Analysis of matched data l.jpg
Analysis of matched data you may decrease the statistical power of your study

  • Mantel-Haenszel OR (pg. 277)

    • If we pair-match cases and controls, we keep them in pairs for the calculation of the odds ratio

    • What combinations will be possible with regard to exposure?


Slide18 l.jpg

  • Concordant pairs: you may decrease the statistical power of your study

    • Both case and control are exposed

    • Neither case nor control are exposed

  • Discordant pairs

    • Case is exposed, control is not

    • Control is exposed, case is not

  • What do the concordant pairs tell us?

    • Nothing

  • We are interested in the discordant pairs


  • Slide19 l.jpg

    Note that each cell contains pairs. So the OR is a ratio of the discordant pairs.

    See pg. 280 for the derivation of this formula.

    How do you interpret the MH OR?

    Just as usual.



    Multivariate analysis of matched data l.jpg
    Multivariate analysis of matched data controls.

    • Conditional logistic regression (pg. 314)

      • Use when you have individual matching

      • Analogous to logistic regression, but the model takes into account the pairing of cases and controls

    • Logistic regression

      • Use when you have frequency matching

      • Simply include the matching variables in the model


    ad