A set of training materials for professionals working in intervention epidemiology, public health microbiology and infection control and hospital hygiene.
Need help with your investigation or report writing? Ask the Expert. Free advice from the professional community.
You can't make decissions on this page's approval status because you have not the owner or an admin on this page's Group.
Studying the effect of an exposure (risk factor, behaviour, intervention etc) on a health outcome within a population is a key part of epidemiology. If life was truly simple, then measuring the distributions of the exposure and outcome of interest in a population and presenting these variables in a single two-by-two table would be enough to determine this effect (relative risk, odds ratio, vaccine effectiveness etc).
However, life is always more complex; there are 'third variables' that can distort (confound) our observation of the effect of interest. In some studies there may be many of these third variables, which we therefore call confounders.
In epidemiology there are different ways to address confounding.
Matching is most often used in a case control design, but it is also possible to use it with a cohort study design.
A confounding factor is a factor associated with the outcome (independently from exposure) and also associated with exposure (without being in the biological pathway between exposure and outcome). The confounding factor distorts the measurement of the effect (RR or OR) between the exposure and the outcome. Matching is the process that leads to have the same distribution of the confounding factor among cases and controls.
If the study was not planned with a matched design, an alternative solution to control confounding will be to perform a stratified analysis or to use multivariate models (for example a logistic regression model).
If matching was performed during the study design, it will need to be taken into account during the analysis. In this event, the formula used to calculate the OR will be different, and a special type of logistic regression should be used (conditional logistic regression). Therefore the table format and the analysis to be used in a matched case control study are different than those be be used in an unmatched case control study.
During the study design, matching can be performed according to different principles of matching, called frequency matching and individual matching,
Matching has some advantages and many disadvantages. Therefore, the decision on whether to do a matched design must be carefully thought, especially nowadays where epidemiologists are not performing calculations by hand and multivariate models like logistic regression are available from many softwares. The greatest advantage is that by doing a matched design, we will be sure that no strata contains few or none observations, therefore increasing the efficency of the analysis, with a reduced sample size and a higher amount of information per subject.
Matching is often used for convenience e.g. when it is difficult to obtain a random sample of the source population as controls. However there is no need to match since there are many limitations and traps when using a matching strategy. If resources are available a larger sample and an "a posteriori" stratified analysis may be easier to design and conduct, especially if we are confident that we can collect data on the main confounding variables. If we decide to match, we should make sure that the matching factor is a confounder, that we do not need to further study that factor, and that identification of matched controls will be logistically feasible and easier than an unmatched selection of more controls.
Join the discussion about this article in the forum!
Naomi Boxall posted on 10/8/2010 11:31:33 AM:
I hope the matching chapter is not too confounding (sorry, lame epi joke). I can see you've worked on it since the last time I looked. i think it would be possible to remove the entire tabulation for the situation where you have more than 1 matched control per case - it is very cumbersome, and I don't think it leads to much clarity. Perhaps you could just leave in the first paragraph of that section (saying that having 3 people in each strata means there are 6 'situations' rather than 4) would be sufficient - and then redirecting to a text book reference on matching? Perhaps your efforts are best concentrated on expanding on examples, for things like 'restrictions' etc. perhaps you could write a bit about the method they used?
Patty Kostkova replied on 10/8/2010 11:52:29 AM:
Hi Naomi, it's a good joke! Shall we leave some in forums to see the response when the site goes live? Look forward to seeing you at ESCAIDE.
Patty (clearly non-epi :-)
Vladimir Prikazsky replied on 10/8/2010 11:58:25 AM:
Hi there, is it ok to put 5* or is there any comment needed in order to save that rating?
You need to be logged in to post comments.
You can log in here. You can register here if you haven't done so yet.