Hi everyone, and welcome to this lecture
on ways to reduce or avoid confounding.
So just a brief recap.
We learned this in epi one
and then we briefly looked at it in module one.
So to be a confound
where a variable must be associated with the exposure,
associated with the outcome, and not on the causal pathway.
Today we'll talk about ways to reduce or avoid confounding
and the different things that we'll talk about
are discussed below.
So restriction, matching, randomization,
stratification, and multivariate analysis.
And when I go through examples,
I like to try to talk about apples and oranges
or other fruits, something that's completely
kind of out of the medical field
to simplify things as much as I can.
So the first thing that we can do
to decrease the risk of confounding in our study
is called restriction.
So restriction is when we limit the study to individuals
who fall within a specified category of each confounder.
So in our example below,
we're now comparing apples to apples.
The limitation of restriction
is that we reduce the generalizability of our study.
So we are now looking at apples versus apples,
we're not looking at fruits,
and the results from our apples study
can't necessarily be generalized to all fruits.
We can also have difficulties recruiting participants.
And this becomes more of a problem
if we are restricting on several variables.
So for example, if I wanted to look at green apples
that were grown in Vermont in a certain months,
I may have trouble getting enough apples to do a good study.
The next thing we can do to decrease confounding
in our studies is called matching.
And this is where we select subjects
such that the confounders are distributed
in identical manner among groups.
This is really useful in case control studies.
We have to be careful of over matching,
which occurs when cases and controls
are inadvertently matched
on characteristics that is potentially causal.
For example, if you matched
on smoking status when the outcome is lung cancer.
We can't then look at the impact of smoking on lung cancer
because we've matched for that variable.
There are two types of matching.
We have individual matching,
which is for every case you match one
or more controls with the same value.
So here we would match every apple to every apple
or every green apple to every green apple,
or every green apple grown organically
in the state of Vermont
to every green apple grown organically
in the state of Vermont.
And then we have frequency matching,
which is where you create a control group
with similar means and proportions of confounding variables
as the case group.
So here we would get a group of apples and oranges
and then match that group characteristics to a group
which will be our control group.
The strengths and limitations of matching
are shown on the screen.
So matching is intuitive and easy to explain.
It guarantees a certain degree of comparability
even in small studies, and it's efficient,
especially if you're matching on a strong confounder.
The limitations is that it's costly.
If it's logistically complicated, it can be an efficient,
if you're matching on a weak confounder,
you can't study the matching variable.
And the more things we match on,
the harder it is to find a match.
And you also have the possibility of residual confounding.
So here's an example of a study that I've actually done
where we used restricting and matching
to decrease the impact of confounding on our results.
So here we looked at patient reported outcomes
at six to 12 months among survivors
of firearm injuries in the US.
So we wanted to look at traumatic injuries as our exposure
and patient reported outcomes as our outcome.
And then we thought that the green variables
listed on the screen
could be potential confounding variables.
And so we restricted the study to some categories
and then we matched the study on other categories.
So we restricted our study to adults
who were between the age of 18 to 64 years.
We looked only at patients admitted in 2015 to 2018,
and we restricted our study to moderate to severe injuries
from firearm injuries or motor vehicle crashes.
And then we matched the firearm injury patients
to motor vehicle crash injury patients
on age, gender, race, and education level,
and on a history of previous psychiatric illness diagnosis.
And then we found that firearm injury survivors
were more likely to have
daily pain screened positive for PTSD
and had worse physical and mental health
related quality of life
compared to similarly injured motor vehicle crash survivors.
So the next thing that we can do
to decrease the impact of confounding on our studies
is called randomization,
which is where you randomly allocate individuals
to study groups.
The strengths is that the groups are likely to be balanced
in terms of identified confounders
and potential confounders that weren't identified.
So in our example below
we're comparing a random allocation of apple and oranges
to a random allocation of apples and oranges.
And potential unknown confounder
that may impact our study results could be worms,
and we don't know which fruit have worms
because we can't see which ones have worms.
But because we've randomly allocated the fruit,
we've also randomly allocated the worms.
The limitations are that you can have unlucky randomization,
which can cause an imbalance,
and this is more likely with small sample sizes.
And randomization isn't always feasible or ethical.
The next way that we can reduce
the potential impacts of confounding on our study
is called stratification.
And this is when
the study population is broken into subgroups
according to the level of potential confounding factors,
and you actually produce results for each level.
So the strength is that you have more granular data,
and the limitations are that you have smaller sample sizes
within each subdivision.
So here we would produce results
for our oranges versus oranges analysis,
and then we would also produce results
for our apple versus apple analysis.
Now, this becomes more difficult
if we want to do an analysis on
apples that are organic versus apples that are not organic.
So we're doing,
it becomes more complicated
if you stratify it down to too many levels.
So an example of a study that I've done,
where we looked at,
where we used stratification
is this study where we looked at the impact of
income on emergency general surgery outcomes
in urban and rural areas.
So we wanted to have income as our exposure
and emergency general surgery adverse outcomes
as our outcome.
And then rurality was the variable
that we thought would be the confounding factor
that we wanted to stratify by.
So low income emergency general surgery patients
have higher rates of postoperative adverse events
compared to high income patients.
And this may be related to healthcare segregation.
The emergent nature of emergency general surgery conditions
and limited number of emergency general surgery providers
in rural areas may result in less healthcare segregation
and therefore less variability
in emergency general surgery outcomes in rural areas.
And so to do this analysis,
we actually stratified our analysis
into rural groups and urban groups,
and we found that income was associated
with higher postoperative adverse events in urban,
but not in rural settings.
So the socioeconomic disparity
in emergency general surgery outcomes in urban settings
may reflect healthcare segregation,
which is a differential access
to high quality healthcare for low income patients.
The last thing that we'll touch on today
is multi-variable or multivariate regression analysis.
These are advanced statistical methods
that allow us to remove the effects
of specified confounding variables.
And we'll really touch on these analysis techniques
later on in epi two.
But basically they allow us to see
if there's a difference after removing
the effects of specified confounding variables.
The strengths are that it's inexpensive
and that it can handle multiple confounders simultaneously.
So this is a study where we looked,
we did a multivariate analysis.
We looked at the lethality of active shooter incidents
with and without semi-automatic rifles in the US.
And so our exposure was the presence of
semi-automatic rifles at an active shooter event.
And our outcome was the number of persons wounded or killed.
The variables in green
were the potential confounding variables
that we wanted to adjust for.
So the place of the shooting, the year of the shooting,
and the presence of other firearms.
And then we found after doing our analysis
that more people were wounded or killed
in incidents in which semi-automatic rifles were used
compared to incidents involving other firearms.
So today we've briefly discussed
on the ways to reduce or avoid confounding
when we are designing a study,
restriction, matching, randomization, stratification,
and multivariate analysis.
So that concludes this lecture.
Take care.