So in this lecture set,
Lecture 10, we're going to actually talk
about something called propensity scores.
Which will give us another approach to
estimating adjusted associations above and
beyond doing traditional
multiple regression.
Where we include all
So in this section we'll put some numbers
around what we were talking about in
section A and look at the results
from simple Cox regression where we
have binary or categorical predictors.
So at the end of this lecture
section hopefully you'll be able to
i
Hi, and welcome back.
In this section,
we'll talk briefly about accounting for
the uncertainty in our slope
estimates from Cox regression.
I don't think we'll see anything
that will surprise you here.
And then, we'll also talk briefly about
translating Co
In this lecture set, we're going to
formally define and discuss some ways of
dealing with something we've eluded to and
spoke of before.
The idea of Confounding.
So in this set of lectures,
we will formally define confounding.
And give some explicit
examp
Greetings, and
welcome to lecture set five.
In this lecture set, we're going to
talk about another phenomena that
involves looking at a two
variable relationship outcome and
exposure, and incorporating information
about a behind the scenes third variable.
Okay everyone,
welcome back to Lecture 7, Section C.
And here we're going to look at some
multiple logistic regression examples from
public health and medical literature.
So, hopefully,
you'll have some more exposure to
interpreting the results from simpl
Okay.
In this section we'll consider simple
logistic regression when our predictor
of interest is now continuous.
And hopefully this will give you
some insight as to why we need to
transform the estimated binary outcome
from a proportion to a log odds.
Wh
Greetings and welcome to Section B.
In this section we're going to start
with multiple linear regression now, and
look at some examples, to make specific
comparisons to what we talked about in
the general context in
the previous section.
So, hopefully by
Greetings!
In this next lecture set,
we're going to more formally
revisit the concept of confounding.
This is an idea we talked about
in Statistical Reasoning 1, and
now we're going to formally define it
in terms of the mechanisms that cause
confounding t
Welcome back.
In this very short section, we're just
going to give a little bit of insight as
to how adjusted estimates come about,
the general idea behind the computations.
What we'll see, very shortly, is that
multiple regression methods provide
a nice
Hello and welcome to Lecture 8.
In this lecture set we are going to
discuss Cox Proportional
Hazards Regression for Estimation,
Adjustment and Basic Prediction.
And this will parallel what
we've done with logistic and
linear regression in
the previous two
Greetings and welcome to lecture set nine.
In this lecture set, we'll give
a brief overview to handling effect
modification in a multiple
regression context, and
also look at another approach above and
beyond categorizing continuous
predictors in a regres
So in this section we'll give some
examples of the use of interaction terms
and their presentation with
the results stemming from them,
from published research.
And so this will give you exposure
to several examples of the use of
interaction terms in rese
Greetings, and
welcome to Lecture Set 8, Section B.
In this section we'll give a brief
treatise of the basics of model selection.
And show how the results from multiple Cox
regression, can be presented in terms of
estimated survival curves or
outcomes fro
Greetings.
In Section C here we're going to talk
a little bit about handling
non-linear relationships with
a continuous predictor in regression and
doing something different.
And we'll talk about the potential
advantages in certain situations above and
be
Hi everyone.
John here again.
In this next set of lectures,
net lecture nine, we're going to
extend the regression models that
we've seen before to go above and
beyond just estimating the relationship
between outcome and multiple predictors at
once, and h
Greetings everyone.
John here again.
In this next lecture set,
lecture ten, we'll talk briefly about
another method that can be used
when we want to control for
a lot of potential confounders, but
we're not particularly interested in
their relationships w
So in this section we'll give two more
examples of propensity score methods and
propensity score adjustment.
Focusing on two articles from
the scientific literature.
So hopefully this will reinforce
the concept of propensity scores and
propensity score ad
All right everyone, welcome to section B.
Here we're going to look at multiple
logistic regression, talk about the basics
of model selection, basically reiterate
what we said for linear regression.
And show how to estimate proportions or
probabilities fro
So in this section we'll continue our
discussion of effect modification and
we'll look at several examples
of studies where one of
the researcher questions involved was
investigating effect modification.
So this lecture section will give more
examples of
So, this section we'll look at
some examples of the use of
Cox Regression from the public health and
medical literature.
And hopefully after this
you'll have experience of
interpreting results from simple and
multiple Cox regression models presented
in th
In this section we're going to look
at a simple regression method for
when we have time to aventate it with the,
in the presence of sensory.
And this something in it's full name
called Simple Cox Proportional Hazards
Regression, frequently
abbreviated as
So in this next lecture set,
Lecture 5, we're going to talk
about a phenomenon called effect
modification, or interaction.
Which like confounding, involves
interpreting an outcome exposure, or
two variable relationship, with regards
to another variable be
Greetings.
John here again, and
again here to talk about multiple
logistic regression action.
This time, when we're actually regressing
time to event data in the presence of
the censoring on predictors of interest.
So, we're going to use a multiple
Cox re
So in this section,
we'll look at simple Cox Regression
with A Continuous Predictor.
And hopefully, by the end of this section,
you'll be able to, again,
interpret the slopes from simple
Cox Regression, as log hazard ratios, but
interpret the proper compa
So in this section, we're going to
take on simple logistic regression,
which is very similar in spirit
to simple linear regression, and
the general linear model framework we
set up at the beginning of lecture one.
In this situation, however, we're dealing
So, in this next section, we're going to
take on the case of simple regression for
time to event outcomes when
there may be censoring.
And, what we're going to see is that
the method we're going to talk about,
which is most commonly
used among the choices
Hello, everyone.
In this section,
we're just going to do a short overview
of what we've just been talking about in
Lecture Sets 4 and 5.
And even in the beginning
of Lecture Set 5,
I wanted to talk about distinguishing
confounding from effect modification
All right.
In this section,
we'll talk about something
we mentioned in section A,
the idea of adjusted estimates,
adjusted for potential confounders.
And we'll talk about the presentation,
the interpretation and
the utility of using these for
assessing po
Greetings and welcome to Lecture Set 6.
This has a rather long title but
it's actually composed of two parts.
In Lecture 6A, we're going to
give an overview of the idea of
multiple regression for
estimation adjustment in basic prediction.
And then we're g