6075_how does one analyze neurological outcomes-naidech
Instructor
Instructor:
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Welcome to this Neuroqual instructional video on how does one
analyze neurological outcomes with me, Andrew Naidech.
Clinical research depends upon showing the differences between
groups so collecting and selecting the appropriate data for your
analysis is crucial. Neurologists ask where is the lesion, not to
torture everyone, although we realize it does that, but because if
you know the neurological localization say brain, brain stem,
spinal cord, nerve and muscle, then you know the differential
diagnosis what it could be.
Similarly, statisticians will want to know what sort of data you
have at your disposal. The data you have will determine which
analysis you can do. The bad news for this instructional video is
that there will be a little math. The good news is that it will not be
hard and there will be no test at the end.
Continuous measures are those of magnitude without a break, such
as age. If continuous measures have a bell shaped distribution,
these are what we refer to as normally distributed data. You can
categorize continuous data into categories. For example, age at
least 65 or greater or less than 65. Note that you cannot go the
other direction. Once you have already categorized age as less
than 65, you cannot then guess how much greater or less than it
might be. Categorical data are in distinct bins such as the
frequency of race/ethnicity.
Dichotomous refers to two categories. For example, good verses
poor outcome, dead verses alive. Ordinal data are in ordered
categories. For example, the modified Rankin scale, a scale from
zero, best, to six, worst clinical outcome. Details are in the
previous slide deck on what are clinical neurologic outcomes.
The type of data you have will determine which statistical test you
can reasonably do. Two groups of normally distributed numbers
are appropriate for a T test. Note that not normally distributed
numbers such as the NIH stroke scale, a standardized neurological
exam and the Glasgow Coma Scale, a standardized scale of coma
are generally not normally distributed and usually not appropriate
for T tests. If you have three or more groups of normally
distributed numbers, the analysis of variance is appropriate. High
scaled Fishers Exact test and similar tests are for comparing how
often data occur in different categories.
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6075_how does one analyze neurological outcomes-naidech
Instructor
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For non normally distributed data such as the Glasgow Coma Scale
or [inaudible] and similar, two groups might be compared with the
[inaudible] or a similarly ranked test. For three or more groups,
the Kruskal Wallace 8 or another similar test is appropriate.
Regression analysis is to determine the associations of independent
variables on a single dependent variable. If your dependent
variable is a normally distributed number, then linear regression is
appropriate. For example, in medical school many encounter
Winter’s Formula, the expected partial pressure of carbon dioxide
is 1-1/2 times the serum bicarbonate plus eight. If your outcome is
dichotomous then logistic regress is appropriate. For example, if
you wish to determine the impact of age, neurologic severity and
history of hypertension on the odds of death, logistic aggression
would be appropriate. If your dependent variable is an ordinal
category, death, poor outcome, good outcome, excellent outcome
than ordinal aggression would be the appropriate analysis.
Many clinical papers and clinical trials target an improved
dichotomous outcome. For example, to seek to test a hypothesis
that a treatment or intervention increase the odds of a good
outcome as opposed to a poor outcome. The upside of this
analysis is that it is relatively easy to understand and the
assumptions of the analysis are relatively easy to satisfy. The
downside is that logistic aggression analysis does not detect
smaller improvements. For example, if an outcome is assigned to
detect a difference between good and poor outcome, you are
unable to detect if the treatment helps someone go from a really
poor outcome to a just barely poor outcome and this has reduced
statistical power to show in effect.
This is an example of hypothesized treatment effects showing how
ordinal aggression may be more powerful than dichotomous
logistic aggression. Four different models are presented that
underscore a variety of assumptions. Some of these play to the
strengths of ordinal aggression, some do not. For example, if you
have treatment that might improve outcome a little bit throughout
the modified Rankin scale from zero, best outcome to Rankin five
or six worst outcome than ordinal aggression, sometimes called
shift analysis is much more statistical technique requiring fewer
than half the patients that logistic aggression would require. If you
have a treatment that sometimes leads to an outstanding outcome,
again ordinal aggression or shift analysis is a generally efficient
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6075_how does one analyze neurological outcomes-naidech
Instructor
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technique. Logistic aggression may be more efficient depending
on where you put the cut point of good rather than poor outcome.
For a treatment that leads to outcome that is moderately good, but
almost no outcome that is outstanding than if you pick good
outcome as an excellent outcome, there is no reasonable chance
you will show a benefit of your therapy but ordinal logistic
aggression will be positive although perhaps less efficient than a
fortunately chosen dichotomous outcome.
The problem with specifying ordinal aggression or shift analysis in
advance for a clinical research study is that the data must meet
certain assumptions, notably proportional odds. If the data do not
meet these assumptions then ordinal aggression cannot be done. If
you find out how to tell if the data will be ordinal aggression
assumptions in advance, please do look at the contact information
on the first slide and let me know. Mostly what is done is that
logistic aggression with a dichotomous end point is chosen and
ordinal aggression is done post-hoc if the data meet the
assumptions of the analysis. This was what was done at interact
two, a study of aggressive blood pressure lowering acute
intracranial hemorrhage published in the summer of 2013 in the
New England Journal of Medicine.
If your intervention is highly effective, than your analysis is likely
to be statistically significant no matter how you do it. These are
summary data from studies of henicraectomy, wide brain
decompression for malignant [inaudible]. An example seen on the
right where you can see a large flap of bone has been removed
overlying infarction of the middle cerebral artery on the patient’s
left. One can see from the results that at any end points of the
modified Rankin Scale, patients who had surgery will more likely
have better modified Rankin Scales than those who did not.
Almost any way you choose to analyze these data will be
statistically significant.
Neuro-QOL and PROMIS data give you outcomes as continuous
numbers. These are usually normally distributed but may not be
depending upon your population and this should be checked. The
variance of these scores is known. Key help for doing paranaltsis.
These data are suitable for a wide variety of statistical tests and
meet assumptions for the usual analysis.
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6075_how does one analyze neurological outcomes-naidech
Instructor
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The sample size for continuous numbers is usually much more
efficient than those for fixed logistical aggression. Here’s another
paper with reasonable assumptions for what the effects of an
intervention are likely to be, comparing the variety of logistic
aggression models with fix dichotomous end points of the
modified Rankin Scale compared to ordinal aggression, a nonparametric test or linear tests such as the T test. In general with
these reasonable set of assumptions, logistic aggression requires
anywhere from 2,000 to 3,000 patients whereas ordinal aggression,
a non-parametric test or a T test would require about 1,500
patients.
Underscoring that these analyses are likely to be statistically much
more efficient as fixed outcomes as good verses poor. In addition
these outcomes such as the [inaudible] and the T test generally do
not require you to specify the distribution of data in advance the
way ordinal aggression does.
Another way of explaining these data is to compare a wide variety
of available statistical techniques from most to least sensitive. In
general, ordinal logistic aggression, T tests and Rank tests and
ordinal aggression with three or four groups are most efficient.
Some K squares are somewhat less efficient and median tests in
dead verses live are least statistically efficient.
Thank you so much for taking the time to watch this instructional
video. We acknowledge the generous support of NIH for the
Neuroqual project.
[End of Audio]
Duration: 10 minutes
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