Transcript IOA presentation on Clinical research

RESEARCH
Charles E. Henley, D.O.
Associate Dean for Clinical Affairs
Marian University College of Osteopathic Medicine
Clinic Project
• You are a partner in a family medicine practice and would like to improve
the glycemic control of your type ll diabetic patients.
You have heard about the use of patient report cards and wonder if it
would work in your practice. You are also wondering how to do a
study to find the answer, so you use the planning algorithm:
P= population ( all type ll diabetic patients in the practice)
I = Intervention ( patients receive report cards about their
progress, on A1C, Weight, Blood Pressure, group A)
C= control group ( Usual care. Patients do not receive report cards,
( group B)
O= outcomes ( which group shows the most improvement over time in
markers for improved control).
Clinic Project
• Patients were randomized into groups A or B based on
the last digit of their SS # either an odd or even. At the
end of 4 and 6 months the data was assessed to see
which group had better outcomes.
• Group A showed a significant lowering of A1C as well as
BP control and weight loss
• Group B showed some improvement but not as much
and BP and weight stayed the same.
• You conclude that patient report cards are a valuable
method for maintaining glucose control
Clinic Project
• This was a prospective clinical trial.
• The independent variable was report cards +/- and
dependent variables were BP, Weight and A1C.
• The research question was whether or not report cards
would improve clinical outcomes and the hypothesis was
yes , they could.
• Because the variables were both bivariate and
continuous, the hypothesis testing would consist of a
simple t test, done by computer after the data was
entered.
• This was research, and it started with a simple
observation and idea for helping patients.
Where do research ideas come from?
• Things you see every day in your clinical practice and
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have questions about.
Things you read about. ( the use of report cards)
Things you hear about in conferences and meetings.
Questions that students or residents ask
Things from the lay press or news stories.
Questions from patients.
Conversations with colleagues.
Quality improvement activities in the hospital.
From your own curiosity.
What are the barriers to research?
Too time consuming, I need to see patients to
make money
2. Too costly, I would need a grant.
3. I don’t have the training in statistics
4. I would need to hire a consultant ( see #2)
5. I don’t know what I could do
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Barriers can be overcome
• Time: In the report card study, the nursing staff and clerks
handled the report card logistics and patient education
about the report cards. They also put the data into the
computer. the physician just saw the patients as they
would normally.
• Cost: minimal to design a report card and print them out
in the clinic.
• Training: simple data entry on a spread sheet and
rudimentary knowledge of how to think about study
methods. Computers do most of the work.
• Ideas: we just listed all the places good ideas come from.
Why do research
• To answer a clinical question from practice
• Intellectual curiosity
• Interest in the topic
• Intellectual challenge
• Enjoy asking questions and getting answers
• Break from routine of practice
• To be part of some other group or organization
(PBN, Med School).
MUCOM
• Clinical faculty interests
Mike Kuchera
• Doctor of osteopathic medicine (DO) degree from the Kirksville
• College of Osteopathic Medicine (KCOM)
• He is studying the effects of exercise in multiple sclerosis, and
the role of osteopathic manipulative treatment (OMT) and other
biomechanical approaches in improving function and quality of
life in Parkinson's Disease. He has also done research on the
efficacy of OMT in carpal tunnel syndrome and ankle sprains.
Thad Wilson PhD U Utah
• Research interests are in thermal physiology, eccrine sweat
glands, and the interaction between the environment and health
/ disease. He also has a special interest in sympathetic nervous
system responses to manual therapies.
• Charles Henley, DO, MPH
• Research interests in heart rate variability as a surrogate for
autonomic nervous system activity and how this is influenced
through OMM.
Research Training
• The more you know about the rules of science and
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methodology and various options for testing a hypothesis
the more enjoyment you get out of asking questions.
No special math skills are required; computers do the
heavy lifting.
Must have an innate curiosity about what we do and why
we do it that way. (HRT and the woman’s health initiative)
There are tools that can help
You will all receive a copy of the research workbook,
“Every Doc can do Research” Just follow the directions.
Getting started
• Asking the research question.
• An essential ingredient in any quality research project is a well-defined
research question.
• The question should be simple and specific - a small, clearly defined project
is always preferable to one that is large and vague.
• (e.g. Should we treat pregnant women who are carriers for Group B strep (GBS)?)
• Search the Literature; OVID or PUBMED.
• When you are done reading these articles, you should have a good idea of
what has been accomplished, how it was done, the problems that others have
had in their studies, and how the results of your study can add to the existing
field of knowledge.
• write a summary of your literature search,
Organizing Data
• There are many good reasons to organize data
• 1. to make sense of it.
• 2. To see it clearly
• 3. To understand it better
• 4. To be able to manipulate it
• 5. To test it
• Some data is descriptive or observational and is meant to
show or describe something
• Some data is trying to answer a question in a structured
way.
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Data Reduction
frequency distribution
Random numbers ( test scores, pulse rates, systolic BP, etc.)
95.00, 100.75, 126.00,114.00.134.25, 116.75,.97.5,102.25,
110.00, 125.00, 144.00, 112.00, 82.5, 135.00, 115.25, 132.00,
105.00, 118.25, 121.75, 109.25, 136.00, 124.00, 91.00, 148.00,
125.25, 88.5, 133.25, 95.00, 104.5, 135.00, 108.25, 122.25,
107.25, 137.00, 114.00 124.00, 118.00, 119.00, 117.25, 03.25,
115.00, 116.5, 99.5, 106.00, 103.75, 115.25, 128.5, 105.00
Of questionable value, no trends, cannot tell much from this
data
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Data Reduction
• Array in ascending order; lowest to highest
• 82.5, 88.5, 91.0, 93.25, 95.0, 99,5, 100.75, 102.25,
105.00, 106, 107.25, 108.25, 109.25, 110.0, 112.0,
113.25, 114.0, 115.0 115.5, 115.75, 116.5, 116.75, 118.0,
119.0, 121.75, 122.5, 124.0, 124.5, 125.0, 125.25,
126.0, 128.5, 128.75, 132.0, 133.25, 134.25, 135.0,
135.5, 136.0, 137.0, 144.0, 148.0
• Orders the data, can now see presence of large
concentrations of groups, upper and lower 50% tiles,
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Data Reduction
Now group the data, by intervals of 10 ( or whatever you want) and count the
number of variables in each class. This is the frequency distribution of the
variables.
•82-92……..3
•93-103……8
•104-114….12
•115-125….14
•126-136….10
•137-147…..2
•148-158…..1
•Graphically Represent the data, (seven classes, intervals of 10,)
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Frequency Histogram
(Frequency distribution)
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10
12
8
2
2
1
82-92
93-103 104-114 115-125
126-136 137-147
148-158
Research Methods
● Hypothesis: An educated guess about an association that
is testable, i.e. group A is better than group B
● Null hypothesis: There is no difference between the
groups.
● Hypothesis testing: A test statistic is computed based
on type of data and compared with a critical value from a
statistical table. When the test statistic exceeds the critical
value the null hypothesis is rejected and the difference is
statistically significant.
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Study design
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P: Cohort of (hypertensive patients)
I: Group A (continuity clinic)
C: Group B (random care)
O: Better BP control yes/no
Types of Studies
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Heart disease and serum cholesterol
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A review of patients records who have had heart
attacks reveals that several had elevated serum
cholesterol levels
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This constitutes a case series.
Types of Studies
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In the case series you cannot determine
whether or not the elevated serum
cholesterol is associated with heart attacks
unless you compare serum cholesterol in
patients with heart attacks to serum
cholesterol in patients who did not have heart
attacks. This constitutes a prevalence or
cross-sectional study
Types of Studies
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If we were to select persons who had actual
heart attacks (cases) and compare them to
persons without heart attacks (controls) with
respect to a history of elevated serum
cholesterol levels, this would be a casecontrol study. But it does not tell you if the
elevated serum cholesterol caused the heart
attack.
Inference (sampling)
● Since we can never test the entire population for a specific
characteristic, we must measure it in a sample and infer
that this is representative of the larger population.
● Is the outcome we see from testing the sample the result
of chance? Or does it really exist in the population
studied.
● If the difference in outcomes between groups is small we
need more subjects to show a significance. If the
difference is larger, we need fewer subjects.
Statistical Significance
P values
● If the computed statistic is larger than > 0.05
it is outside the limits of the normally
distributed parameters and the null hypothesis
is correct. When it is < 0.05 we reject the null
hypothesis and the observation made from the
study is considered statistically correct.
Confidence Intervals
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Based on a normal distribution with 95% of the curve
represented by -1.96 and + 1.96 and a mean of zero.
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Any observation (x) falls between -1.96 and +1.96
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X (observed) = (SD)(+/- 2) (2 =1.96 rounded up)
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Example: Patient W has BP observation of
x=100mmHg. There is an estimated SD =7mmHg. The
confidence limits for the mean (m) are: 100- (2)(7)=86
and100+2(7)=114
The 95% confidence interval for (m) is between 86 and 114
Normally Distributed Data
● Normal distribution of data
● 1 SD is 68% of data points
● 2 SD is 95% of all data
● 3 SD is 99.7 % of data
Understanding Data
Variables
● In experimental research, variables are usually described
in terms of being an independent variable or a dependent
variable. They can also be confounding variables.
● In a study of pancreatic cancer and coffee drinking, the
presence of smoking was a confounding variable.
● The Independent variable would be coffee drinking
Understanding Data
Independent variable:
● In experimental research the Independent Variable is
the variable controlled by the investigator.
Example: in a clinical trial for acute bronchitis,
the independent variable is the treatment;
(antibiotics vs. placebo)
● In observational studies, the independent
variable is the cause in a cause and effect relationship
Example: smoking and lung cancer; smoking is
independent variable.
Understanding Data
● In epidemiologic studies, the independent variable
is the exposure
Example: H1N1 and the flu, The exposure to virus is
the independent variable.
● The Dependent variable is the outcome variable, and
will be affected by the independent variable in some way.
In this example the presence or absence of the flu is the
dependent variable.
Scales
Scales are classified as:
● Nominal or Categorical
● Ordinal
● Ratio or Interval
● Continuous or Parametric
Scales
● Nominal or categorical scales allow classification by
name only such as sex, religion, race and the
dichotomous scale of yes no.
● Ordinal scales have an order but the distance between
may not be the same. i.e., The distance between fair
and good may not be the same as the distance
between good and great.
Scales
● Ratio or Interval scales are ordered like ordinal scales
but the distance between scales is the same. Ratio
scales also have an absolute zero.
● Continuous or parametric scales are just that…
continuous. age, BP, test scores, are all continuous
and have a Gaussian distribution of normality. The
data is evenly distributed across a standard bell
shaped curve and treatment of data is by parametric
analysis.
Selecting a statistical test
(test of the hypothesis)
● Identify the independent, dependent and
confounding variables and the type of scale or data
being used.(example).
● If the independent and dependent variables are both
nominal (binomial or two values), then the selection
of a test will be one of the non parametric tests such
as chi-square.
● Both variables are nominal: Rogaine vs. placebo and
hair growth (yes/no) = chi-square
Bivariate Analysis
Independent Dependent
Interval
Nominal
(2 values)
Nominal
(>2 values)
Nominal
(2 values)
t / z Test
Chi-square
chi-square,
MannWhitney
Nominal
(>2 values)
ANOVA
Chi-square
Chi-square
Interval
Correlation/ Logistical
Regression Regression
Discriminate
Analysis
Using the information in a study
●Study question (hypothesis): Does
continuity of care improve BP control?
●Independent variable: continuity of care
●Dependent variable: BP control: yes/no
●Scale: Nominal bivariate(dependent
variable)
●Scale: Nominal bivariate (independent
Variable)
●Statistic (Hypothesis testing): chi-square
Article on heart rate variability
• Osteopathic Medicine and Primary Care
• BioMed Central
• Research
• Open Access
• Osteopathic manipulative treatment and its
relationship to autonomic nervous system activity as
demonstrated by heart rate variability: a repeated
measures study
Charles E Henley1, Douglas Ivins1, Miriam Mills2,
Frances K Wen1 and Bruce A Benjamin*3
HRV Study
• A theoretical basis for the action of OMT and its effect in
the body has been advanced based on autonomic
activation causing concomitant vasodilatation, smooth
muscle relaxation, and increased blood flow, resulting in
improved range of motion, decrease in pain perception,
and/or change in tissue.
HRV Study
• Indirect methods have been developed and refined to
provide noninvasive markers of autonomic balance, with
heart rate variability (HRV) being commonly used. HRV is
based on the inherent variation of the R to R intervals of a
standard electro- cardiogram (ECG), with these variations
largely due to changes in autonomic balance at the sinus
node.
Methods
• Patients were first placed in the supine position on a tilt
table and measurements of sympathetic activity and vagal
tone were made using the LF/HF ratios.
• Patient were then placed at a 50 degree angle which put
them into a strong sympathetic environment.
• Muscle energy OMM was then performed in this upright
position and a strong parasympathetic response was
observed.
Conclusion
The vagal response produced by the myo- fascial release
procedure in the maximally stimulated sympathetic
environment could only have come from the application of
the OMT. This demonstrates the association between OMT
and the autonomic nervous system. The lack of
significance between control and sham in all positions
indicates that HRV may be a useful method of developing
sham controls in future studies of OMT.
The End