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Introduction to Medical statistics

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Ahmed Elgebaly

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Transcript of Introduction to Medical statistics

Medical Statistics
Applications of
statistics
to
medicine
and the health sciences, including
epidemiology, public health, forensic medicine, and clinical research
.
Why we need to know !
Types of Data
(results of observations)
Role of Researcher
Prepare raw data (master table)
Prepare statistical requests
Evaluate the statistical methods used
variability of living creatures !
The Research Process: an Eight-steps Model
Phase I: deciding what to research
Step I: formulating a research problem
( FINER )
Phase II: planning a research study
Step II: conceptualizing a research design
Step III: constructing an instrument for data collection
Step IV: selecting a sample
Step V: writing a research proposal
Phase III: conducting a research study
Step VI: collecting data
Step VII: processing and displaying data
Step VIII: writing a research report
Age
Temperature
Blood Pressure
Quantitative data
Depends on measuring the quantities
Numerically represented (age, length, weight, . . . .etc)
Have clearly and uniformly defined units
Continues VS. Discrete
Race
Stages of CRC
Being a Hypertensive
Qualitative data
Depends on describing the characteristics (sex, race, occupation, severity, different types of scores)
Nominal
Having a disease or not
Sex
Martial status
0 or 1
Ordinal Data
Stage of CRC
Rating Scale
The first, third and fifth person in a race

Quantitative data
Age = 37.5 Y
Temperature = 38.1 C
BMI= 35.3 kg/m2
Qualitative data
Temperature = Fever
BMI = obese
Transformation of Data
Measures of central tendency & Dispersion
Quantitative data
"Normally distributed"
Arithmetic mean (Mean)
sum of values (x) divided by the number of observations (n)
Normally distributed data
Normally distributed data
"Truths about the general nature of reality,"
Dispersion from the mean
SD
The more the SD the more the variation and vice versa
More sensitive than the range (affected by every value)
There is no range for SD
The value of SD has nothing to do with the goodness of data
Range is NOT the mean ± SD
When the SD is > ½ mean Data are mostly not normal
Extension to the qualitative variable !
Proportion
Frequency Table
SEM
(m) is just is an estimation of this unknown “M”
how much the former is a good representative of the latter ?
Sample size ?
The standard error of mean (SEM) = SD / sqr N
0
1
2
5
6
25
25
29
32
41
94
Mean = 26
0
24
24
25
25
25
25
26
28
28
30
Mean = 26
Standard Deviation (SD)
A measure of how spread out numbers are.
Square root of the Variance.
Variance
The average of the squared differences from the Mean.
Why Squared?
Degree of freedom
Quantitative data
"Non-normally distributed "
Median
Middle value of a ranked array

Suitable for not normal data
Non-Normal Data
Dispersion
0
1
2
5
6
25
25
29
32
41
94
0
24
24
25
25
25
25
26
28
28
30
Median = 25
Median = 25
Centiles
Quartiles
Deciles
Percentiles
Suitable with median
Quartiles
Inter-quartile range: (IQR)
It represents the range including the 2nd & 3rd quartiles
It represents the middle ½ of data
Visualization of data
Qualitative (Categorical) data
Continues quantitative data
Practice
Compared with
normotensive
and
prehypertensive

Hispanic
women, hypertensive participants were older and had less than a
high school education
(Table 1).

They also had a higher number of cardiovascular risk factors, such as
family history of diabetes, stroke, and/or myocardial infarction, treated hypercholesterolemia, treated diabetes, and history of CVD
.

Hypertensive Hispanic women had a higher BMI
(30.3±6.0kg/m2)
than prehypertensive
(29.0±5.4kg/m2)
and normotensive participants
(27.3±5.4kg/m2)
.

Finally, hypertensive Hispanic women
were not currently smokin
g,
did not engage in moderate to strenuous activity
, and were more likely
to be a nondrinker/past drinker.
Statistical inference
95% Confidence Interval
1. Confidence interval
Measures of disease frequency
Incidence
A rate
Time in denominator
Over Time ( Cohort or RCT )
Prevalence
Looked at one time
Cross-sectional
Frequency
Occurrence of a repeating event per unit time.
Cumulative Risk
A proportion
Absolute & Relative Risk
Absolute risk difference
The difference between the observed risks (proportions of individuals with the outcome of interest) in the two groups
Relative Risk
Risk Ratio
The ratio of the risk of an event in the two groups
Odd Ratio
The ratio of the odds of an event
Practice
If 40% of a treated group has a positive response versus just 10% of the placebo group, what are the
risk ratio and odds ratio
(for treatment vs. placebo)?
In a study that enrolled 1000 women for 0.5 years, 10 women developed breast cancer. What was the
incidence rate for breast cancer
In a study that enrolled 1000 women for 0.5 years, 10 women developed breast cancer. What was the
cumulative risk of breast cancer
What measure of disease frequency can be calculated from
cross-sectional studies
?
What measure of disease frequency can be calculated from
case-control studies?
Biology is not physics !
We deal with ?
Fixed Events !
Random events,
Probability !
Statistics is "internal" to medicine !
Diagnosis, prognosis and treatment

Statistical analysis neither makes medical judgment nor prescribes

Planning for services and carrying out clinical trials
Can not be quantified
Summarizing Data
Global picture
Analyze results
using statistical tests
To take
“decisions”
To
communicate
with each other and with our patients.
Confidence interval
When it comes to reality ! Normal distribution !
ؤCI of subject, Use SD
CI of mean, Use SEM
p-value
Introduction to Medical Statistics
Ahmed Elgebaly
How to choose the right statistical test?
Medical Statistics
"The Art of Prediction"
We muddle through life
making choices
based on
incomplete information

quantifying uncertainty!
MBBCH student, Faculty of Medicine, Al-Azhar University

RSDD of Medical Research Society "MRS"

Senior Researcher at MRGE

International peer-reviewed articles and book chapters

Email: Ahmedelgebaly94@gmail.com
Statistics
Descriptive Statistics
Our aim is to get
simple measurements
from the
crude set of data

Inferential Statistics
The science of drawing
statistical conclusions
from
specific data.
1. Descriptive Statistics
Any summary measurements has
the central
and

Normal Distribution
"Truths about the general nature of reality"
When data tends to be
around a central value
with no bias left or right
The
"Bell Curve"
Many things closely follow a Normal Distribution "Blood pressure, Height, ..etc"
When
1- mean = median = mode
3- 50% of values less than the mean and 50% greater than the mean
The science of drawing
statistical conclusions
from specific data.

FACTORS CONTROL THIS ERROR

VARIATION
WITHIN THE SAMPLE “SD”

Sample size

Sampling Error
"Standard Error"
Is it Right to said “The mean for population equals ……?"
“The mean for population lies between….”

We are just
sampling
so we have a
margin of error
In Medical Research, we are dealing with
Inductive
not Deductive reasoning!
Our goal is to
capture the true effect
(because we are just sampling).
Our
sample is the best estimate
but we would always have
a margin of error.
Confidence Interval
"We are pretty sure that our population
lies within this range
"
Estimating confidence interval is the one of the most effective way in
statistical inference.

Confidence interval is to try to include
the true mean
within a range
(because I cannot give you a single estimate and say this is the true effect
).

A
95% CI
should include the
true effect
95% of the time
How to improve your confidence level?
95% CI = Mean +/- 2 SE
2. Hypothesis Testing
Could these observations have occurred by chance?
Types of Hypothesis
The Null hypothesis
is that the observations are purely due to
chance

Alternative hypothesis
is that the observations are due to
real effect

P-Value
If the null hypothesis is true; what is the
probability of observing
such
extreme results

The Probability
that the
Null hypothesis is true
P-Value
The
smaller
the p-value, the stronger
the evidence against the null

A significant of level less than 0.05
Learning Objectives
Bio-medical Statistics: What & Why?
Descriptive Statistics
Statistical Inference
Learning Objectives
Bio-medical Statistics: What & Why?
Descriptive Statistics
Statistical Inference
Learning Objectives
Bio-medical Statistics: What & Why?
Descriptive Statistics
Statistical Inference
References
1. Harris, Michael and Gordon Taylor. Medical Statistics Made Easy. Print.

2. Gonick, Larry and Woollcott Smith. The Cartoon Guide To Statistics. Print.

3. "Introduction - Handbook Of Biological Statistics". Biostathandbook.com. N.p., 2016. Web. 31 Aug. 2016.
Everything we know is only some kind of
approximation
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