Transcript Medical Statistics

http://cc.jlu.edu.cn/ms.html
Medical Statistics
1
Tao Yuchun
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Preface
Introduction to Medical Statistics
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Statistics
•The science of collecting, analyzing, presenting,
and interpreting data. —(Encyclopaedia Britannica
2009) http://www.britannica.com/
•Branch of mathematics that deals with the
collection, organization, and analysis of numerical
data and with such problems as experiment design
and decision making. —(Microsoft Encarta
Premium 2009)
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•A science dealing with the collection, analysis,
interpretation, and presentation of masses of
numerical data. —(Webster's International Dictionary)
•The science and art of dealing with variation in
data through collection, classification, and analysis
in such a way as to obtain reliable results. —(John
M. Last, A Dictionary of Epidemiology )
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• The science of the collection, organization, and
interpretation of data. It deals with all aspects of
this, including the planning of data collection in
terms of the design of surveys and experiments.
—(From Wikipedia, the free encyclopedia)
http://en.wikipedia.org/wiki/Statistics
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Medical Statistics
• deals with applications of statistics to medicine and
the health sciences, including epidemiology, public
health, forensic medicine, and clinical research.
• Medical Statistics has been a recognized branch of
statistics in the UK for more than 40 years but the
term does not appear to have come into general use
in North America, where the wider term
'biostatistics' is more commonly used.
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Why we need to study Medical Statistics?
Three reasons:
(1) Basic requirement of medical research.
(2) Update your medical knowledge.
(3) Data management and treatment.
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I. Basic concepts
1. Homogeneity and Variation
• Homogeneity: All individuals have similar values
or belong to same category.
Example: all individuals are Chinese, women,
middle age (30~40 years old), work in a computer
factory ---- homogeneity in nationality, gender,
age and occupation.
• Variation: the differences in feature, voice…
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• Throw a coin: The mark face may be up or down
---- variation!
• Treat the patients suffering from pneumonia
with same antibiotics: A part of them recovered
and others didn’t ---- variation!
• If there is no variation, there is no need for
statistics.
• Many examples of variation in medical field:
height, weight, pulse, blood pressure, … …
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2. Population and Sample
• Population: The whole collection of individuals
that one intends to study.
• Sample: A representative part of the population.
• Randomization: An important way to make the
sample representative.
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limited population and limitless population
• All the cases with hepatitis B collected in a
hospital in Changchun. (limited)
• All the deaths found from the permanent
residents in a city. (limited)
• All the rats for testing the toxicity of a medicine.
(limitless)
• All the patients for testing the effect of a
medicine. (limitless) hypertensive, diabetic, …
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Random
By chance!
• Random event: the event may occur or may not
occur in one experiment.
Before one experiment, nobody is sure whether
the event occurs or not.
Example: weather, traffic accident, …
There must be some regulation in a large number
of experiments.
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3. Probability
• Measure the possibility of occurrence of a random
event.
• A : random event
• P(A) : Probability of the random event A
P(A)=1, if an event always occurs.
P(A)=0, if an event never occurs.
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Estimation of Probability----Frequency
• Number of observations: n (large enough)
Number of occurrences of random event A: m
f(A)  m/n
(Frequency or Relative frequency)
Example: Throw a coin event:
n=100, m (Times of the mark face occurred)=46
m/n=46%, this is the frequency; P(A)=1/2=50%,
this is the Probability.
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4. Parameter and Statistic
• Parameter : A measure of population
or
A measure of the distribution of population.
Parameter is usually presented by Greek letter.
such as μ,π,σ.
-- Parameters are unknown usually
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--To know the parameter of a population, we need
a sample
• Statistic: A measure of sample
or
A measure of the distribution of sample.
Statistic is usually presented by Latin letter
such as s , p, t.
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5. Sampling Error
error :The difference between observed value and
true value.
Three kinds of error:
(1) Systematic error (fixed)
(2) Measurement error (random) (Observational error)
(3) Sampling error (random)
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Sampling error
• The statistics of different samples from same
population: different each other!
• The statistics: different from the parameter!
The sampling error exists in any sampling
research.
It can not be avoided but may be estimated.
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II. Types of data
1. Numerical Data ( Quantitative Data )
• The variable describe the characteristic of
individuals quantitatively
-- Numerical Data
• The data of numerical variable
-- Quantitative Data
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2. Categorical Data ( Enumeration Data )
• The variable describe the category of individuals
according to a characteristic of individuals
-- Categorical Data
• The number of individuals in each category
-- Enumeration Data
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Special case of categorical data :
Ordinal Data ( rank data )
•
There exists order among all possible categories. ( level
of measurement)
-- Ordinal Data
•
The data of ordinal variable, which represent the order
of individuals only
-- Rank data
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Examples
Which type of data they belong to?
• RBC (4.58 106/mcL)
• Diastolic/systolic blood pressure
(8/12 kPa) or ( 80/100 mmHg)
• Percentage of individuals with blood type A
(20%) (A, B, AB, O)
• Protein in urine (++) (－, ±, +, ++, +++)
• Incidence rate of breast cancer ( 35/100,000)
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III. The Basic Steps of Statistical Work
1. Design of study
• Professional design:
Research aim
Subjects,
Measures, etc.
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• Statistical design:
Sampling or allocation method,
Sample size,
Randomization,
Data processing, etc.
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2. Collection of data
• Source of data
Government report system such as: cholera,
plague (black death) …
Registration system such as: birth/death
certificate …
Routine records such as: patient case report …
Ad hoc survey such as: influenza A (H1N1) …
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• Data collection – Accuracy, complete,
in time
Protocol: Place, subjects, timing; training;
pilot; questionnaire; instruments; sampling
method and sample size; budget…
Procedure: observation, interview, filling
form, letter, telephone, web.
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3. Data Sorting
• Checking
Hand, computer software
• Amend
• Missing data?
• Grouping
According to categorical variables (sex, occupation,
disease…)
According to numerical variables (age, income, blood
pressure …)
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4. Data Analysis
• Descriptive statistics (show the sample)
mean, incidence rate …
-- Table and plot
• Inferential statistics (towards the
population)
-- Estimation
-- Hypothesis testing (comparison)
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About Teaching and Learning
• Aim:
Training statistical thinking
Skill of dealing with medical data.
• Emphasize:
Essential concepts and statistical thinking
-- lectures and practice session
Skill of computer and statistical software
-- practice session
( Excel and SPSS )
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• Practice session
--Experiments and Discussion ( in classroom
or in Computer-room )
(http://en.wikipedia.org/wiki/Potala_Palace)
C
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