2014.3.31

Medical StatisticsMedical Statistics

Tao YuchunTao Yuchun

11

http://cc.jlu.edu.cn/ms.html

2014.3.32

PrefacePreface

Introduction to Medical Statistics

2014.3.33

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)

2014.3.34

•A science dealing with the collection, analysis, interpretation, and presentation of masses ofnumerical 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 )

2014.3.35

• 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

2014.3.36

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.

 

2014.3.37

 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.

2014.3.38

I.I. Basic conceptsBasic concepts

• Homogeneity: All individuals have similar values or belong to same category.

ExampleExample: 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…

1. Homogeneity and Variation

2014.3.39

• 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, … …

2014.3.310

• 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.

2. Population and Sample

2014.3.311

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, …

2014.3.312

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.

ExampleExample: weather, traffic accident, …

There must be some regulation in a large number of experiments.

2014.3.313

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.

2014.3.314

• Number of observations: n (large enough)

Number of occurrences of random event A: m

f(A) m/n

(Frequency or Relative frequency)

ExampleExample: 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.

Estimation of Probability----Frequency

2014.3.315

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

2014.3.316

--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.

2014.3.317

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)

2014.3.318

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.

2014.3.319

II. Types of dataII. 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

2014.3.320

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

2014.3.321

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

2014.3.322

ExamplesExamples

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)

2014.3.323

III. The Basic Steps of Statistical WorkIII. The Basic Steps of Statistical Work

1. Design of study1. Design of study

• Professional design:

Research aim

Subjects,

Measures, etc.

2014.3.324

• Statistical design:

Sampling or allocation method,

Sample size,

Randomization,

Data processing, etc.

2014.3.325

2. Collection of data2. 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) …

2014.3.326

• 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.

2014.3.327

3. 3. Data SortingData Sorting

• Checking

Hand, computer software

• Amend

• Missing data?

• Grouping

According to categorical variables (sex, occupation, disease…)

According to numerical variables (age, income, blood pressure …)

2014.3.328

4. Data Analysis4. Data Analysis

• Descriptive statistics (show the sample)

mean, incidence rate …

-- Table and plot

• Inferential statistics (towards the population)

-- Estimation

-- Hypothesis testing (comparison)

2014.3.329

About Teaching and LearningAbout 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 )

2014.3.330

CC

• Practice session

--Experiments and Discussion ( in classroom

or in Computer-room )

(http://en.wikipedia.org/wiki/Potala_Palace)