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医学研究中的统计分析（一） - PowerPoint PPT Presentation

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医学研究中的统计分析（一）

Statistical Analysis in medical research

Preface

• Key Concepts In Statistics
• The Common Statistical Methods Of Measurement Data

Medical statistics is an science which combines theory of statistics with medical science. It is indispensable for us to conduct medical research.

• The use of statistics allows the researcher to form reasonable and accurate inferences from collected information and to make sound decisions in the presence of uncertainty.
• In the process of thesis writing, we often use statistical method inappropriate, which determines the quality and publishing  of paper，to a certain extent。
Preface

Variables and types of data

• Data---Measurements or observations of a variable
• Variable ---a characteristic that can vary in value among subjects in a sample or a population.
• Types of variables（There are different statistical methods for each type）
• Quantitative variables (Measurement Data):

(a). Continuous variables or interval data

e.g. age , weight , height , BMI

(b). Discrete variables

e.g. the number of patients, newborns

Key Concepts

2. Categoricalvariables (nominal scale、 unordered categories)( Enumeration Data)

Racial-ethnic group (white, black, others)

sex(male or female)

3. Ranked (ordinal) variables （rank data）

Anxiety, stress, (high, medium, low)

Mental impairment (none, mild, moderate, severe)

• Ordering of variable types from highest to lowest level of differentiation among levels:

interval > ordinal > nominal

Key Concepts

Populations and samples

1. Populations :a collection of similar people , observations ,or measurements , in which certain subjects can be sampled to infer a property or attribute of population.

Parameter：Numerical summary of the population. Parameters are unknown usually .

2. Samples : Subjects are selected from a population so that each individual has an equal chance of being selected .

Statistic：Numerical summary of the sample

Key Concepts

3. Simple random sample: In a sample survey, each possible sample of size n has same chance of being selected.

• Random samples are representative of the source population , and can be used to infer the information of population.
• How to implement random sampling:

Use “random number tables” or statistical software that can

generate random numbers.

Key Concepts

Probability(P)

A probability provides a quantitative description of the likely occurrence of a particular event.

Probability is conventionally expressed on a scale from 0 to 1; a rare event has a probability close to 0, a very common event has a probability close to 1.

• P values

The probability of getting a value of the test statistic as extreme as or more extreme than that observed by chance alone , if the null hypothesis is true.

Key Concepts

The P-value is compared to the actual significance level of the test ,and if it is smaller ,the result is statistically significant.

The most widely accepted significance level is 0.05, and the test is said to be “significant at the .05 level” if the P-value ≤ 0.05.

The smaller the P-value, the stronger the evidence against H0 , exact P-value should be reported.

Key Concepts

The Steps of Statistical Work

1. Design of study

• Professional design : Research aim, Subjects, Measures, etc.
• Statistical design : Sampling method, Sample size, Data

processing, etc.

2. Collection of data: Accuracy, complete, in time

3. Data Sorting : Checking , Amend , Missing data ,etc.

Key Concepts

Key Concepts

• Descriptive statistics (show the sample)

--Numerical descriptions

--Table and plot

• Inferential statistics (towards the population)

--Parameter estimation

--Hypothesis test (comparison)

4. DATA ANALYSIS
Choice of statistical method depends on:
• the question raised

1)Statistics used to answer questions concerning differences,

2)Statistics used to answer questions concerning associations,

3)Statistics used to answer questions concerning predictions

• type of data collected

1) Measurement Data

2) Enumeration Data

3) rank data

type of data distribution

1)( approx. )normal distribution

• Data distributions are normal whenever the random sample size is large (at least about 30).

2)skewed distribution (positively or  negatively)

• type of experiment design
• samples size
An important distribution in statistics
• bell-shaped curve
• symmetric about the mean (or median)

0.4

The Normal Distribution

2.5%

increasing probability

2.5%

95%

0

-4

-2

2

4

0

-1.96

1.96

Numeric Description

• Measures of central tendency of data
• Mean （population： μ ，sample： ）（ Symmetrical distribution）
• Median （M） （Skewed , unknown , etc.）
• Geometric Mean （G）
• Measures of variability of data
• Standard Deviation （population：s，sample：s）（ Symmetrical distribution , especially normal distribution）
• Interquartile Range（IQR , Q）（ Skewed , unknown , etc. ）
Descriptive Statistics

Graphical presentation

• Histograms
• Frequency distribution
• Box and whiskers plot
Descriptive Statistics
HistogramContinuous Data

No segmentation of data into groups

Frequency Distribution

Segmentation of data into groups

Discrete or continuous data

Box And Whisker Plots

Popular in Epidemiologic Studies

Useful for presenting comparative data graphically

Parameter estimation

• Confidence Intervals (normal distribution)
• hypothesis test
• 1)One sample one sample t-test (normal distribution)
• 2)Two sample
• paired t-test Paired design (normal distribution,d)
• two- sample t-test for independent
• Independent design ( normality , homogeneity)
inferential Statistics

3)More than two sample

ANOVA

completely random design , normality ,homogeneity

inferential Statistics