医学研究中的统计分析（一）

1. 医学研究中的统计分析（一） Statistical Analysis in medical research

2. Preface • Key Concepts In Statistics • The Common Statistical Methods Of Measurement Data Main Content

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. Statistical Analysis Of Quantitative Data

13. 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

14. 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

15. 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

16. 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

17. Summaries: Descriptive Statistics

18. Graphical presentation • Histograms • Frequency distribution • Box and whiskers plot Descriptive Statistics

19. HistogramContinuous Data No segmentation of data into groups

20. Frequency Distribution Segmentation of data into groups Discrete or continuous data

21. Box And Whiskers Plots

22. Box And Whisker Plots Popular in Epidemiologic Studies Useful for presenting comparative data graphically

23. 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

24. Commonly reported in studies to provide an estimate of the precision of the mean.

25. 3)More than two sample ANOVA completely random design , normality ,homogeneity inferential Statistics

26. Thank You For Your Attention !