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Basics of Biostatistics for Health Research Session 2 – February 14 th, 2013 Dr. Scott Patten, Professor of Epidemiology Department of Community Health.

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Presentation on theme: "Basics of Biostatistics for Health Research Session 2 – February 14 th, 2013 Dr. Scott Patten, Professor of Epidemiology Department of Community Health."— Presentation transcript:

1 Basics of Biostatistics for Health Research Session 2 – February 14 th, 2013 Dr. Scott Patten, Professor of Epidemiology Department of Community Health Sciences & Department of Psychiatry

2 Go to “www.ucalgary.ca/~patten” Scroll to the bottom. Right click to download the files described as being “for PGME Students” –One is a dataset –One is a data dictionary Save them on your desktop

3 Open the Datafile

4 The task from last week… Create a 95% exact binomial confidence interval for the proportion of people with Framingham with > H.S. education

5 Review of Last Week’s Task “use” “generate” “recode” “tabulate” “ci”

6 The actual commands… generate highschool = educ recode highschool 1/2=0 3/4=1 tabulate highschool ci highschool, binomial

7 Creating a “do” file… 1 2 3

8 The “do file” editor

9 Executing a “do” file

10 What is a “do” file? It is a text file – you can copy and paste from the output window in Stata, or from a word processor It is a computer program that consists of actual commands and therefore doesn’t need a compiler Others would call it a “macro”

11 Different Types of Data One type of distinction –Nominal (e.g. sex, race) –Ordinal (e.g. rating scales) –Cardinal (e.g. physical measures) Another type of distinction –Categorical (e.g. # of pregnancies) –Continuous (e.g. height, weight)

12 Body Mass Index (BMI)

13 The BMI in our Data Set This is an example of a continuous variable

14 Changing Data Types in Stata (e.g. continuous to categorical) recode bmi x/y=z This will recode all values of the variable bmi having values from x to y to a single value equal to z.

15 Interpretation of BMI Underweight: < 18.5 Normal weight: 18.5 to 25 Over weight: >25 to 30 Obese: 30+ Your task: Make a “do file” that calculates a 95% confidence interval for the proportion of the population that are overweight or obese.

16 Example of Code for this… generate owo = bmi recode owo 0/25 = /100 = 1 tab owo, missing ci owo, binomial

17 Another Task… Add a use command to your do file Save your “do file” on the desktop using a descriptive file name of your choice Exit Stata Open Stata again Open the “do file” editor and select your do file Execute your “do file”

18 The Power of “do files” Task: Calculate an exact 95% CI for the proportion of the population that are obese (BMI > 30) IMPORTANT: do NOT start from scratch as we did before – try to do this by editing your do file.

19 generate owo = bmi recode owo 0/25 = /100 = 1 tab owo, missing ci owo, binomial generate owo = bmi generate obese = bmi recode owo 0/25 = /100 = 1 recode obese 0/30 = /100=1 tab owo, missing tab obese, missing ci owo obese, binomial For Example…

20 Starting a Log File 1 23

21 Closing a Log File 1 2 3

22 Another Task… Start a log file Run your “do file” Close and save the resulting log file on your desktop Open your log file

23 “do file” Etiquette When you add an * before a line on a “do file” Stata will ignore that line Use this to…. –Add descriptive comments to your code –Remove commands that you don’t want now, but might want later

24 E.g. Without the Tables…

25 Review… Make a value label for obesity Attach this value label to the variable representing obesity

26 Making a Graphic

27 The Pie Chart Dialogue Box Find the Variable that you made 1 2

28 Unedited Output

29 The Graph Editor

30 Here is a good place to start

31 See if you can do these things… Change the color of the pie Add a title Add a comment Change the background Create a work of art

32 Save in a Standard Format

33 Back to BMI May not wish to categorize variables like this Measures of central tendency –Mode –Median –Mean Different types of graphs are useful for examining continuous variables –Box plots –Histograms

34 Box Plots

35 Terminology Median: value with 50% of observations above and 50% below. Interquartile range – contains 50% of observations – plus or minus one quartile Adjacent values (whiskers) – observation that is less than 1.5x the IQR Outliers: anything outside of the adjacent values

36 Calculating Summary Stats Calculate summary stats for BMI

37 Calculating Summary Stats Calculate the mean BMI

38 Calculating Summary Stats Calculate median BMI

39 Make a Box (and whisker) Plot

40 The Boxplot Dialogue Box 1 2 Select BMI from the dropdown list

41 Introducing Histograms 1 2

42 The Histogram Dialogue Box Select the variable here Select the bin# here

43 A Task for You to Do… Make 3 histograms of BMI –In one use the default number of bins –In one use a larger number –In one, use a smaller number Save your favorite histogram Open it in the graph editor, give it a title and improve its appearance Save it in a standard form (e.g. png, jpg, tif)

44 Assessing Normality with a Histogram

45 The distribution is not quite normal, but close

46 Is BMI Higher in Men or Women? We could use confidence intervals to assess this… E.g

47 Here is the dialogue box… Once you’ve selected BMI, click this

48 The dialogue box, continued.. Enter sex as a group variable

49 The output

50 It looks better with value labels

51 Statistical Tests Start with an hypothesis that an “effect” exists –In this case, that there is an effect of sex on BMI Assume that the effect DOES NOT exist –This is the null hypothesis Find the probability of results, or those more extreme given the null hypothesis –This is what the “test” calculates for you If the null is unlikely (alpha value), reject it

52 The t-test (assumptions) The variables are approximately normally distributed The standard deviations of the two groups are approximately equal The two samples are independent

53 Using summarize similarly Use summarize with “by” in the dialogue box Use histograms with a normal density plot and the “by” tab in the dialogue box Your task: use these two techniques to assess the t-test assumptions.

54 Variance Comparisons 1 2 3

55 The t-test 1 2 3

56 The t-test dialogue box optional

57 The output

58 Two group tests for proportions

59 You can also do this with tab tab obese sex, exact

60 Your Final Task for Today Create a “do file” that … –Reads in the data –Recodes BMI to a categorical variable for obesity –Tests whether obesity differs between men and women Create a log file to store the results


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