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J.D. Bramble, Ph.D. Creighton University Medical Center Med 483 -- Fall 2006 Introduction to Statistics Do I have to??

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Presentation on theme: "J.D. Bramble, Ph.D. Creighton University Medical Center Med 483 -- Fall 2006 Introduction to Statistics Do I have to??"— Presentation transcript:

1 J.D. Bramble, Ph.D. Creighton University Medical Center Med Fall 2006 Introduction to Statistics Do I have to??

2 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Why we “do it” "What we really want to get at [in health care research] is not how many reports have been done, but how many people's lives are being bettered by what has been accomplished. In other words, is it being used, is it being followed, is it actually being given to patients—... What effect is it having on people—" Rep. John Porter (R-IL), retired chairman House Appropriations Subcommittee on Labor, Health and Human Services (HHS), and Education

3 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Is Statistics Important? Statistics is important because we can use it to find out whether something we observe can be applied to new and different situations. Knowing this allows us to plan for the future, and to make decisions about how to allocate our scarce resources of money, energy, and ultimately life. We use the term generalizable: can what we know help to predict what will happen in new and different situations?

4 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Why Statistics Scientific knowledge represents the best understanding that has been produced by means of current evidence. Research design, if used properly, strengthens the objectivity of the research. Statistical methods allow us to compare what is actually observed to what is logically expected.

5 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Why Statistics (cont’d) Knowledge of statistics... Useful in conducting investigations Helpful the preparing and evaluating research proposals. Vital in deciding whether claims of a researcher are valid Keep abreast of current developments. Effective presentations of the findings.

6 J.D. Bramble, Ph.D. MED 483 – Fall 2006

7 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Evils of Pickle Eating Pickles are associated with all the major diseases of the body. Eating them breeds war and Communism. They can be related to most airline tragedies. Auto accidents are caused by pickles. There exists a positive relationship between crime waves and consumption of this fruit of the cucurbit family. For example

8 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Evils of Pickle Eating (cont’d) Nearly all sick people have eaten pickles. 99.9% of all people who die from cancer have eaten pickles. 100% of all soldiers have eaten pickles. 96.8% of all Communist sympathizers have eaten pickles. 99.7% of the people involved in air and auto accidents ate pickles within 14 days preceding the accident. 93.1% of juvenile delinquents come from homes where pickles are served frequently. Evidence points to the long- term effects of pickle eating. Of the people born in 1839 who later dined on pickles, there has been a 100% mortality.

9 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Evils of Pickle Eating (cont’d) All pickle eaters born between 1849 and 1859 have wrinkled skin, have lost most of their teeth, have brittle bones and failing eyesight-if the ills of pickle eating have not already caused their death. Even more convincing is the report of a noted team of medical spe­cialists: rats force-fed with 20 pounds of pickles per day for 30 days de­veloped bulging abdomens. Their appetites for WHOLESOME FOOD were destroyed.

10 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Evils of Pickle Eating (cont’d) In spite of all the evidence, pickle growers and packers continue to spread their evil. More than 120,000 acres of fertile U.S. soil are devoted to growing pickles. Our per capita consumption is nearly four pounds. Eat orchid petal soup. Practically no one has as many problems from eating orchid petal soup as they do with eating pickles. EVERETT D. EDINGTON

11 J.D. Bramble, Ph.D. MED 483 – Fall 2006

12 J.D. Bramble, Ph.D. MED 483 – Fall 2006

13 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Types of Statistics Descriptive Statistics enumerate, organize, summarize, and categorize graphical representation of data. these type of statistics describes the data. Examples means and frequency of outcomes charts and graphs

14 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Inferential Statistics drawing conclusions from incomplete information. they make predictions about a larger population given a smaller sample these are thought of as the statistical test Examples t-test, chi square test, ANOVA, regression Types of Statistics

15 J.D. Bramble, Ph.D. MED 483 – Fall 2006

16 J.D. Bramble, Ph.D. Creighton University Medical Center Med Fall 2006 Variables J.D. Bramble, Ph.D. Creighton University Medical Center Med Fall 2006

17 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Types of Data Qualitative data fall into separate classes with no numerical relationship sex, mortality, correct/incorrect, etc. Quantitative numerical data that is continuous pharmaceutical costs, LOS, etc.

18 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Parameters and Statistics Parameters characteristics of the population calculating the exact population parameter is often impractical or impossible Statistics characteristics of the sample represent summary measures of observed values

19 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Types of Variables Variables are symbols to which numerals or values are assigned e.g. X and Y are variables Dependent (Y’s), that which is predicted Independent (X’s), that which predicts Extraneous (Confounding or Control) statistical models “adjust” for their influence

20 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Independent variables Independent variables are the presumed cause of the the dependent variable The variable responsible for the change in the phenomena being observed Nothing is for sure, so avoid the word ‘cause’ and think in terms of independent and dependent variables

21 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Dependent variables Also referred to as the outcome variable The outcome of the changes due to the independent variables Example:y = a + bx

22 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Confounding variables Additional variables that may effect the changes in the dependent variable attributed to the independent variables. These variables are controlled by measuring them and statistical methods adjust for there influence. Sometimes referred to as control variables

23 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Active vs. attribute variables Active variables are those variables under the control of the researcher controlled experimental studies e.g., amount of drug administered Attribute variables can not be manipulated by the researcher quasi-experimental studies e.g.,sex or age of subject; blood pressure; smoker

24 J.D. Bramble, Ph.D. MED 483 – Fall 2006 The Wrong data Leads to Migraines

25 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Levels of Measurement Categorical Variables Nominal Scale Ordinal Scale Continuous Variables Interval Scale Ratio Scale

26 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Continuous Variables Continuous variables are measured and can take on any value along the scale quantitative variables measured on a interval or ratio level Examples Age, income, number of medications

27 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Categorical Variables Categorical variables are measured as dichotomous or polytomous measures qualitative variables measured on a nominal or ordinal level Examples sex; smoking status; ownership Categorizing continuous variables

28 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Nominal measurement scale Used for qualitative data Two or more levels of measurement The name of the groups does not matter Examples Sex (Male/Female) Smoker (Yes/No) Political Party (Rep, Dem, Ind)

29 J.D. Bramble, Ph.D. MED 483 – Fall 2006 All the properties of nominal plus... The groups are ordered or ranked Intervals between groups are not necessarily equal Example: Income (low, med, high) Disease severity Likert scales Ordinal measurement scale

30 J.D. Bramble, Ph.D. MED 483 – Fall 2006 All properties of nominal and ordinal plus... A scale is used to measure the response of the study subjects The intervals scale’s units are equal; however arbitrary (e.g., a relative scale) Examples: Temperature on Fahrenheit scale Interval measurement scale

31 J.D. Bramble, Ph.D. MED 483 – Fall 2006 All properties of the previous scales plus... An absolute zero point Can perform mathematical operations Highest level of measurement Examples Income, age, height, weight Ratio measurement scale

32 J.D. Bramble, Ph.D. Creighton University Medical Center Med Fall 2006 Summarizing Data Measures of Central Tendency and Variation

33 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Mean Arithmetic mean the balance point sum all observations sum all observations divide the sum by the number of observations

34 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Median Divides the distribution into two equal parts. Considered the most “typical” observation Less sensitive to extreme values

35 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Calculating Medians To find the median value: q(n+1) 41, 28, 34, 36, 26, 44, 39, 32, 40, 35, 36, 33 order data in ascending order 26, 28, 32, 33, 34, 35, 36, 36, 39, 40, 41, 44 Apply the median location formula: 0.5(12+1) = 6.5 Note: this is ONLY the location of the median

36 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Quantiles Quantiles are those values that divide the distribution into n equal parts so that there is a given proportion of data below each quantile. The median is the middle quantile. Quartiles are also very common (25, 50, 75) If we divided the distribution into 100 then we have percentiles.

37 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Mode The observation that occurs most frequently Graphically it is the value of the peak of the distribution. Frequency often may be bimodal--two modes. If values are all the same--no mode exists

38 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Single Modal

39 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Bimodal Example

40 J.D. Bramble, Ph.D. MED 483 – Fall 2006 mean median mode Symmetrical: The relationship between the Mean, Median, & Mode

41 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Mode Median Mean Positive Skew: The relationship between the Mean, Median, & Mode

42 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Mode Median Mean Negative Skew: The relationship Between the Mean, Median, & Mode

43 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Summarizing Data Frequency distributions Measures of central tendency The tendency of data to center around certain numerical and ordinal values. Three common measures: mean, median, & mode Measures of variation standard deviation

44 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Five Figure Summary Median Quartiles Maximum Minimum Can be shown in a box and whisker plot

45 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Which Measure? Mean numerical data symmetric distribution Median ordinal data skewed distribution Mode bimodal distribution most popular

46 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Variation Must also report measures of variation Measures of variability reflect the degree to which data differ from one another as well as the mean. Together the mean and variability help describe the characteristics of the data and shows how the distributions vary from one another.

47 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Example of Variation Take the following three sets of data: 1) 10, 8, 5, 5, 2; 2) 5, 6, 6, 7, 6; 3) 6, 6, 6, 6, 6 In all three cases the mean is 6, the variability is a lot of variability in set 1 No variability in set 3. We will discuss three measures of variability: 1) the range; 2) the standard deviation; and 3) variance

48 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Measures of Variation Range the value between the highest and the lowest observations Range = x max - x min limited usefulness since it only accounts for the extreme values can also report the inter-quartile range (q 3 – q 1 )

49 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Standard Deviation most widely used & preferred measure of variation. represented by the symbol s or sd the square root of the variance (s 2 ) larger values = more heterogeneous distribution 75% of the observations lie between x-2s and x+2s if the distribution is normal (bell shaped) 67% = 95% = 99.7% =

50 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Variance and Std Deviation Variance Standard Deviation

51 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Example Using data on the sexual activity of male and female subjects can be found in Chatterjee, Handcock, and Simonoff (1995) A casebook for a first course in statistics. New York: Wiley. They provide data on the reported number of sexual partners for 1682 females and 1850 males. The dependent variable is the number of reported partners.

52 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Descriptive Statistics MaleFemale (n=1850)(n=1685) Mean Median 4 1 Mode 1 1

53 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Write them right into the spreadsheet Be sure to start with an equal sign Use your mouse to highlight data to analyze Using Excel When Syntax in Known

54 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Using Excel When Syntax in Unknown Use the wizard and follow in instructions. All wizards work about the same way. Select the fx button to select appropriate test Select category and then desired test

55 J.D. Bramble, Ph.D. MED 483 – Fall 2006 Follow the Wizard Either highlight the array or just write it in These icons reduce/enlarge the Wizard box


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