SPSS is the 2nd most popular package.

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SPSS is the 2nd most popular package. It is much
easier to use than SAS and Stata.
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Install additional software for statistical odds
and ends

• Instat by GraphPad graphpad.com
• for summary data analysis - 100
• True Epistat by Epistat Services
  true-epistat.com - 395
• for random number table, etc.
• CIA (Confidence Interval Analysis) bmj.com
• for confidence intervals - 35.95 with book
• Statistics with Confidence D. Altman

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Install a sample size program.

• If you can afford to spend 400, buy nQuery
  Advisor statistical solutions - www.statsol.com
  
• If you can afford to spend 0, download PS from
  the Vanderbilt web site
• http//www.mc.vanderbilt.edu/prevmed/ps/index.htm
  
• Both packages are on the CRCs statistical
  workstation in room A-3101. VUMC investigators
  are welcome to use this workstation.

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II. You Will Need a Plan
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Use the scientific method to keep your project
focused.

• State the problem
• Formulate the null hypothesis
• Design the study
• Collect the data
• Interpret the data
• Draw conclusions

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State the Problem

• Among patients hospitalized for a hip fracture
  who develop pneumonia during their stay in the
  hospital, the mortality rate is 2.3 times higher
  at non-trauma centers compared with trauma
  centers
• (48.7 vs. 21.1, P0.043.)
• It is not clear if, or how, those who will
  develop pneumonia could be identified on
  admission.

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Formulate the Null Hypothesis

• Among patients hospitalized for treatment of a
  hip fracture, there are no factors known upon
  admission that are statistically different
  between those who develop pneumonia during their
  stay and those who do not.

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Why bother with a null hypothesis?

• For the same reason that we assume that a person
  is innocent until proven guilty.
• The burden of responsibility is on the prosecutor
  to demonstrate enough evidence for members of a
  jury to be convinced of that the charges are true
  and to change their minds.
• Outcome after treatment with Drug A will not be
  significantly different from placebo.

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Design the Study

• Data on 933 patients with a hip fracture from a
  New York trauma registry will be analyzed.
• The 58 patients with pneumonia will be compared
  with the 875 without pneumonia.

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The Most Common Type of Flaw
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Example of Recall Bias

• A control group is asked,
• Two weeks ago from today, did you eat X for
  breakfast?
• Two weeks after their MI, patients are asked
• Did you eat X for breakfast on the day of your
  heart attack?
• You can prove any food causes an MI using this
  method (Xbacon, XFlintstone vitamins, etc.)

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John Bailars Quote

• Study design and bias are much more important
  than complex statistical methods.
• Devote more time to improving the study design,
  and minimizing and measuring bias.
• Become an expert at study design issues and
  biases in your area of research.

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What is the statistical power of the study?

• Power
• Beta
• Alpha
• Sample size
• Ratio of treated to control group
• Measure of outcome

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Sample Size Table

• See Table 9-1 in the handout
• Sample Size Requirements for Each of Two Groups.

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Collect the Data

• See the handouts for
• I TEC Trauma Systems Study

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III. You Will Need Data Management Skills
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Enter your data with statistical analysis in mind.

• For small projects enter data into Microsoft
  Excel or directly into SPSS.
• For large projects, create a database with
  Microsoft Access.
• Keep variables names in the first row, with lt8
  characters, and no internal spaces.
• Enter as little text as possible and use codes
  for categories, such as 1male, 2female.

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Spreadsheet from Hell
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Spreadsheet from Heaven
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IV. You Will Need to Learn Descriptive Statistics
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Descriptive vs. Inferential

• Descriptive statistics summarize your group.
• average age 78.5, 89.3 white.
• Inferential statistics use the theory of
  probability to make inferences about larger
  populations from your sample.
• White patients were significantly older than
  black and Hispanic patients, Plt0.001.

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Import your data into a statistical program for
screening and analysis.
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Screen your data thoroughly for errors and
inconsistencies before doing ANY analyses.

• Check the lowest and highest value for each
  variable.
• For example, age 1-777.
• Look at histograms to detect typos.
• Cross-check variables to detect impossible
  combinations.
• For example, pregnant males, survivors discharged
  to the morgue, patients in the ICU for 25 days
  with no complications.

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Analyze, descriptive statistics, frequencies,
select the variable
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Analyze, Descriptive Statistics, Crosstabs
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Correct the data in the original database or
spreadsheet and import a revised version into the
statistical package.

• The age of 777 should be checked and changed to
  the correct age.
• Suspicious values, such as an age of 106 should
  be checked. In this case it is correct.

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Interpret the Data
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Run descriptive statistics to summarize your data.
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V. You Will Need to Learn Inferential Statistics
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P Value

• A P value is an estimate of the probability of
  results such as yours could have occurred by
  chance alone if there truly was no difference or
  association.
• P lt 0.05 5 chance, 1 in 20.
• P lt0.01 1 chance, 1 in 100.
• Alpha is the threshold. If P is lt this
  threshold, you consider it statistically
  significant.

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Basic formula for inferential tests

• Based on the total number of observations and the
  size of the test statistic, one can determine the
  P value.

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How many noise units?

• Test statistic sample size (degrees of freedom)
  convert to a probability or P Value.

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Use inference statistics to test for differences
and associations.

• There are hundreds of statistical tests.
• A clinical researcher does not need to know them
  all.
• Learn how to perform the most common tests on
  SPSS.
• Learn how to use the statistical flowchart to
  determine which test to use.

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VI. You Will Need to Understand the Statistical
Terminology Required to Select the Proper
Inferential Test
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Univariate vs. Multivariate

• Univariate analysis usually refers to one
  predictor variable and one outcome variable
• Is gender a predictor of pneumonia?
• Multivariate analysis usually refers to more than
  one predictor variable or more than one outcome
  variable being evaluated simultaneously.
• After adjusting for age, is gender a predictor of
  pneumonia?

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Difference vs. Association

• Some tests are designed to assess whether there
  are statistically significant differences between
  groups.
• Is there a statistically significant difference
  between the age of patients with and without
  pneumonia?
• Some tests are designed to assess whether there
  are statistically significant associations
  between variables.
• Is the age of the patient associated with the
  number of days in the hospital?

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Unmatched vs. Matched

• Some statistical tests are designed to assess
  groups that are unmatched or independent.
• Is the admission systolic blood pressure
  different between men and women?
• Some statistical tests are designed to assess
  groups that are matched or data that are paired.
• Is the systolic blood pressure different between
  admission and discharge?

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Level of Measurement

• Categorical vs. continuous variables
• If you take the average of a continuous variable,
  it has meaning.
• Average age, blood pressure, days in the
  hospital.
• If you take the average of a categorical
  variable, it has no meaning.
• Average gender, race, smoker.

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Level of Measurement

• Nominal - categorical
• gender, race, hypertensive
• Ordinal - categories that can be ranked
• none, light, moderate, heavy smoker
• Interval - continuous
• blood pressure, age, days in the hospital

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Horse race example

• Nominal
• Did this horse come in first place?
• 0no, 1yes
• Ordinal
• In what position did this horse finish?
• 1first, 2second, 3third, etc.
• Interval (scale)
• How long did it take for this horse to finish?
• 60 seconds, etc.

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Normal vs. Skewed Distributions

• Parametric statistical test can be used to assess
  variables that have a normal or symmetrical
  bell-shaped distribution curve for a histogram.
• Nonparamettric statistical test can be used to
  assess variables that are skewed or nonnormal.
• Look at a histogram to decide.

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Examples of Normal and Skewed
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VII. You Will Need to Know Which Statistical
Test to Use
46
Commonly used statistical methods

• 1. Chi-square
• 2. Logistic regression
• 3. Student's t-test
• 4. Fisher's exact test
• 5. Cox proportional-hazards
• 6. Kaplan-Meier method
• 7. Wilcoxon rank-sum test
• 8. Log-rank test
• 9. Linear regression analysis
• 10. Mantel-Haenszel method

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Commonly used statistical methods

• 11. One-way analysis of variance (ANOVA)
• 12. Mann-Whitney U test
• 13. Kruskal-Wallis test
• 14. Repeated-measures analysis of variance
• 15. Paired t-test
• 16. Chi-square test for trend
• 17. Wilcoxon signed-rank test
• 18. Analysis of variance (two-way)
• 19. Spearman rank-order correlation
• 20. Analysis of covariance (ANCOVA)

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Chi-square

• The most commonly used statistical test.
• Used to test if two or more percentages are
  different.
• For example, suppose that in a study of 933
  patients with a hip fracture, 10 of the men
  (22/219) of the men develop pneumonia compared
  with 5 of the women (36/714).
• What is the probability that this could happen by
  chance alone?
• Univariate, difference, unmatched, nominal, gt2
  groups, ngt20.

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Chi-square example
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Fishers Exact Test

• This test can be used for 2 by 2 tables when the
  number of cases is too small to satisfy the
  assumptions of the chi-square.
• Total number of cases is lt20 or
• The expected number of cases in any cell is lt1
  or
• More than 25 of the cells have expected
  frequencies lt5.

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How to calculate the expected number in a cell
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Chi-square for a trend test

• Used to assess a nominal variable and an ordinal
  variable.
• Does the pneumonia rate increase with the total
  number of comorbidities?
• Univariate, association, nominal.
• Analyze, Descriptive Statistics, Crosstabs.

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Mantel-Haenszel Method

• Used to assess a factor across a number of 2 by 2
  tables.
• Is the mortality rate associated with pneumonia
  different between trauma centers and nontrauma
  centers?
• Analyze, Descriptive Statistics, Crosstabs.

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Students t-test

• Used to compare the average (mean) in one group
  with the average in another group.
• Is the average age of patients significantly
  different between those who developed pneumonia
  and those who did not?
• Univariate, Difference, Unmatched, Interval,
  Normal, 2 groups.

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Mann-Whitney U test

• Same as the Wilcoxon rank-sum test
• Used in place of the Students t-test when the
  data are skewed.
• A nonparametric test that uses the rank of the
  value rather than the actual value.
• Univariate, Difference, Unmatched, Interval,
  Nonnormal, 2 groups.

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Paired t-test

• Used to compare the average for measurements made
  twice within the same person - before vs. after.
• Used to compare a treatment group and a matched
  control group.
• For example, Did the systolic blood pressure
  change significantly from the scene of the injury
  to admission?
• Univariate, Difference, Matched, Interval,
  Normal, 2 groups.

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Wilcoxon signed-rank test

• Used to compare two skewed continuous variables
  that are paired or matched.
• Nonparametric equivalent of the paired t-test.
• For example, Was the Glasgow Coma Scale score
  different between the scene and admission?
• Univariate, Difference, Matched, Interval,
  Nonnormal, 2 group.

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ANOVA

• One-way used to compare more than 3 means from
  independent groups.
• Is the age different between White, Black,
  Hispanic patients?
• Two-way used to compare 2 or more means by 2 or
  more factors.
• Is the age different between Males and Females,
  With and Without Pnuemonia?

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Kruskal-Wallis One-Way ANOVA

• Used to compare continuous variables that are not
  normally distributed between more than 2 groups.
• Nonparametric equivalent to the one-way ANOVA.
• Is the length of stay different by ethnicity?
• Analyze, nonparametric tests, K independent
  samples.

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Repeated-Measures ANOVA

• Used to assess the change in 2 or more continuous
  measurement made on the same person. Can also
  compare groups and adjust for covariates.
• Do changes in the vital signs within the first 24
  hours of a hip fracture predict which patients
  will develop pneumonia?
• Analyze, General Linear Model, Repeated Measures.

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Pearson Correlation

• Used to assess the linear association between two
  continuous variables.
• r1.0 perfect correlation
• r0.0 no correlation
• r-1.0 perfect inverse correlation
• Univariate, Association, Interval

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Spearman rank-order correlation

• Use to assess the relationship between two
  ordinal variables or two skewed continuous
  variables.
• Nonparametric equivalent of the Pearson
  correlation.
• Univariate, Association, Ordinal (or skewed).

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Summary of Inferential Tests
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Unpaired vs. Paired

• Students t-test
• Chi-square
• One-way ANOVA
• Mann-Whitney U test
• Kruskal-Wallis H test

• Paired t-test
• McNemars test
• Repeated-measures
• Wilcoxon signed-rank
• Friedman ANOVA

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Parametric vs. Nonparametric

• Students t-test
• One-way ANOVA
• Paired t-test
• Pearson correlation
• Correlated F ratio (repeatedmeasures ANOVA)

• Mann-Whitney U test
• Kruskal-Wallis test
• Wilcoxon signed-rank
• Spearmans r
• Friedman ANOVA

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A Good Rule to Follow

• Always check your results with a nonparametric.
• If you test your null hypothesis with a Students
  t-test, also check it with a Mann-Whitney U test.
• It will only take an extra 25 seconds.

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VIII. You Will Need to Understand Regression
Techniques
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Linear Regression

• Used to assess how one or more predictor
  variables can be used to predict a continuous
  outcome variable.
• Do age, number of comorbidities, or admission
  vital signs predict the length of stay in the
  hospital after a hip fracture?
• Multivariate, Association, Interval/Ordinal
  dependent variable.

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Logistic Regression

• Used to assess the predictive value of one or
  more variables on an outcome that is a yes/no
  question.
• Do age, gender, and comorbidities predict which
  hip fracture patients will develop pneumonia?
• Multivariate, Difference, Nominal dependent
  variable, not time-dependent, 2 groups.

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Draw Conclusions

• We reject the null hypothesis.
• Patients who are at high risk of developing
  pneumonia during their hospitalization for a hip
  fracture can be identified by
• total number of pre-existing conditions
• cirrhosis
• COPD
• male gender

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How this information could be used to predict
pneumonia on admission

• Z-4.899 (number of comorbidities x 0.469)
  (cirrhosis x 2.275) (COPD x 0.714) (age x
  0.021) (genderfemale1, male0 x 0.715)
• e2.718
• Example, an 80 year old male with cirrhosis and
  one other comorbidity (but not COPD) had a 99.4
  chance of developing pneumonia.
• Z-4.899 (2 x 0.469) (1 x 2.275) (0 x
  0.714) (80 x 0.021) (0 x 0.715)

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Survival Analysis

• Kaplan-Meier method
• Used to plot cumulative survival
• Log-rank test
• Used to compare survival curves
• Cox proportional-hazards
• Used to adjust for covariates in survival analysis

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Odds and Ends You Will Need
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95 Confidence Intervals

• A 95 confidence interval is an estimate that you
  make from your sample as to where the true
  population value lies.
• If your study were to be repeated 100 times, you
  would expect the 95 CIs to cross the true value
  for the population in 95 of these 100 studies.
• the value might be a mean, percentage or RR
• Confidence intervals should be included in
  publications for the major findings of the study.

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Prevalence vs. Incidence

• Prevalence
• How many of you now have the flu?
• Incidence
• How many of you have had the flu in the past year?

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Random

• Random is not the same as haphazard, unplanned,
  incidental.
• Allocating patients to the treatment group on
  even days and to the control group on odd days is
  systematic not random.
• Random refers to the idea that each element in a
  set has an equal probability of occurrence.

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Improving a RCT

• See the handout, Table 3-2 pages18-19.
• Checklist to Be Used by Authors When Preparing
  or by Readers When Analyzing a Report of a
  Randomized Controlled Trial.

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IX. You Will Need to Continue Learning About
Statistics
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Recommended books on statistics

• Kuzma Statistics in the Health Sciences
• Norusis Data Analysis with SPSS
• Altman Statistics with Confidence
• Friedman Fundamentals of Clinical Trials
• Pagano Principles of Biostatistics
• Encyclopedia of Biostatistics
• SPSS manuals

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A response to the comment Youre comparing
apples and oranges

• No this is comparing apples and oranges!