1. ASPIRE Class 5 Biostatistics and Data Collection Tools
Daniel M. Witt, PharmD, BCPS, FCCP

2. Learning Objectives
ASPIRE
Class 5: Biostatistics
Differentiate between descriptive and inferential statistics
Choose an appropriate statistical test based on the type of data being analyzed
Describe the concepts of normal distribution, population, and sample
Formulate the analytic plan for a research study
Evaluate various data collection tools and databases to collect study data

3. Elements of a Research Protocol
Background
Population
Design
Objectives
Procedures
Analytical Plan

4. Class 5 Assignment
Please come prepared with the above items October 19th, 2:30-5:00 Kaiser Permanente Central Support Services

5. Daniel M. Witt, PharmD, FCCP, BCPS, CACP Kaiser Permanente Colorado
Biostatistics
Daniel M. Witt, PharmD, FCCP, BCPS, CACP
Kaiser Permanente Colorado

6. Why Biostatistics? Which medical practices actually help?
Determining what therapies are helpful based on simple experience doesn’t work
Biologic variability
Placebo effect

7. Drug appears to be effective at increasing CO
An Example
Drug appears to be effective at increasing CO
Cardiac output
Drug dose

8. Clearly no relationship between drug dose and CO
An Example
Clearly no relationship between drug dose and CO
Cardiac output
Drug dose

9. Biostatistics A useful tool
Turns clinical and laboratory experience into quantitative statements
Determines whether and by how much a treatment or procedure affected a group of patients
Turns boring data into an interesting story

10. Learning Point Experiments rarely include entire population
Selecting unrepresentative samples (bad luck) is unlikely but possible
Biostatistical procedures permit estimation of the chance of such bad luck
Tell a story (who, what, why, where, how)

11. General Research Goals
Obtain descriptive information about a population based on a sample of that population
Test hypotheses about the population
Minimize bias

12. Random Variables Definition: Two types “This is important because….”
Outcomes of an experiment or observation whose values cannot be anticipated with certainty
Two types
Discrete
Continuous
“This is important because….”
choosing (and evaluating) statistical methods depends, in part, on the type of data (variables) used

13. Discrete (counting) Variables
2 types-
Nominal: classified into groups in no particular order, and with no indication of relative severity (e.g., sex, mortality, disease state, bleeding, stroke, MI)
Ordinal: ranked in a specific order, but with no consistent level of magnitude difference between ranks (e.g., NYHA class, trauma score) 1 2 3
Discrete Variables
Caution: Mean and standard deviation is
NOT reported with this type of data

14. Continuous (measuring) Variables
Data are ranked in a specific order with a consistent change in magnitude between units; (e.g., heart rate, LDL cholesterol, blood glucose, INR, blood pressure, time, distance) 1 2
Continuous Data

15. Summarizing Data Bell-shaped frequency distribution Landmarks x: mean
SD: standard deviation (SD)
Normal distribution:
(most common model for
population distributions) 30 35 40 45 50
N=200
Mean=40
SD=5.0 x SD SD

16. Mean (average) Only used for continuous, normally distributed data
SD=2.5 10 15 20
Mean (average)
Only used for continuous, normally distributed data
Sensitive to outliers
Most commonly used measure of central tendency

17. Non-Normal Distributions
Mean ± SD
N=100
Mean=37.6
SD=4.5
Although mean and SD can be calculated for any population,
Does not summarize the distribution as well as for normal distributions
A better approach is to use percentiles

18. Median Half of observations fall below and half lie above
Median (50th percentile)
Median
Half of observations fall below and half lie above
Can be used for ordinal or continuous data
Insensitive to outliers

19. Percentiles 25th percentile 75th percentile
The in a distribution where a value is larger than 25% or 75% of the other values in the sample
Does not assume that the population has a normal distribution

20. Standard Deviation (SD)
68%
95%
- 2SD
- 1SD
mean
+ 1SD
+ 2SD
Standard Deviation (SD)
Appropriately applied only to data that are
normally or near normally distributed
Applicable only to continuous data
Within +/- 1 SD are found 68% of the sample’s values,
Within +/- 2 SD are found 95% of the sample’s values

21. Hypothesis Testing The null hypothesis (Ho)
posits no difference between groups being compared (Group A = Group B)
a statistical convention (but a good one)
is used to assist in determining if any observed differences between groups is due to chance alone (bad luck)
in other words, is any observed difference likely due to sampling variation?

22. Hypothesis Testing
Example: A new anti-obesity medication is compared to an existing one to determine if one agent is better at achieving goal BMI at the recommended starting dose.
Results:
Ho: success rate for new drug = success rate for old drug

23. Hypothesis Testing
Tests for statistical significance determine if the data are consistent with Ho
If Ho is “rejected” = statistically significant difference between groups (unlikely due to chance or ‘bad luck’)
If Ho is “accepted” = no statistically significant difference between groups (results may be due to ‘bad luck’)

24. Hypothesis Testing
The distribution (range of values) for statistical tests when Ho is true is known
Depending on this statistic’s value, Ho is accepted or rejected
Choosing the appropriate statistical test depends on:
Type of data (nominal, ordinal, continuous)
Study design (parallel, cross-over, etc.)
presence of Confounding variables

25. Hypothesis Testing For our example,
0.05
0.01 C2
3.84
6.64
For our example,
data is nominal data, parallel design with no confounders
appropriate test is C2
The frequency distribution of C2 when Ho is true is shown above

26. Hypothesis Testing
Large values are possible when Ho is true, but they occur infrequently (5% of the time when C2 is >3.84 and only 1% of the time when C2 is > 6.64)
These extreme values are used to demarcate the point(s) at which Ho is accepted or rejected

27. Hypothesis Testing For our example:
using the data in the formula for calculating C2 yields a value of 1.64
because 1.64 < 3.84, accept Ho and say that the new drug is not statistically significantly better than the old drug in getting patients to their goal BMI with the recommended starting dose
1.64
3.84 C2

28. Decision Errors

29. Decision Errors
The probability of making a Type I error is defined as the significance level a
By setting a at 0.05, this effectively means that 1 out of 20 times a Type I error will occur when Ho is rejected
The calculated probability that a Type I error has occurred is called the “p-value”
When the a level is set a priori, Ho is rejected when p < a

30. Decision Errors
The probability of making a Type II error (accepting Ho when it should be rejected) is termed b
By convention, b should be < 0.20

31. Decision Errors Power (1-b)
The ability to detect actual differences between groups
Power is increased by:
Increasing a
Increasing n
Large differences between populations
Power is decreased by:
Poor study design
Incorrect statistical tests

32. Statistical Significance Areas for Vigilance
Size of p-value is not related to the importance of the result
Statistically significant does not necessarily mean clinically significant
Lack of statistical significance does not mean results are unimportant

33. Choosing a Statistical Test
Parametric versus non-parametric
Parametric tests assume an underlying normal distribution
Non-parametric tests:
Non-normally distributed data
Nominal or ordinal data

34. Choosing a Statistical Test Continuous Data
Student’s t-test
1 sample: compares mean of study population to the mean of a population whose mean is known
2 sample (independent samples): compares the means of 2 normal distributions
Paired: compares the means of paired or matched samples

35. Choosing a Statistical Test Continuous Data
Analysis of variance (ANOVA)
Compares the means of 3 or more groups in a study
Multiple comparison procedures are used to determine which groups actually differ from each other
e.g., Bonferroni, Tukey, Scheffe, others
Analysis of covariance (ANACOVA)
Controls for the effects of confounding variables

36. Choosing a Statistical Test Ordinal Data
Wilcoxon rank sum
Mann-Whitney U
Wilcoxon signed rank
Kruskal-Wallis
Friedman
These tests may also be used for non-normally distributed continuous data

37. Choosing a Statistical Test Nominal DataX2
Compares percentages between 2 or more groups
Fisher’s exact test
Infrequent outcomes
McNemar’s
Paired samples
Mantel-Haenszel
Controls for influence of confounders

38. 95% Confidence Intervals
When the ABSOLUTE difference between groups is considered:
A 95% confidence interval that excludes zero is considered statistically significant
The 95% confidence interval also provides information regarding the MAGNITUDE of the difference between groups

39. Regression Regression useful in constructing predictive models
Multiple regression involves modeling many possible predictor variables to ascertain which predict a particular target variable
Regression modeling often used to control or adjust for the effects of confounding variables

40. Example of predictive modeling Expected performance derived
from regression
model
Expected
performance
(99% CI)
Observed
performance
Observed differs
from expected by
>5%
Circ Cardiovasc Qual Outcomes 2011;4:22-29

41. Survival Analysis
Studies the time between entry into a study and some event (e.g., death)
Takes into account that some subjects leave the study due to reasons other than the ‘event’ (e.g. lost to follow up, study period ends)
May be utilized to arrive at different types of models
Kaplan-Meier
Cox Regression Model
Proportional hazards regression analysis

42. Kaplan Meier
Uses survival times (or censored survival times) to estimate the proportion of people who would survive a given length of time under the same circumstances
Allows for the production of a survival curve
Uses log-rank test to test for statistically significant differences between groups

43. Survival Analysis-Kaplan Meier Survival Curve
Cumulative Proportion Surviving
1.0
0.8
0.6
0.4
0.2
0.0
Time
Treatment
Control

44. Cox Regression Modeling
Reported graphically like Kaplan-Meier
Investigates several variables at a time
Allows calculation of relative risk estimate while adjusting for differences between groups