This document discusses key concepts in scientific studies and data analysis. It explains study variables, descriptive and inferential statistics, statistical significance, and different types of study designs including observational studies. Specific topics covered include error bars, regression, p-values, strengths and weaknesses of epidemiological data, risk analysis in epidemiology, case-control studies, cohort studies, and an example of the Framingham Heart Study.

1. Science and Data
Dr. Michael Tims

4. Using a proper scale

5. Using Year-over-year Percent Change

6. Study variable
• Refers to measurement that changes from one
individual to another.
1. Response/dependent variable: Measures the outcome of
the study treatment
2. Independent/ predictor variable: Explains or influences
changes in response variable.

7. X (independent) and Y Axis (dependent)
X
Y

8. Designing the study
• The basic elements of a study design
– Selecting appropriate sample size for a specified level of
power and level of significance
– Selecting methods of sampling, data collection, and
analysis appropriate to the study's objectives

9. • Descriptive statistics describes center, variability, shape of data
such that patterns might emerge
• Inferential statistics predictions probability that an observed
difference between groups is a dependable one or not
Statistical Inference
Sample Population
Sample: subset of population
whose center, spread and shape are
as close as that of population.

10. What and Why Error Bars?
• Represents variability in data
• Indicate uncertainty (precision) of measurement
velica.deviantart.com/art/Error-bars-101948712

12. Error Bars

13. Regression
Modeling the relationship between response/dependent variable
and the independent/ predictor variable

14. Data and Error Bars
How to create error bars using Excel
http://people.hws.edu/halfman/Excel-07-
demo/07-Error-Bars.html

15. P Value
• Two shaded areas are equidistant from the null hypothesis value
(260) and each area has a probability of 0.025, for a total of 0.05
• This critical region defines how far away our sample statistic must
be from the null hypothesis value before we can say it is unusual
enough to reject the null hypothesis.
Probability
p < 0.005 excellent
p < 0.05 good

16. Statistical Significance
• When observed p-value less than
the significance level defined for the study
• Always possible that an observed effect would
have occurred due to sampling error alone
• If p-value less than the significance level,
reflects characteristics of whole population
• Does not imply research, theoretical, or
practical significance

17. Clinical/Experimental versus
Observational design

18. Observational – Correlational Evidence

20. Is Vitamin E Associated with Lower
Prostate Cancer Risk
• Correlational evidence predicts that men who take
Vitamin E have lower risk for prostate cancer
• Experimental data suggests Vitamin E contributes to
the likelihood of prostate cancer
• Healthy user bias

21. What about epidemiological studies of
medicinal herbs?
• Large population of self-prescribing herbal users
• Provide safety and efficacy data not currently
generated by clinical trials
• Rapidly identify activity without knowledge of
mechanism or active ingredient (outbreaks)
• Correlate efficacy in animal and in vitro studies with
specific dose and form in humans.

22. Challenges
• What are the actives and how much is present?
• Dose – not directly administered
• Compliance –how to model a dose-response study
with complex pattern of use
• Relevant timing – disease etiology may require
temporal specificity (cancer)
• Outcome biased by consumer – are healthy people
more likely to use herbs?

23. Strengths and Weaknesses of
Epidemiological Data
+
§ Attempts to eliminate risk of recall bias
§ Dose range broader than clinical trial
-
§ Less reliable than randomized controlled study
§ Groups may differ in other ways.
§ Takes a long time

24. Epidemiology: Risk Analysis
Condition:
Coronory Heart Disease
Total
Yes No
Exposure:
Periodontal
Disease
Yes 349 1437 1786
(A) (B) (A+B)
No 1706 6898 8604
(C) (D) (C+D)
Total 2055 8335 10390
(A+C) (B+D) (A+B+C+D)
Relative Risk = (A/A+B) / (C/C+D) = 0.98
Ratio of the incidence of disease in the exposed
group relative to unexposed group
DeStefano et al. (1993) Br Med J 306: 688-691

25. Epidemiology: Case Control Study
• Identification of population with the disease or
outcome of interest (cases) and a suitable control
group without the disease or outcome.
• Retrospective
• Herb/drug interactions
• Fiber consumption vs. colorectal cancers.

26. Epidemiology: Cohort Study
Presence of exposure determined in sample population

27. Framingham Heart Study
Goal: Identify the common factors or characteristics
that contribute to cardiovascular disease (CVD) by
following its development over a long period of time.
Now on fourth generation
https://www.framinghamheartstudy.org/