1. Statistical Reasoning and Analysis
Tony Panzarella
Department of Biostatistics Princess Margaret Cancer Center 
September 2014

2. “Lies, damned lies, and statistics”
(British Prime Minister Benjamin Disraeli,1804–1881)
“Talked politics, scandal, and the three classes of witnesses—liars, d—d liars, and experts.” (The Life and Letters of Thomas Henry Huxley by L. Huxley, 1900)

3. Statistics – the new Sexy!?
Hal Varian, Google's chief economist, says statistician will be 'the sexy job in the next 10 years.' Chad Schafer explains why. (August 4, 2013, Daniel Marsula/Post-Gazette)
Google’s prediction: What will be the "sexy" job in the next ten years?
Here’s a strange prediction
from Google’s Chief Economist:
“I keep saying that the sexy job
in the next 10 years will be
statisticians. And I’m not kidding.”

5. Some Keywords Study Designs, Confounding Pitfalls of Data Analysis
Bias (representative sampling, statistical assumptions)
Errors in methodology (statistical power, multiple comparisons, measurement error)
Interpretation (precision and accuracy, causality, graphical representation)
Era of Big Data

7. Prelude: Design and Analysis
Objective: Design the ultimate Intro to PHS talk… and the worst one that I can still get away with…
Methods: Identify topic(s), and delivery with visuals
Examples; No formula
Take-home messages

8. Correlations, Cluster analysis
Source: Sebastian Wernicke, 2010, TedTalks

9. Nonparametric methods using ranks, Discriminant analysis
Source: Sebastian Wernicke, 2010, TedTalks

10. Confidence Intervals, Associations, Time-to-event analysis
Source: Sebastian Wernicke, 2010, TedTalks

11. Data Mining, Pattern Recognitions
Source: Sebastian Wernicke, 2010, TedTalks

12. Pattern Recognitions (Evidence….Hans Rolling)
Source: Sebastian Wernicke, 2010, TedTalks
Hans Rolling “The best stats you’ve ever seen, New insights on poverty)

13. Motivating Example: Smoking & Survival
20-year follow-up study, Wickham in UK (Tunbridge et al. 1977)
, one-in-six survey of the electoral roll, largely concerned with thyroid disease and heart disease
For simplicity, consider women aged 45 to 75 at the start of the study
Smoking status: current smoker (Y/N)
20-year survival info: determined for all women in the study

14. Smoking & Survival (Cont’d)
Protective effect of smoking?
(data adapted from Appleton et al. 1996, Am. Stat.)

15. Smoking & Survival (Con’t)
Consider 10-year ranges: 45-54,55-64,65-75
Non-smoking group does better in each case!

16. Gender Bias, or Not?
1973, UC Berkeley was sued for discrimination against women in graduate school admissions
Percent acceptance: Male vs Female,
44% vs. 35%

17. Gender Bias, or Not? (cont’d)
P. J. Bickel, E. A. Hammel, J. W. O'Connell. (1975). Sex Bias in Graduate Admissions: Data from Berkeley. Science 187, (4175). pp

18. Message #1
Be aware of the dangers of ignoring a covariate that is correlated to an outcome variable and an explanatory one.
Simpson, E.H. (1951). “The interpretation of Interaction in Contingency Tables”, Journal of the Royal Statistical Society, B, 13,
Simpson’s Paradox; many other examples

19. Guard Against Biases

20. Biases due to … Selection of subjects: web surveys
Responses: e.g. question on income
Contamination in controls: non-blind study
Recall: food-intake
Attrition: drop out
Reporting: negative findings
Publication: meta-analysis
Over thirty kinds of biases

21. Guard Against Biases [BACKUP REFERENCE] Bias in design
Concato et al (2001). A nested case–control study of the effectiveness of screening for prostate cancer: research design
Concato et al. (2001) reports another type of bias in designs for prostate cancer detection when groups were asymptomatic men who received digital rectal examination, screening by prostate specific antigen and transrectal ultrasound, but there was no ‘control’ group with ‘no screening’. Thus the effectiveness of screening could not be evaluated.
Although prostate-specific antigen (PSA) and digital rectal examination (DRE) are commonly used to screen for prostate cancer, available data do not confirm that either test improves survival. This report describes the methodological aspects of a nested case–control study addressing the question of whether PSA screening, with or without DRE, is effective in increasing survival. Potential sources of bias are discussed, as well as corresponding strategies used to avoid them

22. Possible steps to minimize bias
Assess the validity of the identified target population, and the groups to be included in the study in the context of objectives and the methodology.
Evaluate the reliability & validity of the measurements required to assess the antecedents and outcomes, also other tools you plan to deploy.
Carry out a pilot study and pretest the tools. Make changes as needed.
Identify possible confounding factors and other sources of bias; develop an appropriate design that can take care of most of these biases.
Use matching, blinding, masking, and random allocation as needed.
Analyze the data with proper statistical methods. Use standardized or adjusted rates where needed, do the stratified analysis, or use mathematical models such as regression to take care of biases that could not be ruled out by design.
Report only the evidence based the results – enthusiastically but dispassionately

23. Multiple Testing (Large p & small n)

24. Data Dredging

25. “Deming, data and observational studies: a process out of control and needing fixing” Young and Karr (2011) Significance, p

26. “Deming, data and observational studies: a process out of control and needing fixing”

27. Deming, data and observational studies: a process out of control and needing fixing” Young and Karr (2011). Significance, p
Young, S. S. and Yu, M. (2009) To the Editor. Journal of the American Medical Association, 301, 720–721.

28. Visual Display of Quantitative Information
Effectiveness of traffic enforcement in , Before vs. After
Source (Tufte, 1983)

29. In the Age of BIG Data Does Big Data make Statistics obsolete? NO!
BIG data, Big mistake? (Google Flu)

30. Statistical Truisms Correlation does not imply Causation
In fact, causal relationships are among the most significant discoveries from big data that analytics practitioners seek. Finding causes to observed effects would truly be a gold mine of value for any business, science, government, healthcare, or security group that is analyzing big data.
Sample variance does not go to zero, even with Big Data

31. Statistical Truisms (cont’d)
Sample bias does not necessarily go to zero, even with Big Data 
Sample bias can lead to models with biased results, slanted against the wonderful diversity of the original population
Absence of Evidence is not the same as Evidence of Absence 
Reference:

32. Acknowledgements
Prof. Wendy Lou

33. Q & A