ST1232 Statistics in the Life Sciences

1. ST1232 Statistics in the Life Sciences YY TeoAssociate ProfessorSaw Swee Hock School of Public Health, NUSDepartment of Statistics & Applied Probability, NUSLife Sciences Institute, NUSGenome Institute of Singapore, A*STAR

2. Lesson Structure • 13 weeks of 2 lectures (of 2 hours) per week • Practically, 17-18 lectures planned, newspaper statistics, conferences, etc. • Tutorials in computer labs from week 3 onwards (11 weeks of tutorials) • Consultation (Fridays 2pm – 3.30pm) • 3 assessments: • tutorial participation (10%) • mid-term quiz (30%) • end-of-term exam (60%)

3. Resources • Lectures, slides, tutorials • Fred Ramsey and Dan Schafer (2001) The Statistical Sleuth. 2nd edition, Duxbury Press • Julie Pallant. SPSS Survival Manual: A Step-by-Step Guide to Data Analysis Using SPSS for Windows. 3rd edition, Open University Press • http://www.statistics.nus.edu.sg/~statyy/ST1232

4. Tutorials • Note the available time slots and sign up at the CORS system: http://www.nus.edu.sg/cors/. The tutorial will be at S16-05-102 (Com lab 2) • T1: Mondays (8am – 9am) • T2: Mondays (9am – 10am) • T3: Tuesdays (8am – 9am) • T4: Tuesdays (9am – 10am) • T5: Wednesdays (9am – 10am) • T6: Wednesdays (10am – 11am) • T7: Wednesdays (11am – 12pm) • T8: Thursdays (9am – 10am) • T9: Thursdays (10am – 11am) • T10: Thursdays (11am – 12pm) • T11: Fridays (8am – 9am) • T12: Fridays (9am – 10am) • T13: Fridays (10am – 11am)

5. Medical Statistics • Quantitative basis to human diseases and traits • Progression from observational science! • Statistics and mathematics required for this advancement, from observational to quantitative

6. Statistics in medical research Identification of risk factors Disease prevention / treatment Association with genes and environment Disease risk modeling and prediction Pharmaceutical developments / clinical trials Understand inter-population risks to diseases Establish population-specific risk architecture Relevance of international trials and findings Applications in a multi-ethnic setting Medical statistics

7. Pregnancy Test Kit A woman buys a pregnancy test kit, and is interested to find out whether she is pregnant. One hypothesis in this case (status quo), is that she is not pregnant. The other hypothesis(hypothesis of interest), is that she is pregnant. Test kit may show: +ve: indicating there isevidenceto suggest pregnancy –ve: indicating lack of evidence to suggest pregnancy

8. Pregnancy Test Kit The test kit may either be accurate, or inaccurate. Actually pregnant Actually not pregnant Incorrect +ve diagnosis Correct +ve diagnosis(Sensitivity, or Power) Test kit shows +ve Correct –ve diagnosis(Specificity) Incorrect –ve diagnosis Test kit shows –ve

9. Sensitivity or Specificity? • Objective of the experiment

10. Sensitivity or Specificity? • Objective of the experiment • HIV diagnostic kit, 99.9% sensitive and 99.5% specific

11. Sensitivity or Specificity? • Objective of the experiment • HIV diagnostic kit, 99.9% sensitive and 99.5% specific Correct identification of HIV +ves

12. Sensitivity or Specificity? • Objective of the experiment • HIV diagnostic kit, 99.9% sensitive and 99.5% specific Correct identification of HIV -ves Correct identification of HIV +ves

13. Sensitivity or Specificity? • Objective of the experiment • HIV diagnostic kit, 99.9% sensitive and 99.5% specific • Tests on immigrants, assume 1,001,000 applications each month, of which 1000 are truly HIV-positive

14. Sensitivity or Specificity? • Objective of the experiment • HIV diagnostic kit, 99.9% sensitive and 99.5% specific • Tests on immigrants, assume 1,001,000 applications each month, of which 1000 are truly HIV-positive HIV +ve x 1000 HIV –ve x 1,000,000

15. Sensitivity or Specificity? • Objective of the experiment • HIV diagnostic kit, 99.9% sensitive and 99.5% specific • Tests on immigrants, assume 1,001,000 applications each month, of which 1000 are truly HIV-positive HIV +ve x 1000 HIV –ve x 1,000,000 On average, 999 correctly identified, 1 incorrectly diagnosed as HIV -ve On average, 995,000correctly identified as HIV -ve, 5000 incorrectly diagnosed as HIV +ve

16. Sensitivity or Specificity? 995,001 identified as HIV –ve in total 5,999 identified as HIV +ve in total BUT… Almost 5 in 6 of those identified as HIV +ve are FALSE! On average, 999 correctly identified, 1 incorrectly diagnosed as HIV -ve On average, 995,000correctly identified as HIV -ve, 5000 incorrectly diagnosed as HIV +ve

17. Height and Weight

18. Height and Weight

19. Height and Weight

20. Height and Weight

21. Scientific Process

22. Research hypothesis: - What is your scientific question? - What are you trying to achieve?

23. Scientific Process

24. Human Diversity

25. Human Diversity • Even within human race, variation exists between people of different ethnicities, cultures and populations • Genetic basis to a substantial fraction of such variation

26. Human Diversity • Even within human race, variation exists between people of different ethnicities, cultures and populations • Genetic basis to a substantial fraction of such variation • Observable differences – physical appearances, build, weight

27. Human Diversity • Even within human race, variation exists between people of different ethnicities, cultures and populations • Genetic basis to a substantial fraction of such variation • Observable differences – physical appearances, build, weight • Variation in susceptibility to diseases • Influenced by evolutionary processes, over many generations • Cross-sectional observation of adaptation and natural selection

28. Target population • Depends entirely on your research hypothesis!

29. Target population: - Everyone in Singapore? - Every female individuals in Singapore? - Every female individuals of a certain age in Singapore? - Every femal individuals of a certain age in Singapore, and who could be pregnant?

30. Target population: - Everyone in Singapore? - Everyone of a certain age in Singapore? - Everyone of a certain age in NUS? - Everyone of a certain age from a specific population group in Singapore

31. Target populations • Depends entirely on your research hypothesis! • Example: Interest to investigate the genetic factors that increase the risk to type 2 diabetes in Chinese adults in Singapore. • Target population(s): • Every Chinese adult in Singapore that is affected by type 2 diabetes • Normal Chinese adults (unaffected by type 2 diabetes) of the same age band • Classic case-control design in medical epidemiology. But, is this sufficient???

32. Samples versus Population • Obviously not possible to perform an experiment on every diabetic Chinese adult in Singapore • Select a representative set of individuals from the appropriate population to perform the experiment on • This set of individuals is known as your samples. All diabetic Chinese adults in Singapore Selected samples in research

33. Scientific Process

34. What is your intuition? • A pharmaceutical firm is developing a medical drug, that purportedly treats severe headache. • During the clinical trials (testing the efficacy and safety of the drug), it was tested on 10 people, of which 7 reported that it worked to reduce headaches, while 3 claimed it had no effect. • Another pharma also developed a competing treatment, but tested on 1000 people, of which 704 reported it helped to reduce headaches, while 294 claimed it had no effect, and 2 people claimed their headaches worsen. Which setting do you think gives you more information about the developed drug? And why?

35. Sample Size Determination 200 cases and 200 controls RR = 2.5 RR = 1.8 RR = 1.2 1000 cases and 1000 controls For complex diseases! 4000 cases and 4000 controls • Types of effects that can be detected depends entirely on sample sizes.

36. Pregnancy Test Kit The test kit may either be accurate, or inaccurate. Actually pregnant Actually not pregnant Incorrect +ve diagnosis Correct +ve diagnosis(Sensitivity, or Power) Test kit shows +ve Correct –ve diagnosis(Specificity) Incorrect –ve diagnosis Test kit shows –ve

37. Sample Size Determination • An issue commonly discussed in medical research! • Power calculations, sample size, effect sizes, statistical significance? Power calculations Sample size Effect sizes Statistical Significance Recall: Your ability to identify a true pregnancy Require  evidence, means Power What level of statistical evidence do you consider “believable”?

38. Scientific Process

39. Sample Selection • Simple Random Sample • Every sample in the population has an equal chance of being selected (e.g. phonebook sampling) • Stratified Sample • Every sample in the population belongs uniquely to a specific category (e.g. gender) • Cluster Sampling • Each cluster has the characteristics of the population, and sampling is performed within the cluster rather than in the population (e.g. diabetic patients in one hospital in Singapore, compared to all diabetic patients in Singapore) • Multistage Sampling • A combination of different sampling schemes

40. Scientific Process

41. Data exploration and Statistical analysis • Exploratory data analysis • Probability and Bayes Theorem • Theoretical distributions (Uniform, Bernoulli, Binomial, Poisson, Normal) • Confidence Interval • Hypothesis testing (t-test, ANOVA, test of proportions, Chi-square tests) • Non-parametric tests • Linear regression and correlation • Logistic regression GIBBERISH?!

42. Data exploration and Statistical analysis • Data checking, identifying problems and characteristics • Understanding chance and uncertainty • How will the data for one attribute behave, in a theoretical framework? • Theoretical framework assumes complete information, need to address uncertainties in real data • Testing your beliefs, do the data support what you think is true? • What happens when the assumptions of the theoretical framework are not valid • Modeling relationships between multiple outcomes and a numerical response • Ditto, but with a two-state outcome.

43. Data exploration, categorical / numerical outcomes Data Model each outcome with a theoretical distribution Model relationships between different outcomes Estimation of parameters, quantifying uncertainty Estimation of parameters, quantifying uncertainty Hypothesis testing Linear regression (Numerical response) Logistic regression (Categorical response) Parametric tests (t-tests, ANOVA, test of proportions) Non-parametric tests (Wilcoxon, Kruskal-Wallis, rank test) Confidence intervals, to quantifying uncertainty

44. Scientific Process

45. Statistics – Truths or Lies • 21st century – age of information • Responsible for driving scientific progress in multiple disciplines • Core skills for data analysis • Ability and knowledge to ingest and digest information is at a premium

46. Statistics – Truths or Lies

47. Computers and Statistics Computers and Statistics • Excel, SPSS, Minitab, Stata, Mathlab, R, etc… • RExcel for this course: • http://www.stat.nus.edu.sg/~statyy/ST1232/bin/RExcel_installation.docx • Advantages • Speed, accuracy, ease of data manipulation • Easy to produce plots, cross-tabulation tables, summary statistics • Disadvantages • Inappropriate analysis / use of wrong tests • Data dredging

48. Brief introduction to RExcel and SPSS

49. Features • RExcel and SPSS – extremely similar in terms of data entry and usage • Spreadsheet-based data entry system