1. Primer on Statistics for Interventional Cardiologists Giuseppe Sangiorgi, MD Pierfrancesco Agostoni, MD Giuseppe Biondi-Zoccai, MD

2. Why waisting time with statistics? BMJ 2003

3. What you will learn - hopefully! Introduction Basics Descriptive statistics Probability distributions Inferential statistics Finding differences in mean between two groups Finding differences in mean between more than 2 groups Linear regression and correlation for bivariate analysis Analysis of categorical data (contingency tables) Analysis of time-to-event data (survival analysis) Advanced statistics at a glance Conclusions and take home messages

4. What you will NOT learn Multivariable analysis Advanced linear regression methods Logistic regression Cox proportional hazards analysis Generalized linear models Bayesian methods Propensity analysis Resampling methods Meta-analysis Most popular statistical packages (beyond SPSS)

5. What you will learn Introduction Basics Descriptive statistics Probability distributions Inferential statistics Finding differences in mean between two groups Finding differences in mean between more than 2 groups Linear regression and correlation for bivariate analysis Analysis of categorical data (contingency tables) Analysis of time-to-event data (survival analysis) Advanced statistics at a glance Conclusions and take home messages

6. What to choose? Simple and easy-going or … fast but tough?

7. Science or fiction? There are three kind of lies: lies, damn lies, and statistics B. Disraeli Knowledge is the process of piling up facts, wisdom lies in their simplification M. Fisher

8. What is statistics? DEFINITIONS A whole subject or discipline A collection of methods Collections of data Specially calculated figures

9. What is statistics? DEFINITIONS A whole subject or discipline A collection of methods Collections of data Specially calculated figures

10. A collection of methods

11. Statistics is great Find out stuff –Finding stuff out is fun Feel like you have done something It’s small, but it’s something Understand stuff –When are we being deceived –Support, or illumination?

12. Ultimate goal: appraisal of causation

13. Methods of inquiry Statistical inquiry may be… Descriptive (to summarize or describe an observation) or Inferential (to use the observations to make estimates or predictions)

14. Questions?

15. What you will learn Introduction Basics Descriptive statistics Probability distributions Inferential statistics Finding differences in mean between two groups Finding differences in mean between more than 2 groups Linear regression and correlation for bivariate analysis Analysis of categorical data (contingency tables) Analysis of time-to-event data (survival analysis) Advanced statistics at a glance Conclusions and take home messages

16. What you will learn Basics –concepts of population and sample –collecting data –study design and protocol –randomization –intention-to-treat vs per-protocol analysis –types of variables –measurement scales

17. What you will learn Basics –concepts of population and sample –collecting data –study design and protocol –randomization –intention-to-treat vs per-protocol analysis –types of variables and measurement scales

18. Population and sample: at the heart of descriptive and inferential statistics Again: statistical inquiry may be… Descriptive (to describe a sample/population) or Inferential (to measure the likelihood that estimates generated from the sample may truly represent the underlying population)

19. Descriptive statistics AVERAGE 100

20. Descriptive statistics example

21. Descriptive statistics Meredith et al, Am J Cardiol 2007

22. Descriptive statistics example Meredith et al, Am J Cardiol 2007

23. Inferential statistics If I become a scaffolder, how likely I am to eat well every day? P values Confidence Intervals

24. Inferential statistics Mauri et al, New Engl J Med 2007

25. Inferential statistics Mauri et al, New Engl J Med 2007

26. Focus on p values Mauri et al, New Engl J Med 2007

27. Focus on confidence intervals Mauri et al, New Engl J Med 2007

28. Samples and populations This is a sample

29. Samples and populations And this is its universal population

30. Samples and populations example

31. Samples and populations Only 300 patients! Kastrati et al, JAMA 2005

32. Samples and populations This is another sample

33. And this might be its universal population Samples and populations

34. But what if THIS is its universal population? Samples and populations

35. Any inference thus depend on our confidence in its likelihood Samples and populations

36. What you will learn Basics –concepts of population and sample –collecting data –study design and protocol –randomization –intention-to-treat vs per-protocol analysis –types of variables and measurement scales

37. Data collection Data collection is pivotal and should be planned well before actually performing it Any variable or item code should be collected in a clear and unequivocal way A missing code is still a code (eg 999) Data types can be dozens: –String –Categorical –Ordinal –Data –Time –Interval

38. Data collection Coherence and safety checks should always be implemented Multiple data entry should be used to minimize human error Thorough monitoring and quering are also critical Currently, the best approach for data collection in the current era are web-based case report forms (CRF) Despite this, the risk of information bias is always there and should be kept at a minimum as much as possible

39. What you will learn Basics –concepts of population and sample –collecting data –study design and protocol –randomization –intention-to-treat vs per-protocol analysis –types of variables and measurement scales

40. Designs for various research goals CASE STUDY/REPORT/SERIES SURVEY CROSS SECTIONAL MATCHED PAIRS (CASE-CONTROL) HISTORICAL CONTROLS (BEFORE-AFTER) CONCURRENT CONTROLS LONGITUDINAL (COHORT) CROSS-OVER RANDOMIZED CLINICAL TRIALRANDOMIZED CLINICAL TRIAL META-ANALYSIS

41. Phases of clinical research CHEMICAL STUDIES ANIMAL PHARMACOLOGY AND TOXICOLOGY PHASE I PHASE II PHASE IIIPHASE IV REGISTRATION (CE MARK) MARKETING REGULATORY APPROVAL PILOT/FEASIBILITY STUDY PIVOTAL STUDY POST-MARKETING STUDY

42. Endeavor research program 42 ENDEAVOR I Phase I FIM 60 month results Phase I FIM 60 month results ENDEAVOR II Double-blind Randomized Trial 48 month results Double-blind Randomized Trial 48 month results ENDEAVOR II CA Registry Continued Access Safety 48 month results Continued Access Safety 48 month results ENDEAVOR III Confirmatory Trial vs. Cypher 36 month results Confirmatory Trial vs. Cypher 36 month results ENDEAVOR IV Confirmatory Trial vs. Taxus 24 month results Confirmatory Trial vs. Taxus 24 month results Single Arm Trial 12 month results Single Arm Trial 12 month results ENDEAVOR Japan Real-World Performance and Safety Evaluation – 12 month results E-Five Registry Endeavor vs. Cypher Safety Study 8,800 patient RCT Endeavor vs. Cypher Safety Study 8,800 patient RCT PROTECT

43. Reviews

44. Joner et al, JACC 2008 Preclinical studies

45. McFadden et al, Lancet 2004 Case report(s)

46. Cross-sectional study

47. Case-control study

48. Before-after study

49. Cohort study (registry) Lee et al, EuroInterv 2007

50. Cohort study (registry)

51. Cross-over study

52. Fajadet et al, Circulation 2006 Randomized trial

53. Another RCT– the SORT OUT II Galloe et al, JAMA 2008

54. Another RCT– the SORT OUT II Galloe et al, JAMA 2008

55. Another RCT– the SORT OUT II Galloe et al, JAMA 2008 Would you trust this trial?

56. Another RCT– the SORT OUT II Galloe et al, JAMA 2008 Would you trust this trial?

57. Another RCT – the ENDEAVOR IV Patients Enrolled N = 1548 Randomized Endeavor n = 773 Clinical F/U (12 mo) 754/773 97.5% Taxus n = 775 Clinical F/U (12 mo) 751/775 96.9% Clinical F/U (24 mo) 742/773 96.0% Clinical F/U (24 mo) 739/775 95.4%

58. Kastrati et al, NEJM 2007 Meta-analysis

59. What you will learn Basics –concepts of population and sample –collecting data –study design and protocol –randomization –intention-to-treat vs per-protocol analysis –types of variables and measurement scales

60. Randomization Technique enabling the correct application of statistical tests according to frequentist theory (R. Fisher) Randomization means random allocation of the patient (or any other study unit) to one of the possible treatments On the long run, randomization minimizes the chances of finding imbalances in patient or procedural features, but this applies only to large samples (several hundreds) and few key clinical features

61. Randomization types Simple Stratified In blocks Clustered

62. Randomization types Simple Stratified In blocks Clustered Pt numberRx 1A 2B 3B 4B 5B 6A 7A 8B 9B 10A 11A Pt numberRx 12B 13B 14A 15A 16B 17B 18A 19B 20B 21B 22B

63. Randomization types Simple Stratified In blocks Clustered Pt numberRx 1A 2B 3B 4B 5B 6A 7A 8B 9B 10A 11A Pt numberRx 1A 2B 3B 4A 5A 6A 7B 8B 9A 10B 11A

64. Randomization types Simple Stratified In blocks Clustered Pt numberRx 1A 2B 3B 4A 5B 6A 7A 8B 9B 10A 11A Pt numberRx 12B 13B 14A 15A 16B 17B 18A 19B 20A 21B 22B

65. Wrong or pseudo-randomizations EXAMPLES – TO AVOID! 1.Alternate days of admission 2.According to birthday 3.Coin tossing 4.Card deck selection 5.Patient initials

66. What you will learn Basics –concepts of population and sample –collecting data –study design and protocol –randomization –intention-to-treat vs per-protocol analysis –types of variables and measurement scales

67. Intention-to-treat analysis Intention-to-treat (ITT) analysis is an analysis based on the initial treatment intent, irrespectively of the treatment eventually administered ITT analysis is intended to avoid various types of bias that can arise in intervention research, especially procedural, compliance and survivor bias However, ITT dilutes the power to achieve statistically and clinically significant differences, especially as drop-in and drop-out rates rise

68. Per-protocol analysis In contrast to the ITT analysis, the per-protocol (PP) analysis includes only those patients who complete the entire clinical trial or other particular procedure(s), or have complete data In PP analysis each patient is categorized according to the actual treatment received, and not according to the originally intended treatment assignment PP analysis is largely prone to bias, and is useful almost only in equivalence or non-inferiority studies

69. ITT vs PP 100 pts enrolled RANDOMIZATION 50 pts to group A (more toxic) 50 pts to group B (conventional Rx, less toxic) 45 pts treated with A, 5 shifted to B because of poor global health (all 5 died) 50 patients treated with A (none died) ACTUAL THERAPY

70. ITT vs PP 100 pts enrolled RANDOMIZATION 50 pts to group A (more toxic) 50 pts to group B (conventional Rx, less toxic) 45 pts treated with A, 5 shifted to B because of poor global health (all 5 died) 50 patients treated with A (none died) ACTUAL THERAPY ITT: 10% mortality in group A vs 0% in group B, p=0.021 in favor of B

71. ITT vs PP 100 pts enrolled RANDOMIZATION 50 pts to group A (more toxic) 50 pts to group B (conventional Rx, less toxic) 45 pts treated with A, 5 shifted to B because of poor global health (all 5 died) 50 patients treated with A (none died) ACTUAL THERAPY ITT p=0.021 in favor of BITT: 10% mortality in group A vs 0% in group B, p=0.021 in favor of B PP: 0% (0/45) mortality in group A vs 9.1% (5/55) in group B, p=0.038 in favor of A

72. What you will learn Basics –concepts of population and sample –collecting data –study design and protocol –randomization –intention-to-treat vs per-protocol analysis –types of variables and measurement scales

73. Variables Types of variables

74. Variables Types of variablesQUANTITYCATEGORY

75. Variables nominalordinal orderedcategories ranks Types of variablesQUANTITYCATEGORY

76. Variables nominalordinaldiscretecontinuous orderedcategories ranks counting measuring Types of variablesQUANTITYCATEGORY

77. Variables nominalordinaldiscretecontinuous orderedcategories ranks counting measuring Death: yes/no TLR: yes/no TIMIflow BMI Blood pressure QCA data (MLD, late loss) Stent diameter Stent length Types of variablesRadial/brachial/femoral QUANTITYCATEGORY

78. Variables Paired vs unpaired data

79. Variables PAIRED OR REPEATED MEASURES UNPAIRED OR INDEPENDENT MEASURES Paired vs unpaired data

80. Variables PAIRED OR REPEATED MEASURES UNPAIRED OR INDEPENDENT MEASURES eg blood pressure measured twice in the same patients at different times MLD measured at different times in the same segment eg blood pressure measured in several different groups of patients only once MLD measured at the same time in different vessels Paired vs unpaired data

81. Measurement scales What is measurement: the assignment of numbers to objects or events in a systematic fashion Thus, four levels of measurement scales are commonly distinguished: –nominal –ordinal –interval –ratio

82. Thank you for your attention For any correspondence: gbiondizoccai@gmail.com For further slides on these topics feel free to visit the metcardio.org website: http://www.metcardio.org/slides.html gbiondizoccai@gmail.com http://www.metcardio.org/slides.html