1. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Evidence Based Medicine: The Scientific Method and the Role Statistics Plays Al M Best, PhD Affiliate Professor of Biostatistics, School of Medicine Director of Faculty Research Development, School of Dentistry School of Dentistry ALBest@VCU.edu

2. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y CODA → EBD → CAS CODA CODA –2-9 Competent to apply critical thinking and problem- solving skills in the comprehensive care of patients, scientific inquiry and research methodology –2-21 Competent to access, critically appraise, apply, and communicate scientific and lay literature as it relates to providing evidence-based patient care Critical Appraisal Skills Critical Appraisal Skills –Are the results of the study valid? –What are the results? –Will the results help locally?

3. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Outline Science: Using data to answer questions Science: Using data to answer questions Example study Example study –Estimate prevalence in a population  Sampling, measurement, randomness  Estimation –Compare two groups  Hypothesis testing  P-value –Interpret the results  Different outcomes?

4. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Science “a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.” “a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.” Wilson, Edward O. (1998). Consilience: The Unity of Knowledge. New York, NY: Vintage Books. pp. 49–71. ISBN 0-679- 45077-7. Wilson, Edward O. (1998). Consilience: The Unity of Knowledge. New York, NY: Vintage Books. pp. 49–71. ISBN 0-679- 45077-7.ISBN 0-679- 45077-7ISBN 0-679- 45077-7 © Matt Groening, Futurama

5. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y A Scientist’s Quandary Are the results of the study valid? Are the results of the study valid? –Most experiments are highly local but have general aspirations. –How can findings generalize to other people, in other settings, with comparable interventions, and other outcomes. How will you assess whether the paper’s findings will generalize to your situation? How will you assess whether the paper’s findings will generalize to your situation? –This is the question of external validity.

6. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Solution? Use a process where sample data does generalize to the units, treatments, variables and settings not directly observed. Use a process where sample data does generalize to the units, treatments, variables and settings not directly observed. Follow the process called Statistical Inference using the Scientific Method. Follow the process called Statistical Inference using the Scientific Method.

7. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Statistical Inference

8. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Example: David’s “Cardiac Acute Care Nurse Practitioner and 30-Day Readmission.” “The purpose of this study was to determine if the addition of a cardiac acute care NP (CACNP) to care teams could improve utilization outcomes (ie, time of discharge, length of stay, and readmission rates) in patients admitted to a cardiovascular intensive care unit (CCU).” From the objectives of David, et al. (Epub) J Cardiovasc Nurs. pubmed/24651684 “The purpose of this study was to determine if the addition of a cardiac acute care NP (CACNP) to care teams could improve utilization outcomes (ie, time of discharge, length of stay, and readmission rates) in patients admitted to a cardiovascular intensive care unit (CCU).” From the objectives of David, et al. (Epub) J Cardiovasc Nurs. pubmed/24651684pubmed/24651684

9. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Classic Steps: A Paper’s Organization 1. What’s the question? (Introduction) –Conceptualize the population –State the question 2. How will you answer the question? (Methods) –The sample –The measurements –Analysis technique 4. What does it mean? (Discussion) 3. Answer the question (Results) –The sample –The measurements –Analysis technique

10. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y David’s “Cardiac Acute Care Nurse Practitioner and 30-Day Readmission.” What is the PICO question? Who is the Population of interest? Who is the Population of interest? –P = ? What is the Intervention being studied? What is the Intervention being studied? –I = ? The intervention is Compared to what? The intervention is Compared to what? –C = ? What is the Outcome of interest? What is the Outcome of interest? –O = ?

11. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y David’s “Cardiac Acute Care Nurse Practitioner and 30-Day Readmission.” What is the PICO question? Who is the Population of interest? Who is the Population of interest? –P = patients admitted to a cardiovascular ICU What is the Intervention being studied? What is the Intervention being studied? –I = addition of cardiac acute care NP The intervention is Compared to what? The intervention is Compared to what? –C = house-staff team without NP What is the Outcome of interest? What is the Outcome of interest? –O = readmission rate

12. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y David’s “Cardiac Acute Care Nurse Practitioner and 30-Day Readmission.” Main question: Compare Main question: Compare –Rehospitalization within 30 days to the Emergency Department in patients cared for by the NP to –Rehospitalization within 30 days to the Emergency Department in patients not cared for by the NP Design a measurement system Design a measurement system –CCU team = staff+NP or staff –ED return = yes or no

13. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Looking Ahead

14. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Backing up: State the question For NP patients, estimate the 30day readmission rate. For NP patients, estimate the 30day readmission rate. The population has a parameter—call it π —we are trying to estimate this using data. The population has a parameter—call it π —we are trying to estimate this using data.

15. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y The population has a parameter—call it π Conceptualization: The population Conceptualization: The population True:n staff+NP = true count of everyone who was cared for by a NP True:n staff+NP = true count of everyone who was cared for by a NP True:n re = true count of everyone who was cared for by a NP and also readmitted True:n re = true count of everyone who was cared for by a NP and also readmitted π = true prevalence proportion of readmits in NP patients. π = true prevalence proportion of readmits in NP patients. π = True:n re / True:n staff+NP

16. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Estimation of population parameter using sample statistic Definition: A statistic is a single descriptive number computed from the data. Definition: A statistic is a single descriptive number computed from the data. Conceptualization: The sample n staff+NP = count in sample of NP patients n staff+NP = count in sample of NP patients n re = count in sample of NP patients and also readmitted n re = count in sample of NP patients and also readmitted p = estimated proportion of readmits in NP patients. p = estimated proportion of readmits in NP patients. p = n re / n staff+NP

17. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Backing up: State the question In NP patients, estimate the 30da readmission rate. In NP patients, estimate the 30da readmission rate. π = True:n re / True:n staff+NP

18. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Classic Steps What’s the question? (Introduction) What’s the question? (Introduction) –Conceptualize the population –State the question How will you answer the question? (Methods) How will you answer the question? (Methods) –The sample –The measurements –Analysis technique What does it mean? (Discussion) What does it mean? (Discussion) Answer the question (Results) Answer the question (Results) –The sample –The measurements –Analysis technique

19. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Estimation Analyze the data Analyze the data –Readmission of NP patients –n staff+NP = count in sample cared for by the NP –n re = count in sample cared for by the NP and subsequently readmitted p = estimated proportion of readmits in NP patients. p = estimated proportion of readmits in NP patients. p = n re / n staff+NP

20. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Segue: estimation error For NP patients, estimate the 30day readmission rate. For NP patients, estimate the 30day readmission rate. December, 2008 December, 2008 Variability due to sampling: 8 patients Variability due to measurement: n re = 2 n NP = 8 Excluded = ? 2/8 = 0.25

21. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Segue: estimation error For NP patients, estimate the 30day readmission rate. For NP patients, estimate the 30day readmission rate. January, 2009 January, 2009 Variability due to sampling: 4 new + 8 previous patients Variability due to measurement: no new readmits n re = 2 n NP = 12 2/12 = 0.17

22. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Segue: estimation error For NP patients, estimate the 30day readmission rate. For NP patients, estimate the 30day readmission rate. February, 2009 February, 2009 Variability due to sampling: 7 new + 12 previous patients Variability due to measurement: 1 new readmit n re = 3 n NP = 19 3/19 = 0.16

23. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Segue: estimation error For NP patients, estimate the 30day readmission rate. For NP patients, estimate the 30day readmission rate. March, 2009 March, 2009 Variability due to sampling: 2 new + 19 previous patients Variability due to measurement: no new readmits n re = 3 n NP = 21 3/21 = 0.14

24. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Segue: estimation error For NP patients, estimate the 30day readmission rate. For NP patients, estimate the 30day readmission rate. September, 2010 September, 2010 Variability due to sampling: 6 new + 103 previous patients Variability due to measurement: no new readmits n re = 13 n NP = 109 p= 13/109= 0.12

25. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Estimate of p? Analyze the data Analyze the data –ED return within 30 days –From sample p = 13/109 = 11.9%/100 = proportion 0.119 –Point estimate of p = 0.119

26. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Answer the question For NP patients, estimate the 30day readmission rate. For NP patients, estimate the 30day readmission rate. The population has a parameter—call it π —we are trying to estimate—using data. The population has a parameter—call it π —we are trying to estimate—using data. In the team with the CACNP had a 30-day emergency department return rate of 11.9%

27. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Classic Steps What’s the question? (Introduction) What’s the question? (Introduction) –Conceptualize the population –State the question How will you answer the question? (Methods) How will you answer the question? (Methods) –The sample –The measurements –Analysis technique What does it mean? (Discussion) What does it mean? (Discussion) Answer the question (Results) Answer the question (Results) –The sample –The measurements –Analysis technique

28. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Defining the research question: Testable consequence? Conceptual progression from general to specific Conceptual progression from general to specific General question General question –Determine the impact on utilization outcomes of NPs on medical teams for cardiovascular intensive care patients Specific hypothesis Specific hypothesis –Is the 30d readmission rate lower in NP patients than in those not cared for by an NP? Testable consequence Testable consequence –Prediction of a relationship –Potentially refutable by data

29. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Defining the research question: Prediction Prediction: a statistical relationship between intervention and outcome Prediction: a statistical relationship between intervention and outcome –Readmission will be lower in NP patients than in controls How can we arrive at this conclusion? Using a refutable hypothesis How can we arrive at this conclusion? Using a refutable hypothesis

30. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Defining the research question: Formalization A refutable hypothesis A refutable hypothesis Statistical formalization: Statistical formalization: Ho: proportion readmit(staff+NP) = proportion readmit(staff) Ho: proportion readmit(staff+NP) = proportion readmit(staff) –Which may be disproved beyond a reasonable doubt through falsification by data via statistical hypothesis testing, in favor of: Ha: proportion readmit(staff+NP) < proportion readmit(staff) Ha: proportion readmit(staff+NP) < proportion readmit(staff)

31. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Critical Appraisal Is it a testable, research question? Is it a testable, research question? How did they try to rule out bias, confounding, chance? How did they try to rule out bias, confounding, chance? How did they consider multiple outcome measures and multiple predictors? How did they consider multiple outcome measures and multiple predictors? Did they disclose what was done with enough detail so others may replicate? Did they disclose what was done with enough detail so others may replicate?

32. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y How Science Advances Clinical Knowledge Science forms a question and brings data to bear to answer the question. Science forms a question and brings data to bear to answer the question. Informally: Informally: 1.Frame a clinical research question. 2.State its testable consequences as either “just random variability” or “unusual outcomes”. 3.Compare the actual data with these two choices and decide which to believe. 4.Discuss our present understanding.

33. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Testing Hypotheses Or, linking the four steps to the standard IMRD organization of a paper: Or, linking the four steps to the standard IMRD organization of a paper: 1.What’s the question? (Introduction) 2.How do you answer the question? (Methods) 3.Answer the question. (Results) 4.What does it mean? (Discussion)

34. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y What’s the Question? “The purpose of this study was to determine if the addition of a cardiac acute care NP to care team could improve utilization outcomes (ie, time of discharge, length of stay, and readmission rates …” “The purpose of this study was to determine if the addition of a cardiac acute care NP to care team could improve utilization outcomes (ie, time of discharge, length of stay, and readmission rates …” From the Objective of David, et al. (2014) J Cardiovasc Nurs.

35. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y How do you answer the question? Outline Outline –Propose two states of nature –Use the rule of simplicity –Take into account that “noise happens” –Use a test statistic to decide: Signal or Noise?

36. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Two states; Two hypotheses We begin by conceiving the true state of nature as being either: We begin by conceiving the true state of nature as being either: –no difference or –a difference. We always start by assuming that nothing is going on— that any apparent differences are purely because of chance. Our preference, as scientists, is to believe the simplest explanation for a phenomenon. We always start by assuming that nothing is going on— that any apparent differences are purely because of chance. Our preference, as scientists, is to believe the simplest explanation for a phenomenon. –Assume: no difference (AKA null hypothesis).

37. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Rule of Simplicity When you have two competing theories which make exactly the same predictions, the one that is simpler is the better. When you have two competing theories which make exactly the same predictions, the one that is simpler is the better. The simplest explanation for some phenomenon is more likely to be accurate than more complicated explanations. The simplest explanation for some phenomenon is more likely to be accurate than more complicated explanations. The explanation requiring the fewest assumptions is most likely to be correct. The explanation requiring the fewest assumptions is most likely to be correct. AKA “Occam’s Razor”

38. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Statistical World View Thou shalt not interpret randomness. Thou shalt not interpret randomness. Chance happens. Noise exists. Making an interpretation that goes beyond this requires justification. Chance happens. Noise exists. Making an interpretation that goes beyond this requires justification. If random noise, measurement error, or chance occurrence can account for variations (differences) in the observations, then there is no need to formulate a more complicated explanation. If random noise, measurement error, or chance occurrence can account for variations (differences) in the observations, then there is no need to formulate a more complicated explanation. –We embody this preference in the statement of the null hypothesis.

39. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Null Hypothesis We evaluate this proposition using statistical techniques. We evaluate this proposition using statistical techniques. The null hypothesis is the statement that is tested. It’s abbreviated H0: The null hypothesis is the statement that is tested. It’s abbreviated H0: A null-hypothesis is the simplest explanation of events: There is no difference. There is no change. There is no improvement. Nothing unusual is occurring. A null-hypothesis is the simplest explanation of events: There is no difference. There is no change. There is no improvement. Nothing unusual is occurring. A null-hypothesis is the statement we hope to contradict with data. That is, we usually hope to reject the null hypothesis. A null-hypothesis is the statement we hope to contradict with data. That is, we usually hope to reject the null hypothesis.

40. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Assume: Nothing is going on Proportion readmitted within those cared for by the NP is equal to the proportion readmitted within those not cared for by the NP. Proportion readmitted within those cared for by the NP is equal to the proportion readmitted within those not cared for by the NP. HO: π staff+NP = π staff HO: π staff+NP = π staff

41. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Two Hypotheses Proportion readmitted within those cared for by the NP is equal to the proportion readmitted within those not cared for by the NP. Proportion readmitted within those cared for by the NP is equal to the proportion readmitted within those not cared for by the NP. HO: P staff+NP = P staff HO: P staff+NP = P staff Can we reject the above, in favor of: Proportion readmitted within those cared for by the NP is different than the proportion readmitted within those not cared for by the NP. Proportion readmitted within those cared for by the NP is different than the proportion readmitted within those not cared for by the NP. Ha: P staff+NP ≠ P staff Ha: P staff+NP ≠ P staff So: Done with step 1: We’ve stated the question. Next: How will you answer the question?

42. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Proof By Contradiction Nature is either in one state or the other. Nature is either in one state or the other. –We prefer to believe the simplest explanation. Collect data from the real world. Collect data from the real world. Assess the likelihood of observing this data under the null hypothesis. Assess the likelihood of observing this data under the null hypothesis. Choose to believe: Choose to believe: –If the data is within what we would expect then we retain our preference for the null-hypothesis as the best explanation. –If the data is very unlikely, then we reject the null hypothesis in favor of its alternative.

43. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y What if: HO is true? In staff+NP patients, estimate the proportion readmitted. In staff+NP patients, estimate the proportion readmitted. In staff patients, estimate the proportion readmitted. In staff patients, estimate the proportion readmitted. π = π staff+NP = π staff

44. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y What if? Assess the likelihood of observing various data possibilities under the null hypothesis. Assess the likelihood of observing various data possibilities under the null hypothesis. Assume this is true: Assume this is true: –HO: P staff+NP = P staff Then the sample estimate of P staff+NP will be “close to” the sample estimate of P staff. Then the sample estimate of P staff+NP will be “close to” the sample estimate of P staff. –By “close to” we mean that, because of sampling variability and measurement error we expect them to be somewhat different.

45. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Contingency Table “Results: We included 185 patients in this study. …

46. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Contingency Table Assume this is true: Assume this is true: –HO: P staff+NP = P staff = 0.173

47. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Contingency Table Assume this is true: Assume this is true: –HO: P staff+NP = P staff = 0.173 –17.3% of 109 = 18.9 19

48. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Contingency Table Assume this is true: Assume this is true: –HO: P staff+NP = P staff = 0.173 –17.2% of 76 = 13.1 13

49. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Contingency Table Assume this is true: Assume this is true: –HO: P staff+NP = P staff = 0.173 –109 – 19 = 90 –76 – 13 = 63

50. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Test Statistic Test stat: Difference in prevalence Test stat: Difference in prevalence –HO: HO: P staff+NP = P staff = 0.173 –Expected difference = 0%

51. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Testing Hypotheses Recall: Recall: –Frame a clinical research question. –State its testable consequences as either “just random variability” or “unusual outcomes”. –Compare the actual data with these two choices and decide which to believe. –Discuss our present understanding.

52. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y I presume the null-hypothesis is true, do the data support this? Observed difference = 0.3% Observed difference = 0.3% P-value = 0.9549 P-value = 0.9549

53. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y I presume the null-hypothesis is true, do the data support this? Observed difference = 1.4% Observed difference = 1.4% P-value = 0.736 P-value = 0.736

54. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y I presume the null-hypothesis is true, do the data support this? Observed difference = 3.7% Observed difference = 3.7% P-value = 0.464 P-value = 0.464

55. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y I presume the null-hypothesis is true, do the data support this? Observed difference = 42% Observed difference = 42% P-value < 0.001 P-value < 0.001

56. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Trade offs Alpha = Type I error = prob. of rejecting a true null hypothesis Beta = Type II error = prob. of not finding a true difference Conclusion Truth Do not reject null-hypothesis (p-value >.05) Reject null- hypothesis (p-value <.05) Null-hypothesis (no difference) correctType I error Alternative hypothesis (difference) Type II errorcorrect

57. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Significance Level The significance level is represented by the Greek symbol “alpha”, α. It is the probability of rejecting a true null hypothesis. The researcher chooses the risk of making this error: concluding that the null hypothesis is false when it really is true. – –The most typical values are α =.05,.01, or.10.

58. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Universal Decision Rule Choose to believe: Choose to believe: HO: null-hypothesis HO: null-hypothesis (For non-extreme values of the test statistic) –Choose this if p-value ≥ (usually 0.05) –Choose this if p-value ≥ α (usually 0.05) HA: alternative-hypothesis HA: alternative-hypothesis (For extreme values of the test statistic) –Choose this if p-value <(usually 0.05) –Choose this if p-value < α (usually 0.05)

59. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y ! Are we done yet? ! What’s the question? (Introduction) What’s the question? (Introduction) –Conceptualize the population –State the question How will you answer the question? (Methods) How will you answer the question? (Methods) –The sample –The measurements –Analysis technique What does it mean? (Discussion) What does it mean? (Discussion) Answer the question (Results) Answer the question (Results) –The sample –The measurements –Analysis technique

60. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y I presume the null-hypothesis is true, do the data support this? Observed difference = 13.1% Observed difference = 13.1% P-value = 0.0207 P-value = 0.0207

61. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Chi-square test Expected data Expected data Observed data (chi-square, P-value = 0.0207) Observed data (chi-square, P-value = 0.0207) Table 2: P =.011

62. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y State a Conclusion Proportion readmitted within those cared for by the NP is equal to the proportion readmitted within those not cared for by the NP. Proportion readmitted within those cared for by the NP is equal to the proportion readmitted within those not cared for by the NP. HO: P staff+NP = P staff HO: P staff+NP = P staff Can we reject the above, in favor of: Proportion readmitted within those cared for by the NP is different than the proportion readmitted within those not cared for by the NP. Proportion readmitted within those cared for by the NP is different than the proportion readmitted within those not cared for by the NP. Ha: P staff+NP ≠ P staff Ha: P staff+NP ≠ P staff Evidence: 11.9% readmit vs 25% readmit (P =.0207)

63. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y P-value The p-value is the probability that the data occurred by chance, assuming the null hypothesis is true. The p-value is the probability that the data occurred by chance, assuming the null hypothesis is true. The p-value is NOT the probability that the null- hypothesis is true. The p-value is NOT the probability that the null- hypothesis is true.

64. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y The p-value is NOT the probability that the null-hypothesis is true. (and 1−pvalue is NOT the probability that the alternative hypothesis is true)

65. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Trade offs Alpha = Type I error = prob. of rejecting a true null hypothesis Beta = Type II error = prob. of not finding a true difference Conclusion Truth Do not reject null-hypothesis (p-value >.05) Reject null- hypothesis (p-value <.05) Null-hypothesis (no difference) correctType I error Alternative hypothesis (difference) Type II errorcorrect

66. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Actuality Alpha = Type I error = prob. of rejecting a true null hypothesis Beta = Type II error = prob. of not finding a true difference Results Truth Do not reject null-hypothesis (p-value >.05) Reject null- hypothesis (p-value <.05) Null-hypothesis (no difference) Blind alley ? Alternative hypothesis (difference)?Discovery!

67. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y P-value A modest reality: A modest reality: The p-value is simply the probability that the data occurred by chance. Big leap: Big leap: A significant p-value is a license to make up a story.

68. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Discussion “The addition of a CACNP to a multidisciplinary inpatient medical team caring for myocardial infarction and heart failure patients was associated with lower 30-day emergency department readmission and 30-day hospital readmission rates.” “The addition of a CACNP to a multidisciplinary inpatient medical team caring for myocardial infarction and heart failure patients was associated with lower 30-day emergency department readmission and 30-day hospital readmission rates.”Conclusion “It is recommended that CACNPs be considered for patient teaching, care coordination, and multidisciplinary integration to reduce costly rehospitalizations of patients with heart failure and myocardial infarction.” * See page 443, the last Discussion paragraph. “It is recommended that CACNPs be considered for patient teaching, care coordination, and multidisciplinary integration to reduce costly rehospitalizations of patients with heart failure and myocardial infarction.” * See page 443, the last Discussion paragraph.

69. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y Review To assess validity, ask: To assess validity, ask: –What’s the question? –Where did the data come from? (Sampling and measurement.) –What would you expect if “nothing is going on”? –Is the observed data different than that? And consider that other factors could account for the observed difference And consider that other factors could account for the observed difference –Bias, confounding, multiplicity, chance

70. V I R G I N A C O M M O N W E A L T H U N I V E R S I T Y www.phdcomics.com/comics/archive.php?comicid=905Questions?