Medical Statistics Pt 1
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The document outlines a masterclass covering key aspects of medical statistics, including the use of descriptive statistics, types of statistical errors, and common statistical tests. It discusses various statistical methods for analyzing data, such as comparisons, correlation, and regression, as well as the significance of null and alternative hypotheses. It emphasizes understanding sample vs. population and potential errors in interpreting statistical findings.
Medical Statistics Pt 1
- 1. Medical Statistics Masterclass Fastbleep Academic Masterclass #4 University Place 31 May 2011
- 2. About us James Giles Richard Salisbury MB PhD Student F1 & MRes Graduate PAL & Fastbleep Director Cochrane Author Love stats, hated contextless Bioscience geek
- 4. 1. Use of statistics 2. Statistical errors 3. Common statistical tests
- 5. 1. Use of statistics What is your question? What do you want to do?
- 6. Descriptive statistics • Averages - mean/median/mode • Frequency • Range (measures of spread) • Box-and-whisker plot • Bar chart • Pie chart
- 8. Scales of measurement Nominal Name: red, blue, green Ordinal Numerical
- 9. Scales of measurement Nominal Name: red, blue, green Ordinal Order: tumour stage Numerical
- 10. Scales of measurement Nominal Name: red, blue, green Ordinal Order: tumour stage Numerical Continuous: height Discrete: number of teeth
- 11. Nominal Variable Description: Frequency/proportion Illustration: Bar chart/pie chart
- 12. Averages Mean Median - middle number Mode - most frequent
- 13. Distributions
- 14. Distributions
- 15. Distributions
- 16. Distributions
- 17. Distributions
- 18. Averages Mean - numerical & unskewed Median - ordinal & skewed Mode - nominal & bimodal
- 19. Measures of spread Standard deviation (sd) Standard error of the mean (SEM)
- 20. Measures of spread Standard deviation (sd) mean distance from the mean
- 21. Measures of spread Standard deviation (sd) Standard error of the mean (SEM)
- 22. Measures of spread Standard error (SEM) estimate of spread of your sample mean from a true population mean SEM = sd/ n 20 Neutrophil Count / 105/ml 15 10 5 0 A ac C ad B bc D bd
- 23. feltron.com
- 25. Analytical statistics Compare - difference, higher/lower Relate - correlation, regression, agreement
- 26. Comparisons 1) One variable against a constant 2) One variable across 2 dependant groups 3) One variable across 2 independent groups 4) One variable across pairs
- 27. Comparsion one variable against a constant Is Hb level in heavy smokers higher than the average of 14? Variable: Hb level Constant: 14
- 28. Groups 2 types of groups • Dependent: one group a subset of the other • Independent: different sets
- 29. Comparison Example: one variable across 2 dependent(overlapping) groups Is serum PTH higher in severe renal failure than the average value in renal failure patients? Variable: serum PTH Groups: severe renal failure patients, all renal failure patients Is mortality rate in neck of femur fracture higher in case of cardiorespiratory co morbidity than average? Variable: mortality rate Groups: patients with neck of femur fracture and co morbidity, all patients with neck of femur fracture
- 30. Comparison Examples: One variable across 2 independent(non-overlaping) groups Is Trop T higher in patients with STE-ACS than NSTE-ACS? Variable: Trop T Groups: STE-ACS and NSTE-ACS Is success rate to control variceal haemorrhage higher in SCLEROTHERAPY than BALLOON TAMPONADE? Variable: success rate Groups: sclerotherapy and balloon tamponade
- 31. Comparison Examples (pairs): Pairs = repeated measurements: t wo measurements of a variable on one patient at different time points
- 32. Comparison Is the PEF higher after Salbutamol nebuliser in asthma patients? Variable: PEF Pairs: prior and after nebuliser Is the second-day serum lactate level higher than the third-day lactate level following antibiotic therapy in sepsis? Variable: lactate level Pairs: second and third day
- 33. Relation Association Prediction/regression Agreement
- 34. Association Association = correlation of t wo variables if one variable changes the other changes as well (in the same or opposite direction) No association = independence NOT CAUSATION
- 35. Association Association = correlation of t wo variables if one variable changes the other changes as well (in the same or opposite direction) No association = independence NOT CAUSATION
- 36. Association Two variables Examples 1. Is serum GENTAMICIN level dependent on serum CREATININE? Variables: serum Gentamicin, serum Creatinine 2. Is THROMBOLYTIC THERAPY related to the number of in- hospital DEATHS in stroke? Variables: thrombolytic therapy, number of deaths
- 37. Prediction/regression A Formula Knowing the value of one variable(s) Calculating the value of the other variable
- 38. Prediction/regression Usage: To describe the relationship Y = aX + b Y= aX2 + b Z = aX + bY + c Z = a X2 + bY3 + c Framingham CHD Risk Calculator
- 39. Prediction/regression the predicted and predictor (s) Examples: The TIMI risk score to predict odds of death in STEMI The APACHE III system to predict mortality in ICU The IMPACT models to predict 6-month disability in severe traumatic brain injury
- 41. 2. Statistical error What do my findings mean? How likely are my findings to be true?
- 42. Types of error Two types: Type I ( ) error Type II ( ) error
- 43. Hypothesis synthesis Research question/objective And the answer to three questions: What are the variables? What are the groups/pairs? What is the statistical analysis?
- 44. Example 1 • Objective: to assess the effect of oral glucocorticoid on serum IL-8 in COPD patients • Variable: serum IL-8 • Groups/Pairs: prior and following glucocorticoid oral therapy • Statistical analysis: comparison • Alternative hypothesis: there is a difference bet ween serum IL-8 prior and after glucocorticoid therapy • Null hypothesis: there is no difference bet ween serum IL-8 prior and after glucocorticoid therapy
- 45. Example 2 • Objective: to assess whether or not high levels of serum Neuron Specific Endolase (NSE) is associated with CT abnormality in head injury • Variable: serum NSE • Groups: patients with CT abnormality, patients with no CT abnormality • Statistical analysis: comparison • Alternative hypothesis: there is a difference bet ween serum NSE levels in head-injury patients with and without CT abnormality • Null hypothesis: there is no difference bet ween serum NSE levels in head-injury patients with and without CT abnormality
- 46. Example 3 • Objective: to describe the relationship of ISS and ED length of stay • Variables: ISS and ED length of stay • Groups/pairs: NIL • Statistical analysis: regression • Alternate hypothesis: the coefficient is not zero.
- 47. Sample vs. Population Population = everyone Sample = in your study
- 48. Probability of error is the following question: How certain are we that what is observed in the sample can be inferred on the actual population?
- 49. Belonje et al. (Circulation. 2010;121:245-251.) Increased Erythropoietin level is associated with increased mortality in 605 heart failure patients ( p < 0.05). Question: how true is this in the population of all heart failure patients?
- 50. Bloom et al. (Aliment Pharmacol Ther. 2004 Apr 15;19(8):871-8.) No difference in colitis activity of 48 ulcerative colitis patients who receive Tinzaparin with 52 patients who receive placebo (p = 0.84) Question: How true is this in the population of all ulcerative colitis patients?
- 51. Type I error or Definition: When the null hypothesis is rejected in the sample but is true in the population Rejecting null hypothesis, when it is true False positive result Sample Population difference no difference association no association
- 52. Type II error or Definition: When the null hypothesis is true in the sample but is false in the population Accepting null hypothesis, when it is false False negative result Sample Population no difference difference no association association
- 53. Population Sample + - outcome + c a reject null type 1 error - b d accept null type 2 error
- 54. Belonje et al. (Circulation. 2010;121:245-251.) Increased Erythropoietin level is associated with increased mortality in 605 heart failure patients ( p < 0.05). Null hypothesis: no association bet ween Erythropoietin and mortality Result: reject null hypothesis - positive finding
- 55. Reality Test outcome + - + = 0.04 -
- 56. Belonje et al. (Circulation. 2010;121:245-251.) p < 0.05 Probability of type I error < 0.05 Probability of no association in the population <0.05 Probability of no association bet ween increased erythropoietin and mortality in all heart failure patients < 0.05
- 61. Bloom et al. (Aliment Pharmacol Ther. 2004 Apr 15;19(8):871-8.) No difference in colitis activity of 48 ulcerative colitis patients who receive tinzaparin with 52 patients who receive placebo (p = 0.84) • Null hypothesis: no effect for tinzaparin Probability of • Result: true null hypothesis Type II error ?
- 62. Reality Test outcome + - + = 0.84 - =?
- 63. Bloom et al. (Aliment Pharmacol Ther. 2004 Apr 15;19(8):871-8.) Power calculation: 42 subjects in each group (treatment and placebo) Sample size: 48 patients in treatment group 52 patients in placebo group Large enough sample
- 64. Bloom et al. (Aliment Pharmacol Ther. 2004 Apr 15;19(8):871-8.) Low probability of type II error High probability of true null hypothesis in the population High probability of no effect for tinzaparin in all ulcerative colitis patients
- 66. Reality Faecal Occult Blood Test + - + 20 180 200 - 10 1820 1830 30 2000 2030 Sensitivity = 1 - beta = 1 - (10/30) = 0.66 Probability you’ll detect a real cancer
- 67. Reality Faecal Occult Blood Test + - + 20 180 200 - 10 1820 1830 30 2000 2030 Specificity = 1 - alpha = 1 - (180/2000) = 0.91 Probability you’ll reassure a healthy person
- 68. Reality Faecal Occult Blood Test + - + 20 180 200 - 10 1820 1830 30 2000 2030 Positive predictive value = 20/200 = 0.1 Probability a positive result means cancer
- 69. Reality Faecal Occult Blood Test + - + 20 180 200 - 10 1820 1830 30 2000 2030 Negative predictive value = 1820/1830 = 0.995 Probability a negative result means all clear
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