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- 1. Statistics for Your Research Proposal SohailBajammal, MBChB, MSc, FRCS(C), PhD(c) Assistant Professor of Orthopaedics, Umm Al-Qura UniversityDirector of CME & Research Administration, King Abdullah Medical City Makkah bajammal
- 2. What Statistics do you need to know for your proposal?• Types of Variables• Types of Statistics• How to Choose a Statistical Test?• Hypothesis Testing & Sample Size
- 3. Patho Physiology of Research Research Study Plan Actual Study Question Design Implement Target Intended Actual subjects Population Sample Errors Errors Actual Phenomena Intended measurements of interest variablesTruth in the Truth in the Findings in Universe Infer Study Infer the Study External Validity Internal Validity From Hulley et al. Designing Clinical Research. LWW
- 4. Variables
- 5. Variables• Anything whose value can vary• Dependent (response) variable: outcome• Independent (explanatory) variables: predictors• Confounding variable: associated with the independent variable and a cause of the dependent variable – coffee drinking (independent), MI (dependent) – cigarette smoking (confounder),
- 6. Types of Variables Categorical Quantitative (Continuous or “Qualitative” Discrete)Nominal Ordinal Interval RatioNationality Stage I, II, III Temperature Pulse Gender & IV cancer (0 is not zero) (0 is dead)
- 7. Types of Variables Categorical Quantitative (Continuous or “Qualitative” Discrete)Nominal Ordinal Interval RatioNationality Stage I, II, III Temperature Pulse Gender & IV cancer (0 is not zero) (0 is dead)
- 8. Types of Variables Categorical Quantitative (Continuous or “Qualitative” Discrete)Nominal Ordinal Interval RatioNationality Stage I, II, III Temperature Pulse Gender & IV cancer (0 is not zero) (0 is dead)
- 9. Types of Variables Categorical Quantitative (Continuous or “Qualitative” Discrete)Nominal Ordinal Interval RatioNationality Stage I, II, III Temperature Pulse Gender & IV cancer (0 is not zero) (0 is dead)
- 10. Implications of Types of Variable• Details of data collection forms – Age: • ……. Years Ratio • <20yr, 21-30yr, 31-40yr, >40 Ordinal• Choice of statistical analysis test• Coding in the statistical software: 1,2 for nominal – Does not make sense to have a mean for nominal• BIAS if you didn’t consider confounders
- 11. When deciding on your variables• Think of patient-oriented outcomes, instead of disease-oriented outcomes• Studying the effect of new drugs on arrhythmia, which is better as outcome? – Number of arrhythmia – Frequency of palpitation/Quality of Life
- 12. Statistics
- 13. Statistics deals with• Collection• Organization• Analysis Data• Interpretation
- 14. Types of Statistics Statistics Descriptive Inferential Statistics Statistics Measures Measures What Confidence of Central Interval of Spread test? Tendency Inter- StandardMean Median Mode quartile Deviation Range
- 15. Types of Statistics Statistics Descriptive Inferential Statistics Statistics Measures Measures What Confidence of Central Interval of Spread test? Tendency Inter- StandardMean Median Mode quartile Deviation Range
- 16. Types of Statistics Statistics Descriptive Inferential Statistics Statistics Measures Measures What Confidence of Central Interval of Spread test? Tendency Inter- StandardMean Median Mode quartile Deviation Range
- 17. Descriptive Statistics• Numerical• Tabulated• Graphs
- 18. Measures of Central Tendency• Mean: average• Mode: most frequent count• Median: the value separating the top and bottom of data (organized highest to lowest)
- 19. Measures of Central Tendency Best measure ofType of Variable central tendency Nominal Mode Ordinal Median Interval/ratio Mean (not skewed) Interval/ratio Median (skewed)
- 20. Difficult Exam Scores!!Laerd.com
- 21. Measures of Spread (Variability)• Standard Deviation• Percentile• Range &Interquartile Range
- 22. Standard Deviation Variability of values around the mean Variance Standard Deviation Courtesy of Prof. Hassan Baaqeel
- 23. Differences in Standard Deviation Bell-shaped curve 0.08 0.07 Mean = 70 SD = 5 0.06 0.05Density 0.04 Mean = 70 SD = 10 0.03 0.02 0.01 0.00 40 50 60 70 80 90 100 Grades
- 24. Normal Distribution MEAN 68 % of observations 1 SD 2 SD 95 % of observations 99.7 % of observations 3D-3 -2 -1 0 1 2 3 STANDARD DEVIATIONS Courtesy of Prof. Hassan Baaqeel
- 25. Types of Statistics Statistics Descriptive Inferential Statistics Statistics Measures Measures What Confidence of Central Interval of Spread test? Tendency Inter- StandardMean Median Mode quartile Deviation Range
- 26. Inferential Statistics• Allow for making predictions, estimations or inferences about what has not been observed (the whole population) based on what has (the sample)• Every time we use inferential statistics we risk being wrong by chance – We need a range of values to be confident (Confidence Interval)
- 27. Patho Physiology of Research Research Study Plan Actual Study Question Design Implement Target Intended Actual subjects Population Sample Errors Errors Actual Phenomena Intended measurements of interest variablesTruth in the Truth in the Findings in Universe Infer Study Infer the Study External Validity Internal Validity From Hulley et al. Designing Clinical Research. LWW
- 28. Hypothesis Testing• When we compare two groups, we are testing a hypothesis• Null Hypothesis (HO): – there is no difference between the groups – (e.g., no difference in mortality)• Alternate Hypothesis (HA): – there is a difference• We choose a statistical test to do the hypothesis testing
- 29. Chi-squareMann-Whitney Statistical Tests t-test Kruskal-Wallis ANOVA
- 30. How to Choose a Statistical Test?• Your Question: – Difference between groups or correlation/prediction• Variables: – Types: nominal, ordinal, interval or ratio – Distribution: normal or skewed• Groups: – Number: two or more – Relationship: related or un-related
- 31. Brookes University
- 32. Bates College
- 33. Probably the simplest/coolest City College Coventry
- 34. Choice of Statistical Tests Parametric Non-Parametric Tests Tests Interval/Ratio 1. Ordinal Goal (normal 2. Interval/Ratio Nominal distribution) (skewed)Comparison of 2 Unpairedt-test Mann-Whitney Uunrelated groups Chi-square TestComparison of >2 ANOVA Kruskal-Wallis HunrelatedComparison of 2 Paired t-test Wilcoxonrelated groups Binomial Sign TestComparison of >2 Repeated measures Friedmanrelated groups ANOVACorrelation Pearson’s Spearman’s rho Chi-square Test
- 35. All the inferential tests are “estimation”• We need a CONFIDENCE INTERVAL• It is a range of values that if the estimate occurs in this range, we will be confident that it is not due to chance• How much error are we willing to accept ? – α< 0.05
- 36. Paul Mathews, Design of Experiments with MINITAB, 2005, ASQ Press
- 37. Hypothesis Testing (α&β errors)Null Hypothesis: No association between predictor & outcome Truth in the Population Results in the Study Sample Association Between No Association Between Predictor & Outcome Predictor & Outcome Reject null Correct Type I error (α) hypothesis Fail to reject null Type II error (β) Correct hypothesis
- 38. Type I & II Errors• Type I (α) Error: usually 1-5% – Rejecting the null hypothesis when it is actually true – Stating that there is a difference, while in truth there is no difference• Type II (β) Error: usually 10-20% – Failing to reject null hypothesis when it is actually false – Stating that there is no difference, while in truth there is a difference – Statistical Power of the Study = 1-β
- 39. Zazzle.com
- 40. Resources
- 41. What do you need to know in statistics for your proposal?• Types of Variables• Types of Statistics• How to Choose a Statistical Test?• Sample Size bajammal

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for the KAMC course