Basics in Epidemiology & Biostatistics 1 RSS6 2014
- 1. Basics in Epidemiology & Biostatistics Hashem Alhashemi MD, MPH, FRCPC Assistant Professor, KSAU-HS
- 2. Objectives • Definitions. • Types of Data. • Data summaries. • Mean Χ , Stander deviation S. • Stander Error SE, Confidence interval C.I of μ .
- 3. Epidemiology
- 4. Epidemiology
- 5. Biostatistics ?? What is the difference between the two?
- 6. Difference Biostatistics is application of statistical methods in biology, medicine and public health. Epidemiology is the study of patterns of health and illness and associated factors at the population level.
- 7. Descriptive Vs Inferential Statistics • Descriptive: Range, mean, SD, Rank, median, IQR Describe a data of a population or a sample. • Inferential: Sample, SE, CI Sample from a population, & trying to generalize your finding (make an inference about the population)
- 8. A Fancy World made of Biostatistics Averages & %s
- 9. Types of DATA • Quantitative. • Qualitative.
- 10. Quantitative Data Discrete Continuous Dichotomous: Binary: Sex Multichotomous: 1-No order : Race 2-Ordinal: Education Numerical: number pregnancies/residents Ratio (real zero) / Interval (no zero) Temperature/BP (Non-Parametric Data)
- 11. Quantitative Data Discrete Continuous Categorical : 1- Di-chotomous: Sex 2- Multi-chotomous: Race,Education Numerical: number of pregnancies/residents Ratio (real zero) / Interval (no zero) Temperature/BP Types of Data Count Non-Parametric Data Parametric Data Parametric Data
- 13. Summaries Visual Numerical X, 𝛍, s, 𝛔Histogram P, 𝛑, s, 𝛔Bar & Pie Chart (Counts) Categories (Measures) Any value
- 15. Controlled PB ˂ 140/90 Non adherence (12 %) Aadherence (88 %) (63 %) (37 %) Continuous Discrete %%
- 16. A Fancy World made of %s & Averages Biostatistics
- 17. Normality & Approximation to Normality
- 18. Normality Continuous Data Variability Height= Mean X Width= SD, SE Central Tendency & Dispersion
- 19. Binary Data approximation to Normality Death Life
- 20. Coin: Head Vs Tail Discrete: Two categories
- 27. Normality & Approximation to Normality Why?
- 28. Approximation to Normality • If choices are equally likely to happen • If repeated numerous number of times • It will look normal. • Whether it was a coin or a dice (Di-chotomous or Multi-chotomous)
- 29. Normality & Approximation to Normality Clinical Relevance?
- 30. Choices equally likely to happen….. i.e. Out come of interest probability is unknown (Research ethics) Repeated numerous number of times…. i.e. Large sample size Normality assumption helps us predict the Probability % of our outcome
- 31. The Bell / Normal curve Stander deviation(SD)/ sample curve True error (SE)/ population curve • Was first discovered by Abraham de Moivre in 1733. • The one who was able to reproduce it and identified it as the normal distribution (error curve) was Gauss in 1809.
- 32. De Moivre had hoped for a chair of mathematics, but foreigners were at a disadvantage, so although he was free from religious discrimination, he still suffered discrimination as a Frenchman in England. Born 1667 in Champagne, France Died 1754 in London, England
- 33. • Large samples > 30. • Normally distributed. • Descriptive statistics: Range, Mean, SD. Non-parametric data • For small samples & variables that are not normally distributed. • No basic assumptions (distribution free). • Descriptive statistics: Range, Rank, Median, & the interquartile range. (the middle 50 = Q3-Q1). • Median is the middle number in a ranked list of numbers. Parametric data
- 34. The End
- 35. Why?
- 36. Different types of animals Need different ways of care Different types of Data Summaries and analysis are different
- 37. Qualitative Studies/data • Importance (Humanities): needed when trying to find justifications, explanations, opinions regarding the subject of interest. • Examples: Emotions, Perceptions, Pictures. • How: Asking open ended questions (interviews), observing behaviors….
- 38. Quantitative Studies/Data • Importance (Science): For measurements and/or estimation. • Examples: Measurable & countable data (real numbers). • How: Observation, Comparison, Intervention, Correlation.
- 39. BP ≥ 140/90 Uncontrolled Controlled PB ˂ 140/90 Non adherence (12 %) Aadherence (88 %) (63 %) (37 %) Figure 2: Non-adherence to medications and blood pressure control.