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Basics in Epidemiology & Biostatistics
Hashem Alhashemi MD, MPH, FRCPC
Assistant Professor, KSAU-HS
Objectives
• Definitions.
• Types of Data.
• Data summaries.
• Mean Χ , Stander deviation S.
• Stander Error SE, Confidenc...
Epidemiology
Epidemiology
Biostatistics ??
What is the difference between the two?
Difference
 Biostatistics is application of statistical
methods in biology, medicine and public
health.
 Epidemiology is...
Descriptive Vs Inferential Statistics
• Descriptive: Range, mean, SD, Rank, median, IQR
Describe a data of a population or...
A Fancy World made of
Biostatistics
Averages & %s
Types of DATA
• Quantitative.
• Qualitative.
Quantitative
Data
Discrete
Continuous
Dichotomous:
Binary: Sex
Multichotomous:
1-No order : Race
2-Ordinal: Education
Nume...
Quantitative
Data
Discrete
Continuous
Categorical :
1- Di-chotomous:
Sex
2- Multi-chotomous:
Race,Education
Numerical:
num...
Data Summaries??
Exams/memories
Understand/view
Summaries
Visual Numerical
X, 𝛍, s, 𝛔Histogram
P, 𝛑, s, 𝛔Bar & Pie Chart (Counts)
Categories
(Measures)
Any value
Data Presentation
%%
Controlled
PB ˂ 140/90
Non
adherence
(12 %)
Aadherence
(88 %)
(63 %)
(37 %)
Continuous Discrete
%%
A Fancy World made of
%s & Averages
Biostatistics
Normality & Approximation to Normality
Normality
Continuous Data Variability
Height= Mean X
Width= SD, SE
Central Tendency &
Dispersion
Binary Data approximation to Normality
Death Life
Coin: Head Vs Tail
Discrete: Two categories
Discrete Data
36 categories
Normality & Approximation to Normality
Why?
Approximation to Normality
• If choices are equally likely to happen
• If repeated numerous number of times
• It will look...
Normality & Approximation to Normality
Clinical Relevance?
Choices equally likely to happen…..
i.e. Out come of interest probability is unknown
(Research ethics)
Repeated numerous n...
The Bell / Normal curve
Stander deviation(SD)/ sample curve
True error (SE)/ population curve
• Was first discovered by Ab...
De Moivre had hoped for a chair of
mathematics, but foreigners were at a
disadvantage, so although he was free
from religi...
• Large samples > 30.
• Normally distributed.
• Descriptive statistics:
Range, Mean, SD.
Non-parametric data
• For small s...
The End
Why?
Different types of animals
Need different ways of care
Different types of Data
Summaries and analysis are different
Qualitative Studies/data
• Importance (Humanities):
needed when trying to find justifications,
explanations, opinions rega...
Quantitative Studies/Data
• Importance (Science):
For measurements and/or estimation.
• Examples:
Measurable & countable d...
BP ≥ 140/90
Uncontrolled
Controlled
PB ˂ 140/90
Non
adherence
(12 %)
Aadherence
(88 %)
(63 %)
(37 %)
Figure 2: Non-adheren...
Basics in Epidemiology & Biostatistics 1 RSS6 2014
Basics in Epidemiology & Biostatistics 1 RSS6 2014
Basics in Epidemiology & Biostatistics 1 RSS6 2014
Basics in Epidemiology & Biostatistics 1 RSS6 2014
Basics in Epidemiology & Biostatistics 1 RSS6 2014
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Transcript of "Basics in Epidemiology & Biostatistics 1 RSS6 2014"

  1. 1. Basics in Epidemiology & Biostatistics Hashem Alhashemi MD, MPH, FRCPC Assistant Professor, KSAU-HS
  2. 2. Objectives • Definitions. • Types of Data. • Data summaries. • Mean Χ , Stander deviation S. • Stander Error SE, Confidence interval C.I of μ .
  3. 3. Epidemiology
  4. 4. Epidemiology
  5. 5. Biostatistics ?? What is the difference between the two?
  6. 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. 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. 8. A Fancy World made of Biostatistics Averages & %s
  9. 9. Types of DATA • Quantitative. • Qualitative.
  10. 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. 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
  12. 12. Data Summaries?? Exams/memories Understand/view
  13. 13. Summaries Visual Numerical X, 𝛍, s, 𝛔Histogram P, 𝛑, s, 𝛔Bar & Pie Chart (Counts) Categories (Measures) Any value
  14. 14. Data Presentation %%
  15. 15. Controlled PB ˂ 140/90 Non adherence (12 %) Aadherence (88 %) (63 %) (37 %) Continuous Discrete %%
  16. 16. A Fancy World made of %s & Averages Biostatistics
  17. 17. Normality & Approximation to Normality
  18. 18. Normality Continuous Data Variability Height= Mean X Width= SD, SE Central Tendency & Dispersion
  19. 19. Binary Data approximation to Normality Death Life
  20. 20. Coin: Head Vs Tail Discrete: Two categories
  21. 21. Discrete Data 36 categories
  22. 22. Normality & Approximation to Normality Why?
  23. 23. 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)
  24. 24. Normality & Approximation to Normality Clinical Relevance?
  25. 25. 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
  26. 26. 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.
  27. 27. 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
  28. 28. • 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
  29. 29. The End
  30. 30. Why?
  31. 31. Different types of animals Need different ways of care Different types of Data Summaries and analysis are different
  32. 32. 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….
  33. 33. Quantitative Studies/Data • Importance (Science): For measurements and/or estimation. • Examples: Measurable & countable data (real numbers). • How: Observation, Comparison, Intervention, Correlation.
  34. 34. 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.
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