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, Confidence interval C.I of μ .

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 the study of patterns of health and illness and associated factors at the population level.

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)

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

Numerical: number pregnancies/residents

Ratio (real zero) /

Interval (no zero)

Temperature/BP

(Non-Parametric Data)

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

Data Summaries??

Exams/memories

Understand/view

Summaries

Visual Numerical

X, 𝛍, s, 𝛔 Histogram

P, 𝛑, s, 𝛔 Bar & 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 normal.

• Whether it was a coin or a dice

(Di-chotomous or Multi-chotomous)

Normality & Approximation to Normality

Clinical Relevance?

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

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.

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

• 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

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 regarding the subject of

interest.

• Examples:

Emotions, Perceptions, Pictures.

• How:

Asking open ended questions (interviews),

observing behaviors….

Quantitative Studies/Data

• Importance (Science): For measurements and/or estimation.

• Examples: Measurable & countable data (real numbers). • How: Observation, Comparison, Intervention, Correlation.

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.