This document provides an introduction to biostatistics. It discusses key concepts like study populations, samples, systematic error, confounding, and true associations. It also outlines 9 common research questions and the PICOT framework for defining analytical studies. The document reviews variables, steps in data analysis including descriptive and inferential statistics, and statistical tests for different study designs. It discusses factors to consider when choosing a statistical test like the combination of variables, normality, number of groups, and independence. Finally, it briefly introduces concepts like type I error, power, p-values, and regression analysis.

1. Introduction to Biostatistics
Dr. Inaamul Haq
Assistant Professor, Department of Community Medicine
Government Medical College, Srinagar
MD, Community Medicine (JNMC, AMU, Aligarh)
Advanced Program in Clinical Research and Management (CliniIndia)
Certificate in Research Methods(PHFI)
Basic and Advanced Epidemiology and Biostatistics(PGISPH)
MECOR1

2. The Physiology of Research
STUDY
POPULATION
Research Question
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SAMPLE
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Inference

3. The four possible explanations of an
observed association
1. Systematic Error (Bias)
2. Confounding
3. Random Error
4. True Association
Association does not guarantee causation

4. The 9 Research Questions
1. What is the prevalence of a condition?.
2. What is the average (Mean) of a characteristic?
3. What is the strength of correlation between two
quantitative parameters?
4. What is the agreement between methods?
5. What are the diagnostic characteristics of a candidate
test (categorical/quantitative) with reference to a “Gold
Standard”?
6. What is the incidence of an outcome?
7. What are the predictors of an outcome?
8. What are the risk factors associated with an outcome?
9. Evaluation of a candidate intervention against a control
(standard of care)?

5. The Research Question
in Analytical Studies
• (P) – Population (the sample of subjects you wish to
recruit for your study)
• (I) – Intervention (the treatment that will be provided
to subjects enrolled in your study)
• (C) – Comparison (what you plan on using as a
reference group to compare with your treatment
intervention)
• (O) – Outcome (what result you plan on measuring to
examine the effectiveness of your intervention)
• (T) – Time (the time frame over which the outcomes
are assessed)
P I C O T
P I C O T

6. Some examples of PICOT
• In adult patients with knee osteoarthritis (P), how effective
is Hijamah therapy (I) compared to local 1% diclofenac gel
(C) in reducing pain (O) after 6 weeks of therapy (Time)?
• In pregnant females (P) , is the incidence of Pregnancy
Induced Hypertension (O) higher among those with a
family history of clinical hypothyroidism (I) compared to
those without any such family history (C)?
• For patients 65 years and older (P), how does the use of an
influenza vaccine (I) compared to not received the vaccine
(C) influence the risk of developing pneumonia (O) during
flu season (T)?

7. Variables in Medical Research
•Quantitative
–Blood Sugar, Systolic BP, Age, Weight etc.
•Categorical
–Sex, Disease, Residence etc.
• Outcome / Dependent / Response
• Exposure / Predictor / Explanatory / Independent
• Other variables

8. Steps in Data Analysis
Data Entry
Data Cleaning
Data Exploration
Descriptive
Statistics
Inferential
Statistics

9. Data Entry

10. Spreadsheetfromhell

11. spreadsheetfromheaven

12. Data Cleaning and Data Exploration
• Frequency tables
• Histograms
• Cross-tabulations
• Scatterplots

17. Descriptive Statistics
• Tables
– Frequency tables
– Cross-tabulations/Contingency tables
• Graphs
– Univariable graphs – Simple Bar Charts
– Relational graphs – Stacked Bars, Scatterplots, Boxplots
• Numbers
– Continuous variables, Normal distribution
• Mean and SD
– Continuous variables, Not normally distributed
• Transformation – Mean and SD
– Continuous variables, Not normally distributed / Discrete
measurements
• Five-number summary (Min, 1st quartile, Median, 3rd quartile, Max)

18. Maximum
Minimum
Median
1st Quartile
3rd Quartile

19. Inferential Statistics
Estimation
–Point estimation
–Interval estimation
(Confidence Interval)
Hypothesis Testing

20. Three ways of data analysis
1. Univariable Analysis
2. Bivariable Analysis
1. Categorical versus Categorical
2. Categorical versus Quantitative
3. Quantitative versus Quantitative
3. Multi-variable Analysis

21. Statistical measures for the 9 questions
1. Proportion
2. Mean, SD
3. Correlation
4. Agreement
5. Sensitivity, Specificity etc
6. Incidence
7. Risk ratio
8. Odds ratio
9. Effect size

22. FOUR points to consider to choose an
appropriate statistical test
1. Combination of two variables
2. Normal or Non-normal
3. Groups: 2 or >2
4. Related or not related

23. 1. Combination of two variables
• Categorical versus
Categorical
– Chi-square test
– Corrected Chi-square
– Fisher’s Exact Test
– McNemar test
• Categorical versus
Quantitative
– T-TEST
– ANOVA
– Wilcoxon ranksum
test
– Kruskall-Wallis test
– Wilcoxon signed rank
test

24. 1. Combination of two variables ...
• Quantitative versus Quantitative
– Pearsons Correlation coefficient
– Spearmans correlation coefficient

25. 2. Normal or non-normal
• Histogram
• Mean versus Median
• Statistical tests and plots
– QQ plot
– Shapiro-Wilk test

27. 2. Normal or non-normal ...
• Normal
– T-TEST
– ANOVA
– Repeated-measures
ANOVA
• Non-normal
– Wilcoxon ranksum
test
– Wilcoxon signed rank
test
– Kruskall-Wallis test

28. 3. Groups: 2 or >2
• 2 groups
– T-TEST
– Wilcoxon ranksum
test
– Wilcoxon signed rank
test
• >2 groups
– ANOVA
– Kruskall-Wallis test

29. 4. Related or not related
• Related
– Paired T-TEST
– Wilcoxon signed rank
test
– McNemar test
– Repeated measures
ANOVA
– Friedman test
• Not related
– T-test
– Pearson Chi-square
test
– Wilcoxon ranksum
test
– Kruskall-Wallis test

30. Combination of two variables
Categorical vs
Categorical
Quantitative vs
Quantitative
Related
Not
related
McNemar
•Chi-square
•Fisher’s
exact
Pearsons
Correlation
Spearmans
Correlation
Normal
Not
normal

31. Combination of two variables
Categorical vs
Quantitative
Normal
Not
normal
2 groups >2 Groups
Related
Not
related
Related
Not
related
Normal
Not
normal
Related
Not
related
Related
Not
related
Paired t-test
Unpaired
t-test
Wilcoxon
signed rank
Wilcoxon
ranksum
Repeated
measures
ANOVA One-way
ANOVA
Friedmann
Kruskall
Wallis

33. • Type I error – False Positive
• Type II error – False Negative
• Power – True Positive

34. The p-value
• The P value, or calculated probability, is the
probability of finding the observed, or more
extreme, results when the null hypothesis
(H 0) of a study question is true
Random Error

35. Regression Analysis
• Linear Regression – Continuous outcome
• Logistic Regression – Dichotomous outcome

36. https://drive.google.com/file/d/1zWty8vNTL0B7
5oOar6oghpSnzdcn-qxf/view?usp=sharing

37. THANK YOU