Introduction to Basic Biostatistics (Biostats)

Introduction to Basic Biostatistics (Biostats)

1. Introduction to Basic Biostatistics Anjum Yaqoob MPhil Public Health

2. What is Biostatistics? Definition: Application of statistics to biological and medical sciences Combines biology, mathematics, and statistics Used to analyze and interpret biological data Essential for medical research, public health, and clinical trials How do you think biostatistics impacts healthcare decisions?

3. Importance of Biostatistics Helps in evidence-based medicine Crucial for designing and analyzing clinical trials Aids in identifying risk factors for diseases Supports development of new drugs and treatments Enhances epidemiological studies Can you think of a recent medical breakthrough that likely involved biostatistics?

4. Types of Data in Biostatistics Categorical Data: Qualitative, grouped into categories Example: Blood types (A, B, AB, O) Continuous Data: Quantitative, can take any value within a range Example: Blood pressure, height, weight Discrete Data: Countable, whole number values Example: Number of heartbeats per minute What type of data would you collect to study the effectiveness of a new drug?

5. Measures of Central Tendency 1. Mean: Average of all values 2. Median: Middle value when data is ordered 3. Mode: Most frequent value Each measure has its strengths and weaknesses Choice depends on data distribution and research question Can you think of a situation where the median might be more appropriate than the mean?

6. Measures of Variability Range: Difference between highest and lowest values Variance: Average squared deviation from the mean Standard Deviation: Square root of variance Indicates spread of data around the mean Interquartile Range (IQR): Range of middle 50% of data Why is understanding variability important in medical studies?

7. Normal Distribution Bell-shaped, symmetrical curve Characterized by mean and standard deviation 68-95-99.7 rule: Percentage of data within 1, 2, and 3 standard deviations Many biological phenomena follow normal distribution Examples: Height, blood pressure, IQ scores Can you think of a biological trait that might not follow a normal distribution?

8. Hypothesis Testing

9. What is Biostatistics? Application of statistics to biological and medical sciences Combines biology, mathematics, and statistics Used to analyze and interpret biological data Essential for medical research, public health, and clinical trials How do you think biostatistics impacts healthcare decisions?

10. Importance of Biostatistics Helps in evidence-based medicine Crucial for designing and analyzing clinical trials Aids in identifying risk factors for diseases Supports development of new drugs and treatments Enhances epidemiological studies Can you think of a recent medical breakthrough that likely involved biostatistics?

11. Types of Data in Biostatistics Categorical Data: Qualitative, grouped into categories Example: Blood types (A, B, AB, O) Continuous Data: Quantitative, can take any value within a range Example: Blood pressure, height, weight Discrete Data: Countable, whole number values Example: Number of heartbeats per minute What type of data would you collect to study the effectiveness of a new drug?

12. Measures of Central Tendency 1. Mean: Average of all values 2. Median: Middle value when data is ordered 3. Mode: Most frequent value Each measure has its strengths and weaknesses Choice depends on data distribution and research question Can you think of a situation where the median might be more appropriate than the mean?

13. Measures of Variability Range: Difference between highest and lowest values Variance: Average squared deviation from the mean Standard Deviation: Square root of variance Indicates spread of data around the mean Interquartile Range (IQR): Range of middle 50% of data Why is understanding variability important in medical studies?

14. Normal Distribution Bell-shaped, symmetrical curve Characterized by mean and standard deviation 68-95-99.7 rule: Percentage of data within 1, 2, and 3 standard deviations Many biological phenomena follow normal distribution Examples: Height, blood pressure, IQ scores Can you think of a biological trait that might not follow a normal distribution?

15. Hypothesis Testing Process of making inferences about a population based on a sample Null hypothesis (H0) vs. Alternative hypothesis (H1) p-value: Probability of obtaining results as extreme as observed, assuming H0 is true Significance level (α): Threshold for rejecting H0 (commonly 0.05) How might hypothesis testing be used in a clinical trial for a new medication?

16. Sampling Methods Simple Random Sampling: Each member of population has equal chance of selection Stratified Sampling: Population divided into subgroups, then randomly sampled Cluster Sampling: Groups are randomly selected, then all members within group are studied Systematic Sampling: Every nth member of population is selected Why is proper sampling crucial in biostatistical studies?

17. Correlation and Causation Correlation: Measure of relationship between two variables Positive correlation: As one variable increases, so does the other Negative correlation: As one variable increases, the other decreases Causation: One variable directly influences the other Important distinction: Correlation does not imply causation Can you think of an example where two health-related factors might be correlated but not causally linked?

18. Regression Analysis Statistical method for modeling relationship between variables Simple Linear Regression: One independent variable, one dependent variable Multiple Regression: Multiple independent variables, one dependent variable Logistic Regression: Used when dependent variable is categorical How might regression analysis be used in epidemiological studies?

19. Confidence Intervals Range of values likely to contain the true population parameter Typically expressed as 95% confidence interval Wider interval indicates less precise estimate Affected by sample size and variability in data Why do you think researchers often report confidence intervals along with point estimates?

20. Statistical Power Probability of correctly rejecting null hypothesis when it is false Affected by sample size, effect size, and significance level Higher power reduces risk of Type II error (false negative) Important in designing studies and determining sample size How might low statistical power impact the conclusions of a medical study?

21. Odds Ratio and Relative Risk Odds Ratio: Compares odds of an outcome in two groups Relative Risk: Ratio of probability of an outcome in exposed vs. unexposed groups Both used in case-control and cohort studies Help quantify association between exposure and outcome In what type of study would you be more likely to use odds ratio vs. relative risk?

22. Survival Analysis Statistical method for analyzing time-to-event data Used in clinical trials, epidemiology, and other fields Key concepts: Survival function, hazard function, censoring Common techniques: Kaplan-Meier estimator, Cox proportional hazards model How might survival analysis be useful in studying the effectiveness of a new cancer treatment?

23. Meta-Analysis Statistical method for combining results from multiple studies Increases statistical power and improves estimate precision Helps resolve conflicts between different studies Important in evidence-based medicine and systematic reviews Why might meta-analysis be particularly valuable in fields with many small-scale studies?

24. Bias and Confounding Bias: Systematic error in study design or data collection Types: Selection bias, information bias, recall bias Confounding: Presence of an extraneous variable that correlates with both exposure and outcome Both can lead to incorrect conclusions if not addressed Can you think of a potential source of bias in a study on the effects of diet on heart disease?

25. Ethical Considerations in Biostatistics Protecting patient privacy and confidentiality Ensuring informed consent in clinical trials Addressing potential conflicts of interest Responsible reporting of results, including negative findings Ethical use of placebo controls Why is it important to consider ethics in biostatistical research?

26. Applications of Biostatistics Clinical trials for new drugs and treatments Epidemiological studies of disease spread Public health policy and decision-making Genomics and personalized medicine Health services research and quality improvement Can you think of a recent public health decision that likely relied on biostatistical analysis?

27. Future Trends in Biostatistics Big data analytics in healthcare Machine learning and artificial intelligence applications Integration of -omics data (genomics, proteomics, etc.) Real-time data analysis for personalized medicine Advanced computational methods for complex biological systems How do you think these trends might change healthcare in the coming years?