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?
- 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?
Editor's Notes
- #1: Created from: https://docs.google.com/presentation/d/12LYBNyNdJnlxc-9ItNpdSUWMSS3q2y_rvPXyotnI1eY/edit#slide=id.p