7.4 Introduction to biostatistics

Biostatistics is the backbone of public health research, using math to make sense of health data. It's crucial for spotting trends, assessing risks, and evaluating interventions that impact population health.

In this section, we'll cover key concepts like probability, sampling, and statistical methods. Understanding these tools helps public health pros make informed decisions and tackle complex health challenges effectively.

Biostatistics in Public Health

Definition and Applications

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• Biostatistics applies statistical methods to biological and health-related data to analyze and interpret research findings
• Involves designing studies, collecting and analyzing data, and interpreting results in public health and medical research
• Plays crucial role in evidence-based decision-making for public health policy and practice
• Used to identify trends, assess risk factors, evaluate interventions, and predict health outcomes in populations
• Encompasses various subfields (clinical trials, epidemiological studies, health services research)
• Ensures validity and reliability of research findings in public health and medicine

Collaboration and Interdisciplinary Nature

• Biostatisticians collaborate with epidemiologists, clinicians, and other health professionals
• Work together to design studies and analyze complex health data
• Integrate statistical expertise with domain knowledge from various health fields
• Contribute to multidisciplinary research teams in academic and clinical settings
• Provide statistical support for grant proposals and research publications
• Develop and implement statistical software and tools for health research

Basic Statistical Concepts

Probability and Sampling

• Probability measures likelihood of an event occurring, expressed as number between 0 and 1
  • 0 indicates impossibility, 1 indicates certainty
  • Examples: probability of developing a disease, effectiveness of a treatment
• Sampling selects subset of individuals from larger population to make inferences
  • Random sampling gives each member equal chance of selection, reducing bias (lottery drawing)
  • Stratified sampling divides population into subgroups (strata) and selects samples from each (age groups in a health survey)

Distributions and Measures of Central Tendency

• Distribution describes pattern of data values in population or sample
  • Normal distribution (bell curve) symmetrical with most values clustered around mean
  • Skewed distributions asymmetrical (right-skewed or left-skewed)
    • Examples: income distribution (right-skewed), age at death (left-skewed)
• Central tendency measures provide information about typical or average value
  • Mean (arithmetic average)
  • Median (middle value)
  • Mode (most frequent value)
• Variability measures describe spread or dispersion of data points
  • Range (difference between highest and lowest values)
  • Variance (average squared deviation from mean)
  • Standard deviation (square root of variance)

Statistical Methods for Public Health

Descriptive Statistics

• Summarize and describe main features of dataset
• Measures of central tendency and variability key descriptive statistics
• Graphical representations visualize data distributions and relationships
  • Histograms show frequency distribution of continuous data
  • Box plots display median, quartiles, and potential outliers
  • Scatter plots illustrate relationship between two continuous variables

Inferential Statistics

• Draw conclusions about populations based on sample data
• Hypothesis testing makes decisions about population parameters
  • Null hypothesis assumes no effect or difference
  • Alternative hypothesis proposes significant effect or difference
• Confidence intervals provide range of plausible values for population parameters
  • 95% confidence interval commonly used in public health research
• Regression analysis examines relationship between variables
  • Linear regression for continuous outcomes (blood pressure and age)
  • Logistic regression for binary outcomes (presence or absence of disease)
• Analysis of variance (ANOVA) compares means across multiple groups
  • One-way ANOVA for single factor with multiple levels
  • Two-way ANOVA for two factors and their interaction
• Survival analysis studies time-to-event data
  • Kaplan-Meier curves visualize survival probabilities over time
  • Cox proportional hazards model assesses impact of variables on survival

Interpreting Statistical Results

Statistical Significance and Effect Size

• Statistical significance (p-value) indicates probability of obtaining results as extreme as observed
  • Typically, p < 0.05 considered statistically significant
  • Does not necessarily imply practical or clinical significance
• Effect size measures magnitude of relationship between variables
  • Cohen's d for standardized mean difference
  • Odds ratio for binary outcomes in case-control studies
  • Relative risk for cohort studies and clinical trials

Communicating Results

• Visual representations effectively communicate complex statistical information
  • Forest plots for meta-analyses
  • Funnel plots to assess publication bias
• Use clear and concise language to explain concepts to diverse audiences
  • Avoid jargon when presenting to non-technical stakeholders
  • Provide context and real-world implications of findings
• Communicate limitations and potential sources of bias in analyses
  • Sample size and representativeness
  • Confounding factors and unmeasured variables
• Consider ethical aspects when presenting statistical results
  • Avoid misleading representations of data
  • Ensure transparency in data collection and analysis methods
  • Disclose conflicts of interest and funding sources