Epidemiology Research Design Skills

Skills Needed for Epidemiologists (Prior Caring on the Study)

• A study title should clearly and concisely reflect the study problem, questions, design, and type, including the location and time frame.
• Epidemiologists need a deep understanding of epidemiology.
• They require proficiency in defining problems and formulating clear, targeted questions for their study designs. Methodologies and test relations are essential.
• Strong statistical knowledge is critical, encompassing hypothesis formulation (testing hypotheses), and the practical application of statistics in research and statistical inference.

Research Design Tips

• Clear hypothesis formulation needs a strong understanding of the problem definition and the questions asked.
• Appropriate sample sizes are crucial (must be representative)
• Selecting the proper statistical test is needed for the study design.
• Power considerations in the design are essential.
• Understanding and implementing strategies to mitigate errors is critical to producing valid results.

Statistical Symbols

• N = Total population size
• n = Sample size
• p = Probability
• µ = Population mean
• σ = Population standard deviation
• x = Sample mean

Population vs Sample

• Population: The complete group of individuals or objects of interest.
• Sample: A subset of the population selected for study.

Example:

• City population (N) = 500,000
• Study sample (n) = 1,000

Descriptive Statistics (Quantitative Studies)

• Measures of Central Tendency:
  • Mean (average)
  • Median (middle value)
  • Mode (most frequent value)

Measures of Variability

• Range
• Variance
• Standard Deviation

These values are essential to understanding how spread out or clustered the data is.

Inferential Statistics

• Used to make predictions about populations using:
  • Hypothesis testing (rejecting the null hypothesis when p-value ≤ significance level).
  • Confidence intervals
  • Regression analysis

Statistical Hypothesis Formulation and Testing

• Null hypothesis (H0): Asserts no relationship or effect exists.
• Alternative hypothesis (H1): Asserts a relationship or effect exists.
• Significance level (α): Usually 0.05 (acceptable error due to chance).

Hypothesis Formulation (Examples)

• Suggesting possible events (independent): The incidence of tuberculosis will increase in the next decade.
• Suggesting relationships between exposures and outcomes: High cholesterol intake is associated with coronary heart disease.
• Suggested cause-effect relationship: Cigarette smoking is a cause of lung cancer.
• One-sided vs. two-sided hypotheses (examples provided).

Hypothesis Formulation Guidelines

• Define exposure variables precisely.
• Define health outcomes precisely.

Example Hypotheses

• Poor example: Eating junk food is associated with cancer.
• Good example: HPV subtype 16 is associated with cervical cancer.

Hypothesis Testing

• Report the p-value and interpret it in context.
• If necessary, reject the null hypothesis (H0) when the p-value is less than or equal to the significance level.

Hypothesis Testing Steps

• State hypotheses.
• Set a significance level.
• Choose the right statistical test.
• Calculate the statistics.
• Make a decision.

P-values and Decision Making

• If the p-value is less than the significance level (α), reject the null hypothesis (H0).
• If the p-value is greater than the significance level (α), fail to reject the null hypothesis (H0).

Type I Error

• False positive: Rejecting H0 when it's true.
• Probability of type I error (α)

Type II Error

• False negative: Not rejecting H0 when it's false.
• Probability of type II error (β)

Statistical Power

• Power = 1 - β, where β is the probability of a Type II error.
• Factors affecting power include:
  • Sample size
  • Effect size
  • Significance level

Choosing Adequate Tests: Z-tests

• Use when:
  • Sample size (n) is greater than 30
  • Population standard deviation (σ) is known
  • Testing differences in population means

Choosing Adequate Tests: T-tests

• Use when:
  • Sample size (n) is less than 30
  • Population standard deviation (σ) is unknown
• Three types of t-tests exist

Types of T-tests

• One-sample t-test
• Independent samples t-test
• Paired samples t-test

Confidence Intervals (Part 1)

• A range that likely contains the true population parameter.
• Usually a 95% confidence level is employed.

Confidence Intervals (Part 2)

• Factors affecting the width (margin of error) of the confidence interval:
  • Sample size
  • Variability
  • Confidence level.

Sample Size Considerations

• Larger sample sizes provide:
  • Increased precision
  • Reduced error
  • Improved power

Common Statistical Tests

• Z-test vs. t-test
• Paired t-test vs. unpaired t-test
• Parametric tests vs. non-parametric tests

This quiz covers essential skills required for epidemiologists, focusing on research design, hypothesis formulation, and statistical knowledge. Participants will learn about defining problems, selecting sample sizes, and choosing appropriate statistical tests. Gain insights into the critical aspects of conducting successful epidemiological studies.