Statistics
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Questions and Answers

What is the main purpose of statistical analysis in relation to outcome variables?

  • To establish a causal relationship with certainty
  • To determine associations between exposure variables and the outcome variable (correct)
  • To summarize descriptive statistics only
  • To collect raw data on individuals

Which statement correctly describes the relationship between a sample and a population?

  • A population is always larger than a sample (correct)
  • Findings from a sample are universal to all populations
  • A sample can fully represent a population at all times
  • Different samples will yield identical results

What type of variable is defined as can take on any number in a given range, such as BMI?

  • Categorical
  • Discrete
  • Binary
  • Continuous (correct)

Which type of categorical variable assigns individuals to distinct categories with no order?

<p>Nominal (D)</p> Signup and view all the answers

What does the standard deviation measure in a dataset?

<p>How clustered or spread out the numbers are (D)</p> Signup and view all the answers

In longitudinal studies, what does the term 'rate' refer to?

<p>The frequency of occurrence of an illness or death over time (D)</p> Signup and view all the answers

Which of the following is an example of a binary variable?

<p>Presence or absence of depression (B)</p> Signup and view all the answers

Why is it important for a sample to represent the target population accurately?

<p>To generalize findings to larger groups (B)</p> Signup and view all the answers

What type of regression is used for predicting outcomes when the dependent variable is continuous?

<p>Linear regression (A)</p> Signup and view all the answers

What does an odds ratio greater than 1 indicate in health research?

<p>Increased risk or odds of the outcome (C)</p> Signup and view all the answers

Which regression model is suitable for analyzing rate data?

<p>Poisson regression (A)</p> Signup and view all the answers

How do p-values relate to the null hypothesis in hypothesis testing?

<p>Small p-values suggest evidence against the null hypothesis (C)</p> Signup and view all the answers

In the context of risk, what does the term 'risk' quantify?

<p>The proportion of new events related to those at risk (A)</p> Signup and view all the answers

What is the purpose of multiple hypothesis testing corrections such as the Bonferroni correction?

<p>To account for the increased chance of type I error (B)</p> Signup and view all the answers

Which of the following correctly describes logistic regression?

<p>Used for binary outcome analysis (B)</p> Signup and view all the answers

What does Cox regression primarily focus on during analysis?

<p>Analyzing time-to-event data (C)</p> Signup and view all the answers

What might the confidence interval (CI) around an odds ratio indicate?

<p>The range in which the true odds ratio is likely to fall (D)</p> Signup and view all the answers

What is the main function of the outcome variable in research?

<p>To serve as the focus of attention (A)</p> Signup and view all the answers

Why is it crucial for a sample to accurately represent the target population?

<p>To facilitate generalization of findings (C)</p> Signup and view all the answers

Which option best describes continuous numerical variables?

<p>Can take on any value within a range (C)</p> Signup and view all the answers

What information does the mean provide about a dataset?

<p>The average of the values (B)</p> Signup and view all the answers

In what context are rates typically used in research?

<p>To show frequency of occurrences over time (B)</p> Signup and view all the answers

Which type of categorical variable is characterized by a specific order?

<p>Ordered categorical variable (B)</p> Signup and view all the answers

What is one purpose of employing statistical methods in mental health research?

<p>To find effective treatments for mental health problems (B)</p> Signup and view all the answers

What does variance measure in a dataset?

<p>The spread of data points around the mean (B)</p> Signup and view all the answers

What type of regression model is used when analyzing binary outcomes?

<p>Logistic regression (B)</p> Signup and view all the answers

Which statistical measure indicates the strength and direction of a relationship in regression analysis?

<p>Effect size (D)</p> Signup and view all the answers

What is the primary purpose of estimating confidence intervals (CIs) in regression analyses?

<p>To assess the reliability of the estimate (B)</p> Signup and view all the answers

In regression analysis, which method would you use to analyze time-to-event data?

<p>Cox regression (B)</p> Signup and view all the answers

What does a risk ratio greater than 1 indicate in a health study?

<p>Increased risk in exposed group (B)</p> Signup and view all the answers

Which of these is NOT a component of hypothesis testing?

<p>Calculating effect size (D)</p> Signup and view all the answers

Which regression type is suitable for analyzing count data?

<p>Poisson regression (C)</p> Signup and view all the answers

What indicates a need for multiple hypothesis testing corrections?

<p>Testing multiple hypotheses simultaneously (B)</p> Signup and view all the answers

What is the main focus of odds in health research?

<p>Probability of event occurrence (D)</p> Signup and view all the answers

When analyzing mean differences between groups, which regression technique is utilized?

<p>Linear regression (A)</p> Signup and view all the answers

Flashcards

Outcome Variable

The main factor we are studying, like depression, eating disorder, psychosis, or bipolar disorder.

Exposure Variable

Factors that might influence the outcome variable, like genetics, life experiences, or environmental factors.

Population

The entire group we want to study. For example, all adults in the United States.

Sample

A smaller group of individuals selected from the population. We use them to get information about the larger group.

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Statistical Inference

The process of using data from a sample to make educated guesses about the larger population.

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Continuous Variable

A numerical variable that can take on any value within a range, like a person's height or weight.

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Discrete Variable

A numerical variable that can only take on specific whole numbers, like the number of children in a family.

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Categorical Variable

A variable that classifies individuals into distinct categories, like race, gender, or diagnosis.

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Linear Regression

A statistical technique used to analyze data where the outcome variable is continuous (e.g., blood pressure, BMI).

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Logistic Regression

A statistical technique used to analyze data where the outcome variable is binary (e.g., yes/no, present/absent).

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Probability

The probability of an event occurring. Expressed as a proportion or percentage.

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Rate

The number of new events (e.g., diseases) in a population over a specific time period.

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Odds Ratio (OR)

A measure of association between an exposure and an outcome, comparing the odds of the outcome in the exposed group to those in the unexposed group. Often used in logistic regression.

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Poisson Regression

A statistical technique used to analyze data where the outcome variable is a count (e.g., number of events).

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Hazard Ratio (HR)

A measure of the rate of death or disease recurrence in a group over time.

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Cox Regression

A statistical technique used to analyze data where the outcome is the time until an event occurs (e.g., survival time, time to disease recurrence).

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Null Hypothesis

A statistical hypothesis that assumes there is no difference or association between groups or variables.

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P-value

The probability of observing a result as extreme as the one obtained, assuming the null hypothesis is true. A small p-value suggests evidence against the null hypothesis.

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What is an Outcome Variable?

The main point of our study, like depression, eating disorders, or psychosis.

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What are Exposure Variables?

Things that might influence the outcome variable, such as genetics, life experiences, or exposure to certain events.

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What is a Population?

The entire group we want to study and draw conclusions about.

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What is a Sample?

A smaller group of individuals chosen from the population to represent it.

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What is Statistical Inference?

Using data from a sample to make educated guesses about an entire population.

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What is a Continuous Variable?

A number that can take on any value within a range, like a person's height or weight.

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What is a Discrete Variable?

A number that can only take on specific whole numbers, like the number of children in a family.

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What is a Categorical Variable?

A variable that assigns individuals to distinct categories, like gender, race, or diagnosis.

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Mean Difference

A measure of association for continuous outcome variables, reflecting the average difference in outcome between the exposed and unexposed groups. It's often used in linear regression.

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Incidence Rate Ratio (IRR)

A measure of the association between exposure and a count outcome. It estimates the ratio of incidence rates between the exposed and unexposed groups. It's used in Poisson regression.

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Bonferroni Correction

A method used to correct for multiple hypothesis testing, adjusting the p-value threshold to reduce the chance of false positives.

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Confidence Interval (CI)

A technique used to construct confidence intervals for effect estimates, capturing the precision of the estimated association between exposure and outcome.

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Study Notes

Exposure and Outcome Variables

  • Outcome variables are the central focus—conditions like depression, eating disorders, psychosis, and bipolar disorder.
  • Researchers often examine risk factors (exposures) influencing outcomes.
  • The purpose of statistical analysis is to determine the association between exposure variables and the outcome variable.

Population and Sample

  • Data is collected on a sample from a larger group, called the population (also known as the target population).
  • Statistics allows for inferences about the population based on the sample.
  • Different samples will produce different results due to random chance.
  • The sample should ideally represent the target population for generalizable findings.

Data Types

  • Raw data are observations from individuals.
  • Sample size is the number of individuals.
  • Any measured characteristic (e.g., depressive symptoms, loneliness, age, gender) is a variable.
  • The first step in data analysis is classifying variables into different types.
  • The statistical test selection depends on the outcome type.
    • Numerical (quantitative):
      • Continuous: Can take any value within a range (e.g., BMI).
      • Discrete: Can only take integer values (e.g., number of depressive episodes).
    • Categorical (qualitative):
      • Binary: Two categories (e.g., diagnosed/not diagnosed).
      • Ordered Categorical: Categories with order (e.g., socioeconomic status).
      • Nominal: Categories without order (e.g., eye color).
    • Rates: Measures of disease frequency or death over time (e.g., 30-year mortality rates).

Descriptive Statistics

  • Mean: Average of numerical data.
  • Standard Deviation: Measures data dispersion.
  • Variance: Average of squared differences from the mean.

Inferential Statistics—Why Use Them?

  • Description: Examining prevalence and characteristics of mental health problems.
    • Example: How common are mental health problems? Who is more likely to develop them?
  • Causation: Investigating causes, outcomes, and treatment effectiveness.
    • Example: What causes mental health problems? What are the outcomes? Which treatments are effective?

Steps in a Statistical Analysis

  1. Formulating research questions.
  2. Defining comparisons (e.g., exposed vs. unexposed groups).
  3. Collecting and summarizing data (e.g., symptoms).
  4. Estimating and testing differences between groups:
    • Effect sizes (magnitude of differences in mental health symptoms).
    • Mean differences, odds ratios, risk ratios.
    • Confidence intervals (uncertainty estimates).

Regression Models

  • Different regression models for various outcomes:
    • Continuous outcomes: Linear regression (predicting mean differences).
    • Binary outcomes: Logistic regression (estimating odds ratios).
    • Count outcomes: Poisson regression.
    • Time-to-event outcomes: Cox regression (predicting hazard ratios).

Binary Outcomes (Logistic Regression)

  • Key output: Odds ratios.
  • Odds: Probability of an event happening divided by the probability of it not occurring.
  • Comparing risk in exposed vs. unexposed groups (using odds ratios).
  • Ratio of 1 (or close to): Minimal difference between groups.
  • Ratio > 1: Exposure is linked to increased odds of the outcome.
  • Ratio < 1: Exposure is linked to decreased odds.

Rates (Poisson Regression)

  • Key output: Incidence Rate Ratios (IRRs).
  • Incidence rate: Number of new events per unit of time.

Rates vs. Risk

  • Risk: Number of new events relative to the number at risk at the start.
  • Longer observation time potentially inflates risk.
  • Using rates (longer observation time) reduces potential for bias.

Cox Regression Model (Survival Analysis)

  • Estimates hazard ratios (comparing rates of events like death or recurrence).

Hypothesis Testing

  • Null hypothesis: No difference/association between groups.
  • P-value: Probability of observing results as extreme as, or more extreme than the ones observed, assuming the null hypothesis is true.
  • Multiple hypothesis testing corrections (Bonferroni, false discovery rate, permutation ).

Continuous Outcomes (Linear Regression)

  • Used to compare two means (exposed vs. unexposed).
  • Produces mean differences.
  • Confidence intervals around the mean difference and p-values can be generated.
  • Can include additional variables (multiple regression).

Statistical Inference

  • Using sample data to make inferences about a population.

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Description

This quiz covers the key concepts in epidemiology, focusing on exposure and outcome variables, population sampling, and data types. It highlights the importance of statistical analysis in determining risk factors associated with various mental health conditions. Test your understanding of how these elements contribute to research findings.

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