Name: Crossover Experiments
Uploaded: 2022-12-05T11:33:42-05:00
Duration: 1 min 16 s
Description: Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods.

Chapter 12: Statistics in Practice Back To Chapter

12.6: Crossover Experiments

TABLE OF
CONTENTS

X

Chapter 1: Understanding Statistics

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1.1: Introduction to Statistics
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1.2: How Data are Classified: Categorical Data
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1.3: How Data are Classified: Numerical Data
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1.4: Nominal Level of Measurement
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1.5: Ordinal Level of Measurement
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1.6: Interval Level of Measurement
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1.7: Ratio Level of Measurement
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1.8: Data Collection by Observations
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1.9: Data Collection by Experiments
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1.10: Data Collection by Survey
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1.11: Random Sampling Method
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1.12: Systematic Sampling Method
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1.13: Convenience Sampling Method
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1.14: Stratified Sampling Method
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1.15: Cluster Sampling Method

Chapter 2: Summarizing and Visualizing Data

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2.1: Review and Preview
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2.2: What is a Frequency Distribution
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2.3: Construction of Frequency Distribution
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2.4: Relative Frequency Distribution
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2.5: Percentage Frequency Distribution
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2.6: Cumulative Frequency Distribution
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2.7: Ogive Graph
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2.8: Histogram
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2.9: Relative Frequency Histogram
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2.10: Scatter Plot
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2.11: Time-Series Graph
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2.12: Bar Graph
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2.13: Multiple Bar Graph
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2.14: Pareto Chart
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2.15: Pie Chart

Chapter 3: Measure of Central Tendency

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3.1: What is Central Tendency?
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3.2: Arithmetic Mean
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3.3: Geometric Mean
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3.4: Harmonic Mean
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3.5: Trimmed Mean
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3.6: Weighted Mean
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3.7: Root Mean Square
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3.8: Mean From a Frequency Distribution
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3.9: What is a Mode?
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3.10: Median
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3.11: Midrange
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3.12: Skewness
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3.13: Types of Skewness

Chapter 4: Measures of Variation

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4.1: What is Variation?
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4.2: Range
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4.3: Standard Deviation
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4.4: Standard Error of the Mean
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4.5: Calculating Standard Deviation
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4.6: Variance
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4.7: Coefficient of Variation
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4.8: Range Rule of Thumb to Interpret Standard Deviation
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4.9: Empirical Method to Interpret Standard Deviation
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4.10: Chebyshev's Theorem to Interpret Standard Deviation
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4.11: Mean Absolute Deviation

Chapter 5: Measures of Relative Standing

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5.1: Review and Preview
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5.2: Introduction to z Scores
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5.3: z Scores and Unusual Values
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5.4: Percentile
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5.5: Quartile
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5.6: 5-Number Summary
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5.7: Boxplot
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5.8: What Are Outliers?
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5.9: Modified Boxplots

Chapter 6: Probability Distributions

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6.1: Probability in Statistics
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6.2: Random Variables
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6.3: Probability Distributions
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6.4: Probability Histograms
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6.5: Unusual Results
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6.6: Expected Value
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6.7: Binomial Probability Distribution
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6.8: Poisson Probability Distribution
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6.9: Uniform Distribution
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6.10: Normal Distribution
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6.11: z Scores and Area Under the Curve
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6.12: Applications of Normal Distribution
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6.13: Sampling Distribution
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6.14: Central Limit Theorem

Chapter 7: Estimates

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7.1: What are Estimates?
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7.2: Sample Proportion and Population Proportion
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7.3: Confidence Intervals
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7.4: Confidence Coefficient
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7.5: Interpretation of Confidence Intervals
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7.6: Critical Values
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7.7: Margin of Error
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7.8: Sample Size Calculation
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7.9: Estimating Population Mean with Known Standard Deviation
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7.10: Estimating Population Mean with Unknown Standard Deviation
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7.11: Confidence Interval for Estimating Population Mean

Chapter 8: Distributions

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8.1: Distributions to Estimate Population Parameter
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8.2: Degrees of Freedom
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8.3: Student t Distribution
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8.4: Choosing Between z and t Distribution
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8.5: Chi-square Distribution
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8.6: Finding Critical Values for Chi-Square
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8.7: Estimating Population Standard Deviation
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8.8: Goodness-of-Fit Test
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8.9: Expected Frequencies in Goodness-of-Fit Tests
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8.10: Contingency Table
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8.11: Introduction to Test of Independence
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8.12: Hypothesis Test for Test of Independence
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8.13: Determination of Expected Frequency
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8.14: Test for Homogeneity
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8.15: F Distribution

Chapter 9: Hypothesis Testing

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9.1: What is a Hypothesis?
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9.2: Null and Alternative Hypotheses
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9.3: Critical Region, Critical Values and Significance Level
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9.4: P-value
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9.5: Types of Hypothesis Testing
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9.6: Decision Making: P-value Method
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9.7: Decision Making: Traditional Method
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9.8: Hypothesis: Accept or Fail to Reject?
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9.9: Errors In Hypothesis Tests
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9.10: Testing a Claim about Population Proportion
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9.11: Testing a Claim about Mean: Known Population SD
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9.12: Testing a Claim about Mean: Unknown Population SD
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9.13: Testing a Claim about Standard Deviation

Chapter 10: Analysis of Variance

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10.1: What is an ANOVA?
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10.2: One-Way ANOVA
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10.3: One-Way ANOVA: Equal Sample Sizes
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10.4: One-Way ANOVA: Unequal Sample Sizes
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10.5: Multiple Comparison Tests
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10.6: Bonferroni Test
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10.7: Two-Way ANOVA

Chapter 11: Correlation and Regression

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11.1: Correlation
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11.2: Coefficient of Correlation
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11.3: Calculating and Interpreting the Linear Correlation Coefficient
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11.4: Regression Analysis
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11.5: Outliers and Influential Points
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11.6: Residuals and Least-Squares Property
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11.7: Residual Plots
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11.8: Variation
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11.9: Prediction Intervals
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11.10: Multiple Regression

Chapter 12: Statistics in Practice

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12.1: What is an Experiment?
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12.2: Study Design in Statistics
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12.3: Observational Studies
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12.4: Experimental Designs
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12.5: Randomized Experiments
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12.6: Crossover Experiments
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12.7: Controls in Experiments
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12.8: Bias
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12.9: Blinding
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12.10: Clinical Trials

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TRANSCRIPT

A crossover study design is also called a repeated measurements design, where experimental units receive all the treatments in different periods.

For instance, consider a clinical trial comparing drug A and drug B on 10 asthmatic patients randomly divided into group one, and group two.

First, each group receives different drugs for two weeks, recording their effect on the patient's physiology. This is followed by a washout period, to eliminate the drug from the patient's body.

Now, the groups are switched so that the second group receives drug A, and the first group receives drug B. This is termed a cross-over design. In this design, subjects act as their own controls, and their characteristics are not changed throughout the study. It also removes inter-subject variabilities.

This design is generally used in late-phase clinical trials involving drugs that help control the symptoms and not cure them completely.

For instance, in the previous example, drug B will not have an opportunity to demonstrate its effectiveness if drug A cures the patient during the first period.

12.6: Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.

Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments. Furthermore, cross-over designs are preferred to compare the bioavailability or ingestion of the drug in the human body and compare it with a reference drug. This method is unsuitable if the disease is chronic and stable and the drug should not completely cure the disease condition.

Chapter 1: Understanding Statistics

Chapter 2: Summarizing and Visualizing Data

Chapter 3: Measure of Central Tendency

Chapter 4: Measures of Variation

Chapter 5: Measures of Relative Standing

Chapter 6: Probability Distributions

Chapter 7: Estimates

Chapter 8: Distributions

Chapter 9: Hypothesis Testing

Chapter 10: Analysis of Variance

Chapter 11: Correlation and Regression

Chapter 12: Statistics in Practice

12.6: Crossover Experiments

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