Podcast
Questions and Answers
What is a Type I error in the context of mental health research?
What is a Type I error in the context of mental health research?
- Incorrectly concluding there is an effect when none exists (correct)
- Failing to detect an effect that is actually present
- Not observing any results in a conducted study
- Observing a significant effect that is due to chance (correct)
Why is proper sample size calculation important in clinical trials?
Why is proper sample size calculation important in clinical trials?
- To reduce the time taken to recruit participants
- To avoid Type II errors in all cases
- To make the study more appealing to participants
- To ensure the study's results are statistically significant (correct)
Which of the following factors should be considered when determining sample size?
Which of the following factors should be considered when determining sample size?
- Type of treatment being administered
- Methods for recruiting participants
- Duration of the study
- All of the above (correct)
What can happen if a study has too small a sample size?
What can happen if a study has too small a sample size?
How can a well-designed clinical trial impact ethical considerations?
How can a well-designed clinical trial impact ethical considerations?
What is the relationship between sample size and the confidence intervals of estimated differences?
What is the relationship between sample size and the confidence intervals of estimated differences?
What is the power of a study, and how is it calculated?
What is the power of a study, and how is it calculated?
What is a potential consequence of having a trial that is too large?
What is a potential consequence of having a trial that is too large?
Flashcards
Sample Size Calculation
Sample Size Calculation
Determining the optimal number of participants needed for a study to reliably detect a treatment effect (if one exists), and estimate the effect precisely.
Type I Error
Type I Error
A false positive result; concluding there's an effect when there isn't one in reality.
Type II Error
Type II Error
A false negative result; failing to detect an effect that truly exists.
Small Study Problems
Small Study Problems
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Sample Size (Too Small)
Sample Size (Too Small)
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Sample Size (Too Large)
Sample Size (Too Large)
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Power (in Research)
Power (in Research)
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Publication Bias
Publication Bias
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Study Notes
Core Principles of Mental Health Research - Sample Size
- Learning Outcomes:
- Explain the importance of sample size calculations
- Define Type I and Type II errors
- List key factors determining sample size and their impact on study participants
- Calculate sample sizes for studies with continuous and binary outcomes
- Adjust sample sizes to account for loss to follow-up
- Approaches to Sample Size:
- Statistical/Scientific:
- Determine the number of patients needed to reliably detect a treatment effect.
- Accurately estimate the effect size.
- Economic/Pragmatic:
- Available patient numbers
- Time needed to recruit participants
- Cost of the study
- Ethical:
- When a trial can be stopped to prevent patients from receiving inferior treatment.
- Statistical/Scientific:
- Credibility:
- Small trials are unreliable due to low statistical power.
- Small Studies:
- May not detect clinically significant effects
- Produce imprecise estimates
- Less likely to represent the whole population
- More likely to show a false positive (p<0.05) result and be published, skewing findings
- Results may be misleading for clinicians
- Unethical for participants to contribute to a study that does not achieve a robust or meaningful outcome.
- Sample Size (Too Small):
- Lack of precision
- Difficult to distinguish real improvement from random chance.
- Ethical considerations
- Sample Size (Too Large):
- Waste of resources
- Ethical considerations
- A well-designed study should use the minimum number of participants to answer its research question.
- Type I and Type II Errors:
- Type I error (False Positive): Observing an effect that does not exist in the population.
- Type II error (False Negative): Not observing an effect that exists in the population.
- Power: The probability of correctly rejecting a false null hypothesis (1 - Type II error rate).
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