Sample Size in Mental Health Research

<p>It may create publication bias for statistically significant results. (B)</p> Signup and view all the answers

<p>It may lead to inconclusive findings. (C)</p> Signup and view all the answers

<p>Very small trials may be regarded as unreliable. (C)</p> Signup and view all the answers

<p>To ensure an adequate number of participants remain for analysis. (A)</p> Signup and view all the answers

<p>The comparison of death rates between treatment and control groups. (C)</p> Signup and view all the answers

<p>Beck Depression Inventory II (BDI-II) (C)</p> Signup and view all the answers

<p>6 points (A)</p> Signup and view all the answers

<p>Time duration of the study (B)</p> Signup and view all the answers

<p>Increase the sample size (A)</p> Signup and view all the answers

<p>A score reduction of 6 points on the BDI-II (A)</p> Signup and view all the answers

<p>Beck Depression Inventory II score (D)</p> Signup and view all the answers

<p>20 points (B)</p> Signup and view all the answers

<p>6 points (A)</p> Signup and view all the answers

<p>5% (A)</p> Signup and view all the answers

<p>The required sample size per group (C)</p> Signup and view all the answers

<p>80% (B)</p> Signup and view all the answers

<p>12 points (D)</p> Signup and view all the answers

<p>12 sessions (B)</p> Signup and view all the answers

<p>80% (B)</p> Signup and view all the answers

<p>Sample size ÷ 0.75 (D)</p> Signup and view all the answers

<p>No difference exists between the groups tested. (C)</p> Signup and view all the answers

<p>They cannot help identify reasons for expected results not being found. (A)</p> Signup and view all the answers

<p>Proportion lost (B)</p> Signup and view all the answers

<p>Standard deviation is less than specified. (B)</p> Signup and view all the answers

<p>95% confidence intervals (B)</p> Signup and view all the answers

<p>Sample size impacts both Type I and II error rates. (C)</p> Signup and view all the answers

<p>The smallest difference between groups that needs to be detected (A)</p> Signup and view all the answers

<p>Standard deviation of the outcome (C)</p> Signup and view all the answers

<p>80% or 90% (D)</p> Signup and view all the answers

<p>The true proportion in the control group (A)</p> Signup and view all the answers

<p>Proportion of the control group (A)</p> Signup and view all the answers

<p>They share a similar structure in their formulas. (A)</p> Signup and view all the answers

<p>0.05 (A)</p> Signup and view all the answers

<p>All of the above (D)</p> Signup and view all the answers

<p>They enhance the ability to detect smaller differences between groups. (C)</p> Signup and view all the answers

<p>It necessitates larger sample sizes. (D)</p> Signup and view all the answers

<p>It represents the likelihood of detecting a true effect. (D)</p> Signup and view all the answers

<p>It increases the required sample size. (D)</p> Signup and view all the answers

<p>Recruit additional participants to counteract potential loss. (D)</p> Signup and view all the answers

<p>Specifying correlations between baseline and follow-up. (D)</p> Signup and view all the answers

<p>Increased intraclass correlation (ICC). (A)</p> Signup and view all the answers

<p>The timing of events must be carefully recorded. (A)</p> Signup and view all the answers

<p>It reduces the power to detect the specified effect. (D)</p> Signup and view all the answers

The number of participants required to obtain reliable evidence of a treatment effect, while also achieving precise estimates of the effect.

Incorrectly rejecting a true null hypothesis. This means concluding there is an effect when there is not.

Incorrectly failing to reject a false null hypothesis. This means failing to detect an effect that actually exists.

The probability of committing a Type I error. It's the acceptable risk of falsely concluding there's an effect when there isn't.

The power of a study is the probability of correctly rejecting a false null hypothesis. A powerful study can detect an effect that exists.

A small study may lack the power to detect a real treatment effect, resulting in inconclusive findings. This could mislead researchers and clinicians.

Studies with small sample sizes may be more likely to be published if they show a statistically significant result (p<0.05), regardless of the real effect size.

Ensuring that enough participants are enrolled in a study to obtain reliable evidence while balancing ethical, economic, and pragmatic considerations.

The probability of detecting a treatment effect when it is actually present.

The specific value of the effect size that the study aims to detect. This is usually based on clinical significance.

The variation in the outcome measure within the population. A larger SD means more variability in the data.

The smallest difference between groups that is considered meaningful and worth detecting.

The probability of incorrectly rejecting a true null hypothesis, typically set at 5%.

The required sample size per group for comparing two means, where μ1 and μ2 are the means of the two groups.

The required sample size per group for comparing two proportions, where p1 and p2 are the proportions of the two groups.

The relationship between sample size, standard deviation, and effect size. A larger effect size or smaller standard deviation requires a smaller sample size.

The primary outcome measure used to evaluate the effect of the intervention.

The Beck Depression Inventory II (BDI-II) is a self-report questionnaire used to assess the severity of depressive symptoms in individuals. It is a widely used and reliable instrument.

Statistical analysis using a t-test is used to compare the average BDI-II scores between the two groups: those receiving standard treatment as usual (TAU) and those receiving TAU plus cognitive behavioral therapy (CBT).

It is expected that the control group, receiving only standard treatment as usual (TAU), will have an average BDI-II score of 20 with a standard deviation of 12. This reflects the baseline level of depression in cancer patients receiving typical care.

The researchers want to detect a difference of at least 6 points on the BDI-II scale between the groups. This means if the CBT group shows a reduction of 6 points or more on the BDI-II compared to the control group, it will be considered a clinically significant improvement.

The study aims to achieve 80% power. This means there is an 80% chance of finding a statistically significant difference in depression scores between the groups if there is a real difference of at least 6 points.

The standard deviation (SD) of the BDI-II scores in cancer patients is estimated to be 12 points. This indicates the variability or spread of depression scores in the population.

The statistical significance level (alpha) for the study is set at 5%. This means there is a 5% chance of incorrectly concluding that CBT has an effect when it actually doesn't.

The formula for sample size calculation considers the difference to be detected, the standard deviation, the alpha level, and the desired power. This formula helps determine how many participants are needed per group to obtain statistically reliable results.

An adjustment applied to the calculated sample size to account for potential participant dropouts or loss to follow-up during a study, ensuring enough participants remain to achieve the desired statistical power.

A statistical method used to estimate the probability of finding a significant result based on the observed data after the study has been completed. However, it is generally not considered to be informative and should be avoided.

The standard deviation of the outcome variable in the population, representing the variability or spread of data.

The probability of correctly rejecting a false null hypothesis, meaning the study's ability to detect a true difference between groups.

The probability of incorrectly rejecting a true null hypothesis. It's the risk of concluding there's a difference when there isn't one.

The probability of correctly rejecting a false null hypothesis, meaning you detect an effect that is actually there.

The difference between two groups that is considered to be clinically meaningful or practically important. It's what you aim to detect.

The number of participants needed in each group to have enough statistical power to detect a meaningful difference between groups.

The range of values for the outcome variable in your study. Higher variability means more spread out data.

The extent to which participant drop-out affects the results. It can reduce the statistical power of the study.

A group of nested observations, like a patient within a clinic, or a child within a family. This can affect sample size calculations.

The correlation between measurements taken at different time points. This can influence sample size calculations.

A measure of how similar observations are within the same cluster, for example, how similar patients treated by the same therapist might be.