Setting sample size using cost efficiency in fMRI studies
Bibliographic record
Abstract
Background: Sample size calculations are rarely performed for functional magnetic resonance imaging studies involving clinical populations. This may be due to uncertainty as to the size of expected effect and the variance of the blood oxygenation level dependent response. Moreover, existing sample size methods ignore the costs associated with performing the proposed study. The current paper describes how cost efficiency, a recently proposed method, can be used in conjunction with existing methods to address these issues. Methods: Cost efficiency is the ratio of a study’s value to its cost, and sample size is chosen to maximize cost efficiency (ie, to maximize return on investment). It is suggested that sample size calculations begin by calculating the sample sizes required to achieve a given power, through varying the input parameters to the calculation over their plausible ranges. Cost efficiency can then help narrow the resulting range of sample sizes and help choose one sample size. The approach is illustrated through a recent functional magnetic resonance imaging study of autobiographical memory retrieval in patients with major depressive disorder. An example: Setting power to 80% and type 1 error rate to 5%, the method of Mumford and Nichols was used to calculate sample size. There were no reported effect sizes for similar studies in the literature; consequently, this parameter was varied over its plausible range (Cohen’s d varying from 0.2 to 0.8). This yielded sample sizes ranging from 50 to 800. Within these, cost efficiency gave a sample size of 88. Conclusion: Poor reporting of the input parameters to power-based methods of sample size determination results in a wide range of candidate sample sizes. The cost efficiency approach supplies a way of narrowing this range and choosing a sample size from that. Keywords: cost efficiency, sample size, power, fMRI studies
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.560 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".