<p>Functional<strong> </strong>magnetic resonance imaging (fMRI) is a popular technique to study brain function and neural networks. Functional MRI studies are often characterized by small sample sizes and rarely consider statistical power when setting a sample size. This could lead to data dredging, and hence false positive findings. With the widespread use of fMRI studies in clinical disorders, the vulnerability of participants points to an ethical imperative for reliable results so as to uphold promises typically made to participants that the study results will help understand their conditions. While important, power-based sample size calculations can be challenging. The majority of fMRI studies are observational, i.e., are not designed to randomize participants to test efficacy and safety of any therapeutic intervention. My PhD thesis therefore addresses two objectives: firstly, to identify potential obstacles to implementing sample size calculations, and secondly to provide solutions to these obstacles in observational clinical fMRI studies. This thesis contains three projects.</p> <p>Implementing a power-based sample size calculation requires specifications of effect sizes and variances. Typically in health research, these input parameters for the calculation are estimated from results of previous studies, however these often seem to be lacking in the fMRI literature. Project 1 addresses the first objective through a systematic review of 100 fMRI studies with clinical participants, examining how often observed input parameters were reported in the results section so as to help design a new well-powered study. Results confirmed that both input estimates and sample size calculations were rarely reported. The omission of observed inputs in the results section is an impediment to carrying out sample size calculations for future studies.</p> <p>Uncertainty in input parameters is typically dealt with using sensitivity analysis; however this can result in a wide range of candidate sample sizes, leading to difficulty in setting a sample size. Project 2 suggests a cost-efficiency approach as a short-term strategy to deal with the uncertainty in input data and, through an example, illustrates how it narrowed the range to choose a sample size on the basis of maximizing return on investment.</p> <p>Routine reporting of the input estimates can thus facilitate sample size calculations for future studies. Moreover, increasing the overall quality of reporting in fMRI studies helps reduce bias in reported input estimates and hence helps ensure a rigorous sample size calculation in the long run. Project 3 is a systematic review of overall reporting quality of observational clinical fMRI studies, highlighting under-reported areas for improvement and suggesting creating a shortened version of the checklist which contains essential details adapted from the guidelines proposed by Poldrack et al. (2008) to accommodate strict word limits for reporting observational clinical fMRI studies.</p> <p>In conclusion, this PhD thesis facilitates future sample size and power calculations in the fMRI literature by identifying impediments, by providing a short-term solution to overcome the impediments using a cost-efficiency approach in conjunction with conventional methods, and by suggesting a long-term strategy to ensure a rigorous sample size calculation through improving the overall quality of reporting.</p> / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/15301 |
| Date | 25 September 2014 |
| Creators | Guo, Qing |
| Contributors | Pullenayegum, Eleanor, Clinical Epidemiology/Clinical Epidemiology & Biostatistics |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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