Since variables of interest in the study have different hazard ratios, it would be much reasonable to calculate the confidence interval of effect sizes i.e.. The bootstrapping method wer
Trang 1To avoid the large sample size fallacy, it is highly recommended to not use Null Hypothesis Significance Testing (NHST) as the sole determination of the relevance of the predictors Since variables of interest
in the study have different hazard ratios, it would be much reasonable to calculate the confidence interval
of effect sizes (i.e hazard ratios) as they are offering more information and context as opposed to NHST of
a statistic The bootstrapping method were found to be useful over resampling the Cox Regression model With more certainty about the results, we can generate our forecasting model based on these factors and have more accuracy in terms of finding students at risk
References:
Banjanovic, E S., & Osborne, J W (2016) Confidence Intervals for Effect Sizes:
Applying Bootstrap Resampling Practical Assessment, Research & Evaluation, 21(5).
Curran-Everett, D (2009) Explorations in statistics: the bootstrap Advances in Physiology Education, 33(4), 286–292 doi.org/10.1152/advan.00062.2009
Guerrasio, J., Garrity, M J., & Aagaard, E M (2014) Learner deficits and academic outcomes of medical students, residents, fellows, and attending physicians referred to
a remediation program, 2006-2012 Academic Medicine: Journal of the Association of American Medical Colleges, 89(2), 352–358
doi.org/10.1097/ACM.0000000000000122
Stegers-Jager, K M., Cohen-Schotanus, J., & Themmen, A P N (2012) Motivation, learning strategies, participation and medical school performance Medical Education, 46(7), 678–688 doi.org/10.1111/j.1365-2923.2012.04284.x
Winston, K A., Vleuten, C P M van der, & Scherpbier, A J J A (2014) Prediction and prevention of failure: An early intervention to assist at-risk medical students
Medical Teacher, 36(1), 25–31 doi.org/10.3109/0142159X.2013.836270
Introduction
According to Stegers-Jager et al., (2012), “medical
schools wish to better understand why some students
excel academically and other have difficulty in passing
medical courses” (p.679) Although undergraduate and
graduate applicants are considered as the most talented
and highly motivated students, not everyone can come to
grips with medical school courses, trainings, and
residency to become a competent physician Hence, it is
not unusual that “approximately 7 to 28 percent of
medical trainees, regardless of their level of training or
specialty, will require remediation in the form of an
individualized learning plan to achieve competence”
(Gurreasio et al., 2014, p.352) Winston et al (2014)
suggested, “prediction and prevention of failure, or
remediation after failure” as two proper strategies for
dealing with this problem (p.26) To predict and prevent
the failure a head of time, proper statistical analysis is
needed The present study will extend existing
knowledge about the timing of failure and results
robustness by applying the bootstrap method
Design
According to Banjanovic and Osborne (2016),
“bootstrap resampling is a systemic method of
computing CI for nearly any estimate” (p.2) Generally,
this technique is useful where sample size is not
enough, additional data cannot be obtained, and/or the
data is not normally distributed For instance, as small
sample size may not be a typical of the underlying
population, we can use bootstrap to realize how well
the statistical theory holds (Curran-Everett, 2009)
Data
Data included 1670 student’s records of 1 to 17 exams
during first and second years of pre-clinical education
at Ohio University, Heritage College of Osteopathic
Medicine throughout academic year of 2000 to 2014
Based on Cox Regression, we have defined right and
left censored and have assigned them 0 and 1
respectively A total of 23512 observations i.e
student/exam have been used for this simulation
Methods
Results
The results of 1000 Bootstrap resampling of the Cox Regression hazard ratio obtained from R are reported
in tables and graphs Interestingly, the results are not consistent with the main Cox Regression model After bootstrapping, the hazard ratio of 1 (null hypothesis of two groups are equal) is included in the 95% confidence interval of hazard ratio range and we cannot reject the null hypothesis This include, Gender, Age, First generation (FG), in-state, and MCAT Bio However, other factors are not including the hazard ratio of 1, meaning that either group is above or below hazard ratios 1and they are consistent with previous results
Conclusion
Discussion
Medical Students at Risk: Application of Bootstrap Resampling
Abolfazl Ghasemi Arkansas College of Osteopathic Medicine
Hazard Ratio Confidence Interval with 1000 resampling
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Hazard Ratio Confidence Interval with 1000 resampling
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