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Related Concept Videos

Ranks01:02

Ranks

306
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
306
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

242
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
242
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

655
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
655
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

333
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
333
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

386
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
386
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

324
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
324

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National Resident Matching Program Rank Order and Performance in an Internal Medicine Residency.

George D Everett1, Edward Maharam1, Fanchao Yi1

  • 1From the Internal Medicine Department and the Research Institute, AdventHealth Orlando, Orlando, Florida.

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Higher USMLE scores predict better performance in Internal Medicine residency, including milestones and faculty rankings. While NRMP rank showed some correlation, USMLE scores were the strongest predictor of resident success.

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Area of Science:

  • Medical Education Research
  • Graduate Medical Education
  • Internal Medicine Training

Background:

  • The National Resident Matching Program (NRMP) rank order list process is time-intensive for residency applicants and programs.
  • Previous research indicates a weak correlation between NRMP rank and resident performance, questioning its utility.
  • No prior studies have examined this relationship specifically within Internal Medicine residencies.

Purpose of the Study:

  • To investigate the association between NRMP rank order and objective performance outcomes in Internal Medicine residency.
  • To identify predictors of resident success beyond the NRMP ranking.

Main Methods:

  • Retrospective cohort study of Internal Medicine residents (N=56) from five graduating classes (2013-2020).
  • Evaluated five objective outcomes: ACGME milestones, faculty rankings, In-Training Examination scores, chief resident attainment, and fellowship attainment.
  • Analyzed eight predictors including NRMP rank, medical school factors, and USMLE scores using univariate and multivariate analyses.

Main Results:

  • USMLE scores were significantly associated with favorable ACGME milestones, faculty rankings, and In-Training Examination scores in multivariate analysis.
  • NRMP rank showed a significant univariate correlation with ACGME milestones but was not a significant predictor in multivariate analysis.
  • No predictors were significantly associated with fellowship attainment.

Conclusions:

  • Higher USMLE scores are a strong predictor of objective success in Internal Medicine residency.
  • While NRMP rank may have some relevance, USMLE scores appear to be a more robust indicator of resident performance.
  • Further research may explore the predictive value of other objective metrics in residency selection.