Related Experiment Video
Updated: Mar 9, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
Published on: March 13, 2020
Mind the Gap: The Prospects of Missing Data
Background:
The increasing use of workplace-based assessments (WBAs) in competency-based medical education has led to large data sets that assess resident performance longitudinally. With large data sets, problems that arise from missing data are increasingly likely.
Objective:
The purpose of this study is to examine (1) whether data are missing at random across various WBAs, and (2) the relationship between resident performance and the proportion of missing data.
Methods:
During 2012-2013, a total of 844 WBAs of CanMEDs Roles were completed for 9 second-year emergency medicine residents. To identify whether missing data were randomly distributed across various WBAs, the total number of missing data points was calculated for each Role. To examine whether the amount of missing data was related to resident performance, 5 faculty members rank-ordered the residents based on performance. A median rank score was calculated for each resident and was correlated with the proportion of missing data.
Results:
More data were missing for Health Advocate and Professional WBAs relative to other competencies (P < .001). Furthermore, resident rankings were not related to the proportion of missing data points (r = 0.29, P > .05).
Conclusions:
The results of the present study illustrate that some CanMEDS Roles are less likely to be assessed than others. At the same time, the amount of missing data did not correlate with resident performance, suggesting lower-performing residents are no more likely to have missing data than their higher-performing peers. This article discusses several approaches to dealing with missing data.
More Related Videos
Related Concept Videos
Quantifying and Rejecting Outliers: The Grubbs Test
Conservation of Declining Populations
Hindsight Biases
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Data Collection I

