Data Analysis & Reporting Flashcards
7 cards from real ABCS practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Data Analysis & Reporting flashcards as text
A cosmetic surgery practice uses run charts to monitor monthly infection rates. Which pattern would most reliably signal a special cause requiring investigation?
Answer: Six consecutive points all ascending or descending
Six consecutive points trending in one direction is a classic run chart rule for detecting non-random special cause variation.
When comparing pre- and post-operative FACE-Q scores for the same patients, which statistical test is most appropriate?
Answer: Paired t-test
A paired t-test accounts for the within-subject correlation when the same patients are measured before and after an intervention.
Which approach best addresses the problem of missing data in a cosmetic surgery outcomes registry?
Answer: Apply multiple imputation methods
Multiple imputation creates several plausible complete datasets and combines results, reducing bias from missing data more effectively than simple deletion or substitution.
A multi-site cosmetic surgery group wants to compare complication rates across five different locations. Which statistical method is most appropriate to test for differences?
Answer: One-way ANOVA or Kruskal-Wallis test
One-way ANOVA (or its non-parametric equivalent, Kruskal-Wallis) compares means across three or more independent groups simultaneously.
In cosmetic surgery quality reporting, a 'sentinel event' is best defined as:
Answer: An unexpected occurrence involving death or serious physical harm
A sentinel event is an unexpected, serious adverse outcome—including death or severe harm—that signals a need for immediate root cause analysis.
A surgeon presents data showing 85% patient satisfaction but fails to mention the 40% non-response rate. This omission is an example of which reporting issue?
Answer: Non-response bias obscuring generalizability
Non-responders often differ systematically from responders, and omitting the response rate hides a major threat to the data's generalizability.
For ABCS data submissions, which measure quantifies the ability of a screening tool to correctly identify patients who will develop a complication?
Answer: Sensitivity
Sensitivity measures the proportion of true positive cases correctly identified—i.e., the screening tool's ability to detect those who will develop the complication.