Related Experiment Video
Updated: May 31, 2026

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
Published on: January 23, 2017
Rating certainty of evidence when the target threshold is a value-based threshold and the point estimate is close to
Linan Zeng1, Monica Hultcrantz2, Romina Brignardello3
1Department of Pharmacy/ Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University/ Children's Medicine Key Laboratory of Sichuan Province/ NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation/Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Objectives:
It is crucial, when using Grading of Recommendations Assessment, Development and Evaluation (GRADE), that authors are clear about what it is in which they are rating the certainty of evidence (ie, the target of certainty of evidence rating). This article addresses challenges GRADE users face in choosing a target of certainty rating for any value-based threshold (ie, little or no difference; an important effect; small, moderate or large difference) when the point estimate is close to that threshold.
Study Design And Setting:
Using an iterative process, authors generated and refined possible solutions to the identified challenges.
Results:
The challenges GRADE users face when point estimates prove close to the initially chosen value-based threshold include (1) the intuitive interpretation of the point estimate is similar whichever side of the threshold the point estimate lies on but the formal interpretation differs greatly and (2) rating down for imprecision due to the confidence interval (CI) crossing the threshold when the CI is very narrow. This paper provides four possible solutions. The first is continuing to rate certainty in an important or unimportant effect and rating down for imprecision when the CI crosses the MID. The second is to consider uncertainty around the MID, rating certainty in relation to a range of plausible MIDs (ie, a range formed by the smallest plausible MID and the largest plausible MID), and rate down for imprecision when the CI crosses either plausibility bound. The third solution is considering both the null and moderate effect thresholds, rating certainty in a trivial or small effect, and rating down for imprecision when the CI crosses either threshold. The fourth solution is using more than one of the above solutions and presenting two or more certainty of evidence ratings.
Conclusions:
GRADE users should note these challenges and apply their chosen solution to transparently determine the target of their certainty rating.
Plain Language Summary:
When point estimates prove to be close to the initially chosen value-based threshold, to decide on what it is in which GRADE users rate their certainty (ie, the target of certainty rating), GRADE users face challenges. This paper provides four possible solutions related to the minimal important difference (MID) that are also applicable to other value-based thresholds. (1) Continuing to rate certainty in an important or unimportant effect and rate down for imprecision when the CI crosses the MID; (2) Considering a plausible range of MIDs, rating certainty in an effect that is close to the MID, and rating down for imprecision when the CI crosses either or both plausibility bounds; (3) Considering both the null and moderate effect thresholds, rating certainty in a trivial or small effect, and rating down for imprecision when the CI crosses either threshold; and (4) presenting more than one certainty of evidence rating. GRADE users may note these challenges and consider applying the solutions to transparently determine the target of their certainty rating and to make certainty assessments accordingly.
Related Concept Videos
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...
Interpretation of Confidence Intervals
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Intervals
A confidence...
Margin of Error
Errors In Hypothesis Tests

