Review Question - QID 219541

Fragility index is a measure of how many alternate outcomes must occur in the results of a research trial to change the statistical outcome of the trial.

The fragility index is a measure of the robustness (or fragility) of the results from a clinical trial that uses dichotomous outcomes. It most often is used when results are statistically significant. It represents the minimum number of participants whose status needs to change from an “event” to a “nonevent” (or vice versa) so that the results switch from statistically significant to non-significant. The larger the fragility index, the more robust the trial’s data.

Ekhtiari et al. reviewed the Fragility Index (FI) as a method for evaluating the robustness of statistically significant findings from randomized controlled trials (RCTs) beyond the P value in trials with dichotomous outcomes. They reported that the FI is defined as the number of patients in one arm of a trial that would have to have a different outcome to change the results of the trial from statistically significant to nonsignificant. They concluded that fragility is another reason to be cautious when conducting or interpreting small trials, and to continue to strive toward large trials to answer important questions.

Gazendam et al. reviewed the current literature to determine if the design and execution of RCTs has improved. They reported that although marked improvements have been made in the design and implementation of trials, there is still considerable room for improvement. They concluded that adequately blinded and powered studies evaluating clinically important outcomes and differences should be key considerations in trial design moving forward.

Incorrect Answers:
Answer 2: The likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that that same result would be expected in a patient without the target disorder is the definition of the likelihood ratio.
Answer 3: The probability that a patient with a positive test actually has the disease is the definition of positive predictive value.
Answer 4: The probability that test results will be positive in patients with the disease is the definition of sensitivity.
Answer 5: The risk of developing disease for people with known exposure compared to risk of developing disease without exposure is the definition of relative risk.