• Kate L Lapane, Anthony Marmarou, and IMPACT Investigators.
• Department of Epidemiology and Community Health, Virginia Commonwealth University, Richmond, Virginia 23298-0212, USA. jlu1@vcu.edu
• J. Neurotrauma. 2012 Mar 20;29(5):719-26.

AbstractThis study extends our previous investigation regarding the effect of nondifferential dichotomous Glasgow Outcome Scale (GOS) misclassification in traumatic brain injury (TBI) clinical trials to the effect of GOS misclassification on ordinal analysis in TBI clinical trials. The impact of GOS misclassification and ordinal outcome analysis was explored via probabilistic sensitivity analyses using TBI patient datasets from the IMPACT database (n = 9205). Three patterns of misclassification were explored given the pre-specified misclassification distributions. For the random pattern, we specified a trapezoidal distribution (minimum: 80%, mode: 85%, and 95%, maximum: 100%) for both sensitivity and specificity; for the upward pattern, the same trapezoidal distribution for sensitivity but with a perfect specificity; and for the downward pattern, the same trapezoidal distribution for specificity but with a perfect sensitivity. The conventional 95% confidence intervals and simulation intervals, which accounts for the misclassification and random errors together, were reported. The results showed that given the specified misclassification distributions, the misclassification with a random or upward pattern would have caused a slightly underestimated outcome in the observed data. However, the misclassification with a downward pattern would have resulted in an inflated estimation. Thus the sensitivity analysis suggests that the nondifferential misclassification can cause uncertainties on the primary outcome estimation in TBI trials. However, such an effect is likely to be small when ordinal analysis is applied, compared with the impact of dichotomous GOS misclassifications. The result underlines that the ordinal GOS analysis may gain from both statistical efficiency, as suggested by several recent studies, and a relatively smaller impact from misclassification as compared with conventional binary GOS analysis.

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