Novel End Point Analytic Techniques and Interpreting Shifts Across the Entire Range of Outcome Scales in Acute Stroke Trials


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Abstract

Background and Purpose—Stroke treatments are generally not curative, but rather alter patient outcome over the entire range of functional measures. Dichotomizing outcome scales reduces computational complexity, but discards substantial outcome information, artificially privileges only a single health state transition as clinically meaningful, and often reduces study power. Newer approaches to endpoint analysis have several advantageous properties.Summary of Review—The global statistic assesses treatment effects on multiple outcome measures simultaneously. However, translating the global statistic multidimensional vector effect at the population level into benefit or harm expected in the individual patient is problematic. Responder analysis adjusts outcome thresholds to patient stroke severity at study entry, identifying achievable goals for each patient. However, responder analysis still discards substantial outcome information. Shift analysis gauges change in outcome distributions over the full range of ascertained outcomes, incorporating benefit and harm at all health state transitions valued by patients and clinicians, and often increasing study power. Translation of findings of shift analyses into clinically accessible terms may be accomplished using the recently developed joint outcome table specification technique, which yields the following values for the number needed to treat for 1 patient to improve in a clinically important manner: nimodipine in subarachnoid hemorrhage, 6.8; coiling over clipping, 5.9; intra-arterial pro-urokinase in acute cerebral ischemia, 4.8; intravenous tissue plasminogen activator, 3.3.Conclusions—Dichotomized, global statistic, responder, and shift analyses each offer distinctive benefits and drawbacks. Choice of primary end point analytic technique should be tailored to the study population, expected treatment response, and study purpose. Shift analysis generally provides the most comprehensive index of a treatment's clinical impact.

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