Treatment effects for which shift or binary analyses are advantageous in acute stroke trials

Abstract

Background: In acute stroke trials, functional outcome may be analyzed by dichotomizing ordinal outcome scales or by evaluating the entire scale range (shift analysis). The conditions under which shift or binary analysis will be more efficient have not been previously well delineated.

Methods: Model randomized clinical trials employing the modified Rankin Scale of global handicap were constructed to reflect 1) mild benefits experienced across all ranges of stroke severity (neuroprotective effect), 2) substantial benefits across all ranges of stroke severity (early recanalization effect), 3) substantial benefits across wide range of stroke severity but with limited ability to achieve fully normal outcome (late recanalization effect), 4) benefits clustered at unexpected health state transitions.

Results: In neuroprotective models, shift analysis was the most efficient technique in detecting a treatment effect. In the early recanalization models, dichotomization at excellent outcome and shift analysis were of comparable efficiency, both superior to dichotomization at good outcome. In the late recanalization models, dichotomization at good outcome performed best, shift analysis less well, and dichotomization at excellent outcome poorly. In the unexpected benefits model, shift analysis substantially outperformed dichotomization analyses. These patterns held among the seven actual acute trials reporting full range Rankin outcomes and showing treatment benefit identified in the literature.

Conclusions: The pattern of treatment effect of the intervention determines whether shift analysis or simple dichotomized analysis will be more efficient. Shift analysis is especially advantageous when treatments confer a relatively uniform, mild benefit to patients over a wide range of stroke severities or confer benefits at unexpected but clinically important health state transitions.

Figure 1 The four analyzed treatment effect models For each, the staircase schematic shows the pattern of the treatment effect, the upper table shows the percentile distribution of outcomes among the six Rankin levels in the treatment and control groups, and the bottom table shows the sample size required by each of the endpoint analytic techniques.

Figure 2 Treatment effect profile maps In individual bar graphs, each bar represents the absolute difference in the percent of patients achieving a good outcome under active treatment vs control, when good outcome is defined as modified Rankin scale (mRS) = 0 (column 1), mRS = 0–1 (column 2), mRS = 0–2 (column 3), mRS = 0–3 (column 4), mRS = 0–4 (column 5). In the overall figure, the four bar graphs in A are the treatment effect profile maps for the four models. The four bar graphs in B are the treatment effect profile maps for actual clinical trials that resemble each of the prototypical models.