Several approaches to outcome analysis have recently been applied in acute stroke trials, including shift analysis [6
], a rank test of original ordinal data, ordinal logistic regression [3
], and the concept of a sliding dichotomy [5
Analyzing the full range of ordinal data for functional outcome has been shown to be more statistically efficient than collapsing data [3
]. A recent study compared statistical approaches to the analysis of differences in functional outcome as measured on ordinal scales. The authors concluded that methods that made use of the full range of ordinal outcomes were more sensitive to treatment effects. However, in this study methods that incorporate baseline prognostic information, such as the sliding dichotomy, could not be evaluated [3
Shift analysis [6
] is not a formal test, but a calculus to estimate the proportion of patients moving from one category on the ordinal scale to the next. This can be a useful measure to gain insight in the size of a treatment effect. A disadvantage of shift analysis is that the uncertainty concerning the effect estimate cannot be quantified, as in statistical effect estimation.
The term 'shift test' is used to designate the Mantel-Haenszel (CMH) test that compares two ordered outcome distributions after adjusting for one or more baseline variables. The test provides a p-value, but not an effect estimate. The Van Elteren variant of the test was employed in the analysis of the results of the GAIN trial [8
] and in a post-hoc analysis of NINDS and ECASS-2 trials [9
] Ordinal logistic regression was used to estimate an effect size with its corresponding 95% confidence interval.
Classic statistical methods to compare distributions on an ordinal scale require rank tests. Translation of the results of a rank test (a p-value) to an estimate of the treatment effect is not straightforward; this may be done either by shift analysis (with the aforementioned drawbacks) or by ordinal logistic regression. This analysis does not make use of the available baseline information. Ordinal logistic regression may be used to estimate treatment effects in studies with ordered outcomes. The relative risk of a transition is estimated as an odds ratio. An assumption is that any treatment effect is similar across outcome levels, i.e. the odds of moving from mRS level 3 to 2 are similar to the odds of moving from level 5 to 4. In the Optimizing Analysis of Stroke Trials (OAST) study, a study to assess which statistical approaches are most efficient in analyzing outcomes from stroke trials, the assumption of proportional odds was not met in 8 of the 55 datasets according to the authors, but they did not specify how they tested for this assumption [3
]. Furthermore, this method might be inefficient when treatment effects cluster at one transition. When this approach was tested in a large dataset of patients with severe head injury, it did not perform better than the sliding dichotomy [5
]. Effect estimates should be meaningful from a clinical point of view. In ordinal logistic regression, meaningful interpretation is hampered by the point that moving 1 category up on the mRS may have a different clinical interpretation when it concerns low mRS scores, compared to high mRS scores.
In our view, these are important arguments for not using ordinal regression in the primary outcome analysis.
An advantage of the sliding dichotomy approach is that it makes the least assumptions about the type of patients who will be included in the study, the type of outcome they will experience, and the treatment effect pattern the treatment strategy under study will exert. It provides a simple outcome measure that is relatively easy to interpret, i.e. the relative risk of improvement beyond the level that could be expected from baseline prognostic information [5
]. To our opinion, this approach is to be preferred in the analysis of treatment effects employing the full range of outcomes on the mRS. With the concept of sliding dichotomy, each individual patient's baseline prognosis is taken into account. This approach may be more relevant for clinical practice and may improve statistical power, as patients at the prognostic extremes have the potential to contribute to the estimation of the treatment effect.
An exact, parametric approach to sample size estimation for studies that make use of the sliding dichotomy approach for ordered categorical outcome variables is not available. Simulation studies of randomized clinical trials in traumatic brain injury that made use of the Glasgow Outcome Scale, [10
] suggest that " substantial gains in statistical efficiency can be made". Either of these approaches along with adjustment for baseline covariates gave efficiency gains equivalent to reducing the required sample size by up to 50% [5
We realize that this approach may also have disadvantages. Patients, care givers, and clinicians may consider the results of this way of analysis more difficult to interpret than collapsing data into a binary outcome. We wonder whether this is really the case. Taken at face value, a transition across the boundary between mRS 2 and mRS 3 does not tell us much at all about the real health benefit of a treatment, and a common odds ratio based on sliding dichotomy, may be in fact more informative for those who have grown accustomed to it.