Interpretation of INTERACT-2 Study in Intracerebral Hemorrhage

[R_cubed]

@yetanotherpatel

Hopefully my questions make sense. I’m happy to provide any clarification or further clinical background. Thank you and looking forward to discussing further.

This is a great question, and I understand all too well the challenges of examining the stroke literature.

I will do my best to link to some useful citations and threads here that suggest an answer to some of your questions, and hopefully the others with more expertise can elaborate.

As for your question:

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While I assume that most of the datamethods community would advocate for the use of an ordinal regression, this seems to be particularly controversial on Twitter.

Aside from the authority Dr. Harrell (our host), who recommends ordinal logistic regression as a widely applicable technique, the following citations also mention it as one of the preferred ways of examining stroke data.

Can We Improve the Statistical Analysis of Stroke Trials? Statistical Reanalysis of Functional Outcomes in Stroke Trials: The Optimising Analysis of Stroke Trials (OAST) Collaboration
https://www.ahajournals.org/doi/10.1161/strokeaha.106.474080

From the abstract:

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Conclusions— When analyzing functional outcome from stroke trials, statistical tests which use the original ordered data are more efficient and more likely to yield reliable results. Suitable approaches included ordinal logistic regression, t test, and robust ranks test.

Statistical Analysis of the Primary Outcome in Acute Stroke Trials
https://www.ahajournals.org/doi/full/10.1161/STROKEAHA.111.641456

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Common outcome scales in acute stroke trials are ordered categorical or pseudocontinuous in structure but most have been analyzed as binary measures. The use of fixed dichotomous analysis of ordered categorical outcomes after stroke (such as the modified Rankin Scale) is rarely the most statistically efficient approach and usually requires a larger sample size to demonstrate efficacy than other approaches.

We can eliminate the other methods of inference (t-tests, etc.) as the data from these scales is inherently ordinal. For example:

The Missing Medians: Exclusion of Ordinal Data from Meta-Analyses

https://www.sciencedirect.com/science/article/abs/pii/S0022103117307746

These ordinal clinical scales require more sophisticated techniques to analyze. The problems Dr. Harrell mentions in the depression literature that uses the Ham-D is also a problem with the neurological outcome literature (as cited in the Missing Medians) article. In this thread, he describes the use of ordinal proportional odds logistic model, along with nonlinear smoothing methods, to extract information that would be missed by other methods.

Exemplary QOL analyses that avoid change-from-baseline blunders?

The ISCHEMIA study QOL analyses led by John Spertus are role models for how to do this, avoiding change from baseline and using Bayesian models to get more interpretable and accurate results. We’ll have to wait for a paper or preprint to see analysis details. I have a perfect example but of how a proper analysis yields a much different (and more correct result) but it uses proprietary data so I can’t show it. But here’s a description. The Hamilton-D depression scale is often used in antidepr…

Using ordinal regression eliminates the meaningless disputes about “cut offs.” Any particular cut off is arbitrary. See the discussion in Chapter 18 of Biostatistics for Biomedical Research (aka BBR (pdf)). That is a gold mine of wisdom regarding applied statistics. Pay particular attention to the discussion on the information loss by dichotomization of continuous variables. Information loss implies a loss of power, and an effective reduction in the sample size.

Quality of life surveys and proportional odds models

Hello all, I was asked by a colleague to examine a data set that looked at pre-operative frailty as a predictor of post-operative quality of life, but I am struggling to identify a suitable model due to the nature of the input variables. The outcome is a RAND 36-item health survey questionnaire which collapses down to eight categories. For example; one category, “physical functioning”, is the mean of 10 individual questions, set against a three point scale; “pain”, is the mean of two questions…

I forgot to add: Mixed effects ordinal regression is also recommended as a widely applicable technique for the meta-analyses of individual patient data, or aggregate data summaries.

I doubt I would have ever found this technique if I had not learned of the proportional odds model from BBR and Regression Modelling Strategies (RMS) first.

A general framework for the use of logistic regression models in meta-analysis...

I’d ask the critics of ordinal regression, how they would aggregate the results from studies that use different cut points into a meta-analysis without access to individual patient data.

There is no coherent method to do so. The different cut points lead to heterogeneous data sets, and the best you could do, being extremely charitable, is a vote count, taking for granted that the direction of the outcome was accurately reported.

If we are to do useful, economical, and ethical research for conditions like ICH, there should be no doubt that the proportional odds ordinal logistic model, within a broader, Bayesian decision theoretic framework is the perspective to take. If we place RCTs into a broader, formal decision making context, more informative yet economical experiments can be designed that answer the questions front line clinicians really want answered.