Trial reportBMC medical research methodology2020
Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum score outcomes.
Trial report in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT01571531 (European Multicentre Study of Human SCI), which is not on this map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
European Multicentre Study of Human SCI
Who cites it
3 citing papers in PubMed, 10 citations in OpenAlex.
- Applicability of Regression Models for the Analysis of Ordinal PANSS Data in Schizophrenia: A Cohort Study.Consortium psychiatricum · 2026Article
- Article
- Segmental motor recovery after cervical spinal cord injury relates to density and integrity of corticospinal tract projections.Nature communications · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSum scores of ordinal outcomes are common in randomized clinical trials. The approaches routinely employed for assessing treatment effects, such as t-tests or Wilcoxon tests, are not particularly powerful in detecting changes in relevant parameters or lack the ability to incorporate baseline information. Hence, tailored statistical methods are needed for the analysis of ordinal outcomes in clinical research.
methodsWe propose baseline-adjusted proportional odds logistic regression models to overcome previous limitations in the analysis of ordinal outcomes in randomized clinical trials. For the validation of our method, we focus on common ordinal sum score outcomes of neurological clinical trials such as the upper extremity motor score, the spinal cord independence measure, and the self-care subscore of the latter. We compare the statistical power of our models to other conventional approaches in a large simulation study of two-arm randomized clinical trials based on data from the European Multicenter Study about Spinal Cord Injury (EMSCI, ClinicalTrials.gov Identifier: NCT01571531). We also use the new method as an alternative analysis of the historical Sygen®clinical trial.
resultsThe simulation study of all postulated trial settings demonstrated that the statistical power of the novel method was greater than that of conventional methods. Baseline adjustments were more suited for the analysis of the upper extremity motor score compared to the spinal cord independence measure and its self-care subscore.
conclusionsThe proposed baseline-adjusted proportional odds models allow the global treatment effect to be directly interpreted. This clear interpretation, the superior statistical power compared to the conventional analysis approaches, and the availability of open-source software support the application of this novel method for the analysis of ordinal outcomes of future clinical trials.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.