The added value of ordinal analysis in clinical trials: an example in traumatic brain injury

Roozenbeek, Bob; Lingsma, Hester F.; Perel, Pablo; Edwards, Phil; Roberts, Ian; Murray, Gordon D.; Maas, Andrew I. R.; Steyerberg, Ewout W.

doi:10.1186/cc10240

Cited by 144 publications

(133 citation statements)

References 26 publications

(32 reference statements)

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“…18–21 In these trials, relative to FLU-IVIG, different parameter values (e.g. treatment effect size) may modify the magnitude of the effect of the four factors evaluated in this article on power.…”

Section: Discussionmentioning

confidence: 99%

Analysis of an ordinal endpoint for use in evaluating treatments for severe influenza requiring hospitalization

et al. 2017

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Background/Aims A single best endpoint for evaluating treatments of severe influenza requiring hospitalization has not been identified. A novel six-category ordinal endpoint of patient status is being used in a randomized controlled trial (FLU-Intravenous Immunoglobulin - FLU-IVIG) of intravenous immunoglobulin. We systematically examine four factors regarding the use of this ordinal endpoint that may affect power from fitting a proportional odds model: (1) deviations from the proportional odds assumption which result in the same overall treatment effect as specified in the FLU-IVIG protocol and which result in a diminished overall treatment effect, (2) deviations from the distribution of the placebo group assumed in the FLU-IVIG design, (3) the effect of patient misclassification among the six categories, and (4) the number of categories of the ordinal endpoint. We also consider interactions between the treatment effect (i.e. factor 1) and each other factor. Methods We conducted a Monte Carlo simulation study to assess the effect of each factor. To study factor 1, we developed an algorithm for deriving distributions of the ordinal endpoint in the two treatment groups that deviated from proportional odds while maintaining the same overall treatment effect. For factor 2, we considered placebo group distributions which were more or less skewed than the one specified in the FLU-IVIG protocol by adding or subtracting a constant from the cumulative log odds. To assess factor 3, we added misclassification between adjacent pairs of categories that depend on subjective patient/clinician assessments. For factor 4, we collapsed some categories into single categories. Results Deviations from proportional odds reduced power at most from 80% to 77% given the same overall treatment effect as specified in the FLU-IVIG protocol. Misclassification and collapsing categories can reduce power by over 40 and 10 percentage points, respectively, when they affect categories with many patients and a discernible treatment effect. But collapsing categories that contain no treatment effect can raise power by over 20 percentage points. Differences in the distribution of the placebo group can raise power by over 20 percentage points or reduce power by over 40 percentage points depending on how patients are shifted to portions of the ordinal endpoint with a large treatment effect. Conclusion Provided that the overall treatment effect is maintained, deviations from proportional odds marginally reduce power. However, deviations from proportional odds can modify the effect of misclassification, the number of categories, and the distribution of the placebo group on power. In general, adjacent pairs of categories with many patients should be kept separate to help ensure that power is maintained at the pre-specified level.

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Section: Discussionmentioning

confidence: 99%

Analysis of an ordinal endpoint for use in evaluating treatments for severe influenza requiring hospitalization

et al. 2017

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“…This modeling strategy estimates a single beta coefficient for each predictor and is analogous to a summary of the odds ratios obtained from separate binary logistic regression models 7–9 . It also allows for the estimation of risk for each outcome (eg, disseminated infection) as well as risk for more than one outcome (eg, local or disseminated infection) using a single model.…”

Section: Methodsmentioning

confidence: 99%

A Clinical Prediction Algorithm to Stratify Pediatric Musculoskeletal Infection by Severity

Benvenuti

Mignemi³

et al. 2019

Journal of Pediatric Orthopaedics

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Objective There are currently no algorithms for early stratification of pediatric musculoskeletal infection (MSKI) severity that are applicable to all types of tissue involvement. In this study, the authors sought to develop a clinical prediction algorithm that accurately stratifies infection severity based on clinical and laboratory data at presentation to the emergency department. Methods An IRB-approved retrospective review was conducted to identify patients aged 0–18 who presented to the pediatric emergency department at a tertiary care children’s hospital with concern for acute MSKI over a five-year period (2008–2013). Qualifying records were reviewed to obtain clinical and laboratory data and to classify in-hospital outcomes using a three-tiered severity stratification system. Ordinal regression was used to estimate risk for each outcome. Candidate predictors included age, temperature, respiratory rate, heart rate, C-reactive protein, and peripheral white blood cell count. We fit fully specified (all predictors) and reduced models (retaining predictors with a p-value ≤ 0.2). Discriminatory power of the models was assessed using the concordance (c)-index. Results Of the 273 identified children, 191 (70%) met inclusion criteria. Median age was 5.8 years. Outcomes included 47 (25%) children with inflammation only, 41 (21%) with local infection, and 103 (54%) with disseminated infection. Both the full and reduced models accurately demonstrated excellent performance (full model c-index 0.83, 95% CI [0.79–0.88]; reduced model 0.83, 95% CI [0.78–0.87]). Model fit was also similar, indicating preference for the reduced model. Variables in this model included C-reactive protein, pulse, temperature, and an interaction term for pulse and temperature. The odds of a more severe outcome increased by 30% for every 10-unit increase in C-reactive protein. Conclusions Clinical and laboratory data obtained in the emergency department may be used to accurately differentiate pediatric MSKI severity. The predictive algorithm in this study stratifies pediatric MSKI severity at presentation irrespective of tissue involvement and anatomic diagnosis. Prospective studies are needed to validate model performance and clinical utility.

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“…That said, a theoretical basis for both shift analysis and sliding dichotomy (the latter uses prognostic information) appears preferable to fixed dichotomy to capture more information about the response to an intervention. Recent studies based on simulations, 13 the reanalysis of a major head injury study, 18 and the prospective analysis of a major stroke trial 19 have all demonstrated very substantial efficiency gains from the application of ordinal analysis. Perhaps the GOS and mRS, which were not developed for use in SAH, do not assess outcomes that are relevant to patients with SAH.…”

Section: Discussionmentioning

confidence: 99%

Sliding dichotomy compared with fixed dichotomization of ordinal outcome scales in subarachnoid hemorrhage trials

Ilodigwe

Stat.

Murray

et al. 2013

JNS

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Object In randomized clinical trials of subarachnoid hemorrhage (SAH) in which the primary clinical outcomes are ordinal, it has been common practice to dichotomize the ordinal outcome scale into favorable versus unfavorable outcome. Using this strategy may increase sample sizes by reducing statistical power. Authors of the present study used SAH clinical trial data to determine if a sliding dichotomy would improve statistical power. Methods Available individual patient data from tirilazad (3552 patients), clazosentan (the Clazosentan to Overcome Neurological Ischemia and Infarction Occurring After Subarachnoid Hemorrhage trial [CONSCIOUS-1], 413 patients), and subarachnoid aneurysm trials (the International Subarachnoid Aneurysm Trial [ISAT], 2089 patients) were analyzed. Treatment effect sizes were examined using conventional fixed dichotomy, sliding dichotomy (logical or median split methods), or proportional odds modeling. Whether sliding dichotomy affected the difference in outcomes between the several age and neurological grade groups was also evaluated. Results In the tirilazad data, there was no significant effect of treatment on outcome (fixed dichotomy: OR = 0.92, 95% CI 0.80–1.07; and sliding dichotomy: OR = 1.02, 95% CI 0.87–1.19). Sliding dichotomy reversed and increased the difference in outcome in favor of the placebo over clazosentan (fixed dichotomy: OR = 1.06, 95% CI 0.65–1.74; and sliding dichotomy: OR = 0.85, 95% CI 0.52–1.39). In the ISAT data, sliding dichotomy produced identical odds ratios compared with fixed dichotomy (fixed dichotomy vs sliding dichotomy, respectively: OR = 0.67, 95% CI 0.55–0.82 vs OR = 0.67, 95% CI 0.53–0.85). When considering the tirilazad and CONSCIOUS-1 groups based on age or World Federation of Neurosurgical Societies grade, no consistent effects of sliding dichotomy compared with fixed dichotomy were observed. Conclusions There were differences among fixed dichotomy, sliding dichotomy, and proportional odds models in the magnitude and precision of odds ratios, but these differences were not as substantial as those seen when these methods were used in other conditions such as head injury. This finding suggests the need for different outcome scales for SAH.

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The added value of ordinal analysis in clinical trials: an example in traumatic brain injury

Cited by 144 publications

References 26 publications

Analysis of an ordinal endpoint for use in evaluating treatments for severe influenza requiring hospitalization

Analysis of an ordinal endpoint for use in evaluating treatments for severe influenza requiring hospitalization

A Clinical Prediction Algorithm to Stratify Pediatric Musculoskeletal Infection by Severity

Sliding dichotomy compared with fixed dichotomization of ordinal outcome scales in subarachnoid hemorrhage trials

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