ArticleTrials2024
Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches.
Article in Trials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Not yet cited in PubMed.
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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.
A Multicenter, International Randomized, 2x2 Factorial Design Study to Evaluate the Effects of Lantus (Insulin Glargine) Versus Standard Care, and of Omega-3 Fatty Acids Versus Placebo, in Reducing Cardiovascular Morbidity and Mortality in High Risk People With Impaired Fasting Glucose (IFG), Impaired Glucose Tolerance (IGT) or Early Type 2 Diabetes Mellitus: The ORIGIN Trial (Outcome Reduction With Initial Glargine Intervention)
ONTARGET ONgoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial A Large, Simple Randomized Trial of an Angiotensin II Receptor Antagonist (Telmisartan) and an ACE-Inhibitor (Ramipril) in Patients at High Risk for Cardiovascular Events and TRANSCEND Telmisartan Randomized AssessmeNt Study in aCE iNtolerant Subjects With Cardiovascular Disease. A Parallel Study Comparing the Effects of Telmisartan With Placebo and Outcomes in Patients at High Risk for Cardiovascular Events and Intolerant to ACE-I.
A Randomized Controlled Trial of Rivaroxaban for the Prevention of Major Cardiovascular Events in Patients With Coronary or Peripheral Artery Disease (COMPASS - Cardiovascular OutcoMes for People Using Anticoagulation StrategieS).
Multicenter, Randomized, Double-blind, Double-dummy, Active-comparator, Event-driven, Superiority Phase III Study of Secondary Prevention of Stroke and Prevention of Systemic Embolism in Patients With a Recent Embolic Stroke of Undetermined Source (ESUS), Comparing Rivaroxaban 15 mg Once Daily With Aspirin 100 mg
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10 authors.
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Abstract
backgroundThe Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest.
methodsAlternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit).
resultsThe beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches.
conclusionWhen analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure. TRIALS REGISTRATION: ORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.
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