Predicting the placebo response in oa to improve the precision of the treatment effect estimation

Branders, Samuel; Dananberg, Jamie; Clermont, Frantz; Xie, Benhuai; Hsu, Bang‐Gee; Visich, Jennifer; Khan, Aamir Z.; Masaki, L.; Pereira, Álvaro

doi:10.1016/j.joca.2021.05.032

Cited by 2 publications

(2 citation statements)

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“…Data from four analgesia clinical trials 24,31 were used to fit the composite covariate. The four chronic pain studies included patients suffering from peripheral neuropathic pain or painful osteoarthritis of the knee and hip.…”

Section: Real Life Applicationmentioning

confidence: 99%

“…The composite covariate approach was applied in a phase 2 trial testing the efficacy of a single-dose intra-articular injection in patients suffering from painful osteoarthritis (OA) of the knee 23,24 . In OA and more generally in chronic pain, the magnitude and the variability of the placebo response have a blurring effect when testing for the superiority of active compounds compared to placebo [25][26][27][28] .…”

Section: Real Life Applicationmentioning

confidence: 99%

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Leveraging historical data to optimize the number of covariates and their explained variance in the analysis of randomized clinical trials.

Branders¹,

Pereira²,

Ernst

et al. 2021

Stat Methods Med Res

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The amount of data collected from patients involved in clinical trials is continuously growing. All baseline patient characteristics are potential covariates that could be used to improve clinical trial analysis and power. However, the limited number of patients in phases I and II studies restricts the possible number of covariates included in the analyses. In this paper, we investigate the cost/benefit ratio of including covariates in the analysis of clinical trials with a continuous outcome. Within this context, we address the long-running question “What is the optimum number of covariates to include in a clinical trial?” To further improve the benefit/cost ratio of covariates, historical data can be leveraged to pre-specify the covariate weights, which can be viewed as the definition of a new composite covariate. Here we analyze the use of a composite covariate to improve the estimated treatment effect in small clinical trials. A composite covariate limits the loss of degrees of freedom and the risk of overfitting.

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Section: Real Life Applicationmentioning

confidence: 99%