ArticleContemporary clinical trials2011
Quantifying the cost in power of ignoring continuous covariate imbalances in clinical trial randomization.
Article in Contemporary clinical trials, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Multi-arm covariate-adaptive randomization.Science China. Mathematics · 2023Article
- Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint.Statistical methods in medical research · 2022Article
- Bryostatin Placebo-Controlled Trials Indicate Cognitive Restoration Above Baseline for Advanced Alzheimer's Disease in the Absence of Memantine1.Journal of Alzheimer's disease : JAD · 2022Article
- Minimal sufficient balance-a new strategy to balance baseline covariates and preserve randomness of treatment allocation.Statistical methods in medical research · 2015Article
- Measuring continuous baseline covariate imbalances in clinical trial data.Statistical methods in medical research · 2015Article
- Continuous covariate imbalance and conditional power for clinical trial interim analyses.Contemporary clinical trials · 2014Article
- Bias, precision and statistical power of analysis of covariance in the analysis of randomized trials with baseline imbalance: a simulation study.BMC medical research methodology · 2014Article
- Covariate imbalance and adjustment for logistic regression analysis of clinical trial data.Journal of biopharmaceutical statistics · 2013Article
- Assessment and implication of prognostic imbalance in randomized controlled trials with a binary outcome--a simulation study.PloS one · 2012Article
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Authors and funding
4 authors.
Funding
Abstract
Motivated by potentially serious imbalances of continuous baseline covariates in clinical trials, we investigated the cost in statistical power of ignoring the balance of these covariates in treatment allocation design for a logistic regression model. Based on data from a clinical trial of acute ischemic stroke treatment, computer simulations were used to create scenarios varying from the best possible baseline covariate balance to the worst possible imbalance, with multiple balance levels between the two extremes. The likelihood of each scenario occurring under simple randomization was evaluated. The power of the main effect test for treatment was examined. Our simulation results show that the worst possible imbalance is highly unlikely, but it can still occur under simple random allocation. Also, power loss could be nontrivial if balancing distributions of important continuous covariates were ignored even if adjustment is made in the analysis for important covariates. This situation, although unlikely, is more serious for trials with a small sample size and for covariates with large influence on primary outcome. These results suggest that attempts should be made to balance known prognostic continuous covariates at the design phase of a clinical trial even when adjustment is planned for these covariates at the analysis.
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