Concerning Sichieri R, Cunha DB: Obes Facts 2014;7:221-232. The Assertion that Controlling for Baseline (Pre-Randomization) Covariates in Randomized Controlled Trials Leads to Bias Is False
| Autor: | David B. Allison, J. Michael Oakes, Michelle M Bohan Brown, John A. Dawson, Andrew W. Brown, Peng Li, Scott W. Keith, Kathryn A. Kaiser |
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| Rok vydání: | 2015 |
| Předmět: |
050103 clinical psychology
Health (social science) Randomization Randomized experiment MEDLINE lcsh:TX341-641 030209 endocrinology & metabolism law.invention 03 medical and health sciences 0302 clinical medicine Randomized controlled trial Frequentist inference law Physiology (medical) Statistics Covariate Medicine 0501 psychology and cognitive sciences Baseline (configuration management) lcsh:RC620-627 business.industry 05 social sciences Assertion 3. Good health lcsh:Nutritional diseases. Deficiency diseases business lcsh:Nutrition. Foods and food supply |
| Zdroj: | Obesity Facts, Vol 8, Iss 2, Pp 127-129 (2015) |
| ISSN: | 1662-4033 1662-4025 |
| DOI: | 10.1159/000381434 |
| Popis: | We read with interest the article ‘Unbalanced Baseline in School-Based Interventions to Prevent Obesity: Adjustment Can Lead to Bias – a Systematic Review’ [1] , hereafter ‘ the article ’. We agree with the authors that more rigor is needed in research on obesity treatment and prevention, and in the design, analysis, and reporting of cluster randomized controlled trials (cRCTs) [2] , also called group randomized trials. Unfortunately, rather than offering clarifying information, the article is based on incorrect statistical reasoning and inaccurate statements about what past publications have shown. The fundamental conclusion as stated in its title and elsewhere in the article is incorrect. For example, the statement ‘Although adjusting for the baseline values of parameters (sic, variables – Li et al.) that are highly influenced by baseline values is a standard procedure, this approach can bias the results …’ is simply untrue. Such erroneous conclusions could lead researchers to avoid legitimate power-enhancing analytic methods, and should be retracted. Adjusting for pre-randomization covariates in randomized trials does not introduce bias nor invalidate significance tests. This is known from statistical principles and requires neither simulation nor meta-analyses. By definition and design, in randomized experiments prerandomization covariates are independent of treatment assignment, with the exception of chance deviations which are accommodated in the calculation of frequentist significance tests and their associated p values. If the outcome variable (Y) is measured pre-randomization (Y 0 ) and at the end of the study (Y 1 ), then using either Y 1 or Y 0 – Y 1 as outcomes and either |
| Databáze: | OpenAIRE |
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