%0 Journal Article %1 Berger1999 %A Berger, V W %A Exner, D V %D 1999 %J Controlled clinical trials %K ComputerSimulation DataCollection DataCollection:statistics&numericaldata Humans RandomizedControlledTrialsasTopic:statistics ReproducibilityofResults SelectionBias RCT %N 4 %P 319-27 %T Detecting selection bias in randomized clinical trials. %U http://www.ncbi.nlm.nih.gov/pubmed/10440559 %V 20 %X Lack of concealment of allocation in randomized clinical trials can invite selection bias, which is the preferential enrollment of specific patients into one treatment group over another. For example, patients more likely to respond may be enrolled only when the next treatment to be assigned is known to be the active treatment, and patients less likely to respond may be enrolled only when the next treatment to be assigned is known to be the control. Despite the fact that selection bias can compromise both the internal and external validity of trials, little methodology has been developed for its detection. An investigator may test the success of the randomization by comparing baseline characteristics across treatment groups, but such test is limited by the potential inability of the measured baseline variables to predict response. A new method for detecting selections bias, based on response data only, is developed for the case in which a small block size, and either unmasking of treatment codes or an open-label design, have compromised the concealment of allocation. This new method complements baseline comparisons, and is sensitive to detect selection bias even in situations in which baseline comparisons are not.

@article{Berger1999, abstract = {Lack of concealment of allocation in randomized clinical trials can invite selection bias, which is the preferential enrollment of specific patients into one treatment group over another. For example, patients more likely to respond may be enrolled only when the next treatment to be assigned is known to be the active treatment, and patients less likely to respond may be enrolled only when the next treatment to be assigned is known to be the control. Despite the fact that selection bias can compromise both the internal and external validity of trials, little methodology has been developed for its detection. An investigator may test the success of the randomization by comparing baseline characteristics across treatment groups, but such test is limited by the potential inability of the measured baseline variables to predict response. A new method for detecting selections bias, based on response data only, is developed for the case in which a small block size, and either unmasking of treatment codes or an open-label design, have compromised the concealment of allocation. This new method complements baseline comparisons, and is sensitive to detect selection bias even in situations in which baseline comparisons are not.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Berger, V W and Exner, D V}, biburl = {https://www.bibsonomy.org/bibtex/212bc580e366203e0f7a0bd90b833dfb3/jepcastel}, interhash = {89433b23b2cc090d27b631b0801be066}, intrahash = {12bc580e366203e0f7a0bd90b833dfb3}, issn = {0197-2456}, journal = {Controlled clinical trials}, keywords = {ComputerSimulation DataCollection DataCollection:statistics&numericaldata Humans RandomizedControlledTrialsasTopic:statistics ReproducibilityofResults SelectionBias RCT}, month = {8}, note = {3092<m:linebreak></m:linebreak>Mesures de contrast}, number = 4, pages = {319-27}, pmid = {10440559}, timestamp = {2023-05-04T08:59:38.000+0200}, title = {Detecting selection bias in randomized clinical trials.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10440559}, volume = 20, year = 1999 }