Alexander Krauss1. 1. a London School of Economics ; University College London , London , UK.
Abstract
BACKGROUND: Randomised controlled trials (RCTs) are commonly viewed as the best research method to inform public health and social policy. Usually they are thought of as providing the most rigorous evidence of a treatment's effectiveness without strong assumptions, biases and limitations. OBJECTIVE: This is the first study to examine that hypothesis by assessing the 10 most cited RCT studies worldwide. DATA SOURCES: These 10 RCT studies with the highest number of citations in any journal (up to June 2016) were identified by searching Scopus (the largest database of peer-reviewed journals). RESULTS: This study shows that these world-leading RCTs that have influenced policy produce biased results by illustrating that participants' background traits that affect outcomes are often poorly distributed between trial groups, that the trials often neglect alternative factors contributing to their main reported outcome and, among many other issues, that the trials are often only partially blinded or unblinded. The study here also identifies a number of novel and important assumptions, biases and limitations not yet thoroughly discussed in existing studies that arise when designing, implementing and analysing trials. CONCLUSIONS: Researchers and policymakers need to become better aware of the broader set of assumptions, biases and limitations in trials. Journals need to also begin requiring researchers to outline them in their studies. We need to furthermore better use RCTs together with other research methods. Key messages RCTs face a range of strong assumptions, biases and limitations that have not yet all been thoroughly discussed in the literature. This study assesses the 10 most cited RCTs worldwide and shows that trials inevitably produce bias. Trials involve complex processes - from randomising, blinding and controlling, to implementing treatments, monitoring participants etc. - that require many decisions and steps at different levels that bring their own assumptions and degree of bias to results.
BACKGROUND: Randomised controlled trials (RCTs) are commonly viewed as the best research method to inform public health and social policy. Usually they are thought of as providing the most rigorous evidence of a treatment's effectiveness without strong assumptions, biases and limitations. OBJECTIVE: This is the first study to examine that hypothesis by assessing the 10 most cited RCT studies worldwide. DATA SOURCES: These 10 RCT studies with the highest number of citations in any journal (up to June 2016) were identified by searching Scopus (the largest database of peer-reviewed journals). RESULTS: This study shows that these world-leading RCTs that have influenced policy produce biased results by illustrating that participants' background traits that affect outcomes are often poorly distributed between trial groups, that the trials often neglect alternative factors contributing to their main reported outcome and, among many other issues, that the trials are often only partially blinded or unblinded. The study here also identifies a number of novel and important assumptions, biases and limitations not yet thoroughly discussed in existing studies that arise when designing, implementing and analysing trials. CONCLUSIONS: Researchers and policymakers need to become better aware of the broader set of assumptions, biases and limitations in trials. Journals need to also begin requiring researchers to outline them in their studies. We need to furthermore better use RCTs together with other research methods. Key messages RCTs face a range of strong assumptions, biases and limitations that have not yet all been thoroughly discussed in the literature. This study assesses the 10 most cited RCTs worldwide and shows that trials inevitably produce bias. Trials involve complex processes - from randomising, blinding and controlling, to implementing treatments, monitoring participants etc. - that require many decisions and steps at different levels that bring their own assumptions and degree of bias to results.
Authors: Allan I Pack; Ulysses J Magalang; Bhajan Singh; Samuel T Kuna; Brendan T Keenan; Greg Maislin Journal: Sleep Date: 2021-02-12 Impact factor: 5.849
Authors: Allan I Pack; Ulysses J Magalang; Bhajan Singh; Samuel T Kuna; Brendan T Keenan; Greg Maislin Journal: Sleep Date: 2021-04-09 Impact factor: 5.849
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