Journal of clinical epidemiology
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A survey of randomized controlled trials found that almost a quarter of trials had more than 10% of responses missing for the primary outcome. There are a number of ways in which data could be missing: the subject is unable to provide it, or they withdraw, or become lost to follow-up. Such attrition means that balance in baseline characteristics for those randomized may not be maintained in the subsample who has outcome data. For individual trials, if the attrition is systematic and linked to outcome, then this will result in biased estimates of the overall effect. It then follows that if such trials are combined in a meta-analysis, it will result in a biased estimate of the overall effect and be misleading. The aim of this study was to investigate the impact of attrition on baseline imbalance within individual trials and across multiple trials. ⋯ Although, in theory, attrition can introduce selection bias in randomized trials, we did not find sufficient evidence to support this claim in our convenience sample of trials. However, the number of trials included was relatively small, which may have led to small but important differences in outcomes being missed. In addition, only 2 of 10 trials included had attrition levels greater than 15% suggesting a low level of potential bias. Meta-analyses and systematic reviews should always consider the impact of attrition on baseline imbalances and where possible any baseline imbalances in the analyzed data set and their impact on the outcomes reported.
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We assessed the reliability and validity of two measures of change, one retrospective (the Global Rating of Change Scale [GRCS]) and one prospective (the Punum Ladder), and the relative utility of the two methods of assessing change and establishing the minimal important difference (MID) of the Cough Quality of Life Questionnaire (CQLQ), a reliable and valid cough-specific quality-of-life (QoL) instrument. ⋯ The prospective Punum Ladder is likely to be more useful, because it reflects the actual change in QoL over time in a less biased and more accurate way than the retrospective GRCS.
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Health care providers, policy makers, and importantly patients themselves are increasingly interested in the outcomes of clinical trials yet often expect different questions to be addressed than those commonly asked in conventional phase 3 trials. ⋯ Although we do not believe that RWTs will supplant conventional RCTs, properly designed RWTs will enrich our understanding of the effectiveness of new health care interventions and better inform patients and health care providers alike.
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Meta-analyses of randomized controlled trials (RCTs) provide the highest level of evidence regarding the effectiveness of interventions. Less is known about how they are used to inform the design and reporting of RCTs. ⋯ Consulting previous research before embarking on a new trial and basing decisions about future research on the impact on an updated meta-analysis will make the reporting of research more coherent and the design of new RCTs more efficient.
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To generate anchor-based values for the "minimally important difference" (MID) for a number of commonly used patient-reported outcome (PRO) measures and to examine whether these values could be applied across the continuum of preoperative patient severity. ⋯ In general, there is little association between baseline severity and MID values. However, a moderate association persists for some measures, and it is recommended that researchers continue to test for this relationship when generating anchor-based MID values from change scores.