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The Article Alert for the week of November 24, 2014 (sample articles)
Mickenautsch S, Fu B, Gudehithlu S, Berger VW. Accuracy of the Berger-Exner test for detecting third-order selection bias in randomised controlled trials: a simulation-based investigation. BMC Med.Res.Methodol. 2014 Oct 6;14(1):114. PMID: 25283963.
Background: Randomised controlled trials (RCT) are highly influential upon medical decisions. Thus RCTs must not distort the truth. One threat to internal trial validity is the correct prediction of future allocations (selection bias). The Berger-Exner test detects such bias but has not been widely utilized in practice. One reason for this non-utilisation may be a lack of information regarding its test accuracy. The objective of this study is to assess the accuracy of the Berger-Exner test on the basis of relevant simulations for RCTs with dichotomous outcomes.
Methods: Simulated RCTs with various parameter settings were generated, using R software, and subjected to bias-free and selection bias scenarios. The effect size inflation due to bias was quantified. The test was applied in both scenarios and the pooled sensitivity and specificity, with 95% confidence intervals for alpha levels of 1%, 5%, and 20%, were computed. Summary ROC curves were generated and the relationships of parameters with test accuracy were explored.
Results: An effect size inflation of 71% - 99% was established. Test sensitivity was 1.00 (95% CI: 0.99 - 1.00) for alpha level 1%, 5%, and 20%; test specificity was 0.94 (95% CI: 0.93 - 0.96); 0.82 (95% CI: 0.80 - 0.84), and 0.56 (95% CI: 0.54 - 0.58) for alpha 1%, 5%, and 20%, respectively. Test accuracy was best with the maximal procedure used with a maximum tolerated imbalance (MTI) = 2 as the randomisation method at alpha 1%.
Conclusions: The results of this simulation study suggest that the Berger-Exner test is generally accurate for identifying third-order selection bias.
- FREE FULL TEXT: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209086/pdf/12874_2013_Article_1127.pdf
- DOI: http://dx.doi.org/10.1186/1471-2288-14-114
- PubMed: http://www.ncbi.nlm.nih.gov/pubmed/25283963
- PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209086/
Viergever RF, Karam G, Reis A, Ghersi D. The quality of registration of clinical trials: still a problem. PLoS One. 2014 Jan 10;9(1):e84727. PMID: 24427293.
Introduction: The benefits of clinical trials registration include improved transparency on clinical trials for healthcare workers and patients, increased accountability of trialists, the potential to address publication bias and selective reporting, and possibilities for research collaboration and prioritization. However, poor quality of information in registered records of trials has been found to undermine these benefits in the past. Trialists' increasing experience with trial registration and recent developments in registration systems may have positively affected data quality. This study was conducted to investigate whether the quality of registration has improved.
Methods: We repeated a study from 2009, using the same methods and the same research team. A random sample of 400 records of clinical trials that were registered between 01/01/2012 and 01/01/2013 was taken from the International Clinical Trials Registry Platform (ICTRP) and assessed for the quality of information on 1) contact details, 2) interventions and 3) primary outcomes. Results were compared to the equivalent assessments from our previous study.
Results: There was a small and not statistically significant increase from 81.0% to 85.5% in the percentage of records that provided a name of a contact person. There was a significant increase from 68.7% to 74.9% in the number of records that provided either an email address or a telephone number. There was a significant increase from 44.2% to 51.9% in the number of intervention arms that were complete in registering intervention specifics. There was a significant increase from 38.2% to 57.6% in the number of primary outcomes that were specific measures with a meaningful timeframe. Approximately half of all trials continued to be retrospectively registered.
Discussion: There have been small but significant improvements in the quality of registration since 2009. Important problems with quality remain and continue to constitute an impediment to the meaningful utilization of registered trial information.
- FREE FULL TEXT: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888400/pdf/pone.0084727.pdf
- DOI: http://dx.doi.org/10.1371/journal.pone.0084727
- PubMed: http://www.ncbi.nlm.nih.gov/pubmed/24427293
- PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888400/
Fielding S, Fayers P, Ramsay CR. Analysing randomised controlled trials with missing data: choice of approach affects conclusions. Contemp.Clin.Trials. 2012 May;33(3):461-9. PMID: 22265924.
Background: The publication of a wrong conclusion from a randomised trial could have disastrous consequences. Missing data are unavoidable in most studies, but ignoring the problem may introduce bias to the results. Finding an appropriate way to deal with missing data is of paramount importance. We show how the choice of analysis method can impact on the conclusion of the trial with regard to the quality of life outcomes.
Methods: Various analysis strategies (analysis of covariance, linear mixed effects model) with and without imputation were carried out to assess treatment difference in four quality of life outcomes in an example clinical trial.
Results: Across all four quality of life outcomes, the various analysis approaches provided different estimates of treatment difference, with varying precision, using different numbers of patients. In some cases the decision about statistical significance differed. The results suggested that where possible extra effort should be made to retrieve missing responses. In the presence of data missing at random, simple imputation was inappropriate with multiple imputation or a linear mixed effects model more useful.
Conclusion: Different trial conclusions were obtained for a variety of analysis approaches for the same outcome. Collecting as much data as possible is of paramount importance. Careful consideration should be taken when deciding on the most appropriate strategy for analysis when missing data are involved and this strategy should be pre-specified in the trial protocol. Making inappropriate decisions could result in inappropriate conclusions potentially leading to the adoption of a clinical intervention in error.
Copyright © 2012 Elsevier Inc. All rights reserved.