scholarly journals Letter in response to: Cooper et al. Established search filters may miss studies when identifying randomized controlled trials [J Clin Epidemiol. 2019 Apr 13;112:12-19. doi: 10.1016/j.jclinepi.2019.04.002]. Language for trial phase necessary when searching for RCT

2020 ◽  
Vol 117 ◽  
pp. 153-154
Author(s):  
Juliette C. Thompson ◽  
David A. Scott
Keyword(s):  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Search Filters ◽  
Trial Phase

scholarly journals Which are the most sensitive search filters to identify randomized controlled trials in MEDLINE?

2020 ◽  
Vol 108 (4) ◽  
Author(s):  
Julie Glanville ◽  
Eleanor Kotas ◽  
Robin Featherstone ◽  
Gordon Dooley
Keyword(s):  
Randomized Controlled Trials ◽  
Gold Standard ◽  
High Sensitivity ◽  
Controlled Trials ◽  
Relative Precision ◽  
Randomized Controlled ◽  
Ovid Medline ◽  
Search Filters ◽  
Standard Set ◽  
Central Database

Objective: The Cochrane Handbook of Systematic Reviews contains search filters to find randomized controlled trials (RCTs) in Ovid MEDLINE: one maximizing sensitivity and another balancing sensitivity and precision. These filters were originally published in 1994 and were adapted and updated in 2008. To determine the performance of these filters, the authors tested them and thirty-six other MEDLINE filters against a large new gold standard set of relevant records.Methods: We identified a gold standard set of RCT reports published in 2016 from the Cochrane CENTRAL database of controlled clinical trials. We retrieved the records in Ovid MEDLINE and combined these with each RCT filter. We calculated their sensitivity, relative precision, and f-scores.Results: The gold standard comprised 27,617 records. MEDLINE searches were run on July 16, 2019. The most sensitive RCT filter was Duggan et al. (sensitivity=0.99). The Cochrane sensitivity-maximizing RCT filter had a sensitivity of 0.96 but was more precise than Duggan et al. (0.14 compared to 0.04 for Duggan). The most precise RCT filters had 0.97 relative precision and 0.83 sensitivity.Conclusions: The Cochrane Ovid MEDLINE sensitivity-maximizing RCT filter can continue to be used by Cochrane reviewers and to populate CENTRAL, as it has very high sensitivity and a slightly better precision relative to more sensitive filters. The results of this study, which used a very large gold standard to compare the performance of all known RCT filters, allows searchers to make better informed decisions about which filters to use for their work.

scholarly journals VP195 Using The ISSG Search Filter Resource In Health Technology Assessment

2017 ◽  
Vol 33 (S1) ◽  
pp. 240-240
Author(s):  
Kath Wright ◽  
Julie Glanville ◽  
Carol Lefebvre
Keyword(s):  
Randomized Controlled Trials ◽  
Systematic Reviews ◽  
Health Technology ◽  
Controlled Trials ◽  
Health State ◽  
Search Filter ◽  
Utility Values ◽  
Randomized Controlled ◽  
Search Filters ◽  
Health State Utility Values

INTRODUCTION:Information specialists and others searching for Health Technology Assessments (HTAs) can use the ISSG Search Filter resource (SFR) to identify filters to incorporate into search strategies. This can save time and effort when designing searches and create more efficient searches that retrieve fewer and possibly more relevant database records (link available here: https://sites.google.com/a/york.ac.uk/issg-search-filters-resource/home).What are search filters? Search filters are collections of search terms designed to retrieve selections of records from bibliographic databases. Some filters are designed to retrieve records of specific study designs such as randomized controlled trials (RCTs) or systematic reviews; others aim to retrieve records relating to other features or topics such as the age or gender of study participants.Search filters may be designed to be sensitive, precise or balanced between sensitivity and precision.METHODS:When would you use a search filter in HTA? Search filters can be added to search strategies to limit to specific study types, for example, RCTs, mixed methods studies, systematic reviews. They can also be used when searching for other aspects of HTA such as patient views or specific age groups.The ISSG SFR includes sections listing search filters to help identify adverse effects, aetiology, economic evaluations, health state utility values, public views, and quality of life.RESULTS:How are filters used? A search filter is often used in combination with a topic search to restrict the search results to a specific type of record, for example, records reporting health state utility values or records of randomized controlled trials.CONCLUSIONS:Further guidance on the use of search filters can be found in the SuRe Info Search Filters chapter.

Reporting of Results by Sex in Randomized Controlled Trials of Acute Stroke Therapies (2010–2020)

Stroke ◽  
2021 ◽  
Author(s):  
Julia Pudar ◽  
Brent Strong ◽  
Virginia J. Howard ◽  
Mathew J. Reeves
Keyword(s):  
New England ◽  
Randomized Controlled Trials ◽  
Acute Stroke ◽  
Temporal Trends ◽  
Geographic Region ◽  
Controlled Trials ◽  
Phase 2 ◽  
Exact Test ◽  
Randomized Controlled ◽  
Trial Phase

Background and Purpose: When reporting primary results from randomized controlled trials, recommendations include reporting results by sex. We reviewed the reporting of results by sex in contemporary acute stroke randomized controlled trials. Methods: We searched MEDLINE for articles reporting the primary results of phase 2 or 3 stroke randomized controlled trials published between 2010 and June 2020 in one of nine major clinical journals. Eligible trials were restricted to those with a therapeutic intervention initiated within one month of stroke onset. Of primary interest was the reporting of results by sex for the primary outcome. We performed bivariate analyses using Fisher exact tests to identify study-level factors associated with reporting by sex and investigated temporal trends using an exact test for trend. Results: Of the 115 studies identified, primary results were reported by sex in 37% (n=42). Reporting varied significantly by journal, with the New England Journal of Medicine (61%) and Lancet journals (40%) having the highest rates ( P =0.03). Reporting also differed significantly by geographic region (21% Europe versus 48% Americas, P =0.03), trial phase (13% phase 2 versus 40% phase 3, P =0.05), and sample size (24% <250 participants versus 61% >750 participants, P <0.01). Although not statistically significant ( P =0.11), there was a temporal trend in favor of greater reporting among later publications (25% 2010–2012 versus 48% 2019–2020). Conclusions: Although reporting of primary trial results by sex improved from 2010 to 2020, the prevalence of reporting in major journals is still low. Further efforts are required to encourage journals and authors to comply with current reporting recommendations.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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