Journal of Clinical Epidemiology
ISSN: 0895-4356
Datasets associated with articles published in Journal of Clinical Epidemiology
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24 results
- Data for: Redundant meta-analyses are common in genetic epidemiologyData for: Redundant meta-analyses are common in genetic epidemiology
- Data for: Appraisal of 91 Practice Guidelines in the Philippines Using AGREE II Results of the appraisal of 91 CPGs in the Philippines
- Data for: Adoption of the GRADE Approach by Guideline Developers in the United States: An Assessment of Published Guidance Documents SPSS data set on implementation of GRADE bu US guideline developers
- Data for: Quality Control for Crowdsourcing Citation Screening: The Importance of Reviewer Characteristics, Assessment Number and Qualification Set Size.Quality Control for Crowdsourcing Citation Screening: The Importance of Reviewer Characteristics, Assessment Number and Qualification Set Size
- Data for: Asking authors of RCTs to confirm accuracy and supplement missing data for the purposes of meta-analysis yields a very low task completion rate: Another call for data sharing This is a SPSS file with the data we collected from the authors of 116 RCTs. There is detailed documentation in the 'Variables View' sheet
- Data for: Latency to enrol, attrition and intervention effect estimation: Meta-epidemiological study of four randomised behaviour change trials Data from four (published) web-based intervention trials used here to test methodological hypotheses
- Data for: Latency to enrol, attrition and intervention effect estimation: Meta-epidemiological study of four randomised behaviour change trials Data and do file
- Data for: Using the negative control design with a prenatal exposure subject to assortative matingThe following files simulate the data and run all models. Each do file takes arguments "nobs" and "effsize" to define the number of observations and effectsize - neg_contout_datsim (continuous outcome with no paternal effect) - neg_contout_datsim2 (continuous outcome with paternal effect) - neg_contr_datsim.do (binary outcome) Each of these uses "progs.do" to run the bootstrapping of the confidence interval of the difference in maternal and paternal effect estimates The separate datasets for each sample size and effect size are compiled into a single dataset using "compile_results.do". Statistics across simulations are calculated using the following files: - sim_cont_ready.do (continuous outcome with no paternal effect) - sim_cont_ready2.do (continuous outcome with paternal effect) - sim_bin_ready.do (binary outcome) The final datasets are included as the following .csv files: - neg_cont_simout (bias and other statistics for continuous outcome) - neg_cont_difs (mean and bootstrapped 95% CI of difference in estimates for continuous outcome) - neg_cont_simout (bias and other statistics for continuous outcome with paternal effect) - neg_cont_difs (mean and bootstrapped 95% CI of difference in estimates for continuous outcome with paternal effect) - neg_bin_simout (mean and bootstrapped 95% CI of difference in estimates for binary outcome) - neg_bin_difs (mean and bootstrapped 95% CI of difference in estimates for binary outcome)
- Data for: Publication bias among prognostic accuracy studies of middle cerebral artery Doppler ultrasound1. Complete research dataset 2. List with excluded conference abstracts 3. List with excluded full text articles
- Final dataset and code for the paper: "Overall bias and sample sizes were unchanged in ICU trials over time: a meta-epidemiological study"The file final_data_27_04_2019.csv contains the final dataset used for all analyses in the paper "Overall bias and sample sizes were unchanged in ICU trials over time: a meta-epidemiological study" by Carl Thomas Anthon, Anders Granholm, Anders Perner, Jon Henrik Laake and Morten Hylander Møller. The file is provided as a comma-seperated values (csv) file. A data dictionary is provided in the electronic supplementary material (ESM) for the said publication. The file final_code.nb.html contains the final, annotated analysis code for the paper "Overall bias and sample sizes were unchanged in ICU trials over time: a meta-epidemiological study" by Carl Thomas Anthon, Anders Granholm, Anders Perner, Jon Henrik Laake and Morten Hylander Møller. The code is written in R and is provided as a Rmarkdown-notebook HTML-file. This file can be opened in a web browser and will display the annotated analysis code and the results, or in RStudio (www.rstudio.com), where it can be run and edited (https://bookdown.org/yihui/rmarkdown/notebook.html).
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