UMass Chan AffiliationsMeyers Primary Care Institute
Department of Quantitative Health Sciences
Department of Medicine
Document TypeJournal Article
Clinical Laboratory Techniques
Clinical Trials as Topic
Comparative Effectiveness Research
Epidemiologic Research Design
Interviews as Topic
data collection approaches
Health Information Technology
Health Services Research
MetadataShow full item record
AbstractWe provide an overview of the different data-collection approaches that are commonly used in carrying out clinical, public health, and translational research. We discuss several of the factors that researchers need to consider in using data collected in questionnaire surveys, from proxy informants, through the review of medical records, and in the collection of biologic samples. We hope that the points raised in this overview will lead to the collection of rich and high-quality data in observational studies and randomized controlled trials.
Saczynski JS, McManus DD, Goldberg RJ. Commonly used data-collection approaches in clinical research. Am J Med. 2013 Nov;126(11):946-50. doi:10.1016/j.amjmed.2013.04.016. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/30117
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Automatic extraction of quantitative data from ClinicalTrials.gov to conduct meta-analysesPradhan, Richeek; Hoaglin, David C.; Cornell, Matthew; Liu, Weisong; Wang, Victoria; Yu, Hong (2019-01-01)OBJECTIVE: Systematic reviews and meta-analyses are labor-intensive and time-consuming. Automated extraction of quantitative data from primary studies can accelerate this process. ClinicalTrials.gov, launched in 2000, is the world's largest trial repository of results data from clinical trials; it has been used as a source instead of journal articles. We have developed a web application called EXACT that allows users without advanced programming skills to automatically extract data from ClinicalTrials.gov in analysis-ready format. We have also used the automatically extracted data to examine the reproducibility of meta-analyses in three published systematic reviews. STUDY DESIGN: We developed a Python-based software application (EXACT, Extracting Accurate efficacy and safety information from ClinicalTrials.gov) that automatically extracts data required for meta-analysis from the ClinicalTrials.gov database in a spreadsheet format. We confirmed the accuracy of the extracted data and then used those data to repeat meta-analyses in three published systematic reviews. To ensure that we used the same statistical methods and outcomes as the published systematic reviews, we repeated the statistics using data manually extracted from the relevant journal articles. For the outcomes whose results we were able to reproduce using those journal article data, we examined the usability of ClinicalTrials.gov data. RESULTS: EXACT extracted data at ClincalTrials.gov with 100% accuracy, and it required 60% less time than the usual practice of manually extracting data from journal articles. We found that 87% of the data elements extracted using EXACT matched those extracted manually from the journal articles. We were able to reproduce 24 of 28 outcomes using the journal article data. Of these 24 outcomes, we were able to reproduce 83.3% of the published estimates using data at ClinicalTrials.gov. CONCLUSION: EXACT (http://bio-nlp.org/EXACT) automatically and accurately extracted data elements from ClinicalTrials.gov and thus reduced time in data extraction. The ClinicalTrials.gov data reproduced most meta-analysis results in our study, but this conclusion needs further validation.
Prognostic factors for changes in the timed 4-stair climb in patients with Duchenne muscular dystrophy, and implications for measuring drug efficacy: A multi-institutional collaborationGoemans, Nathalie; Wong, Brenda L; Van den Hauwe, Marleen; Signorovitch, James; Sajeev, Gautam; Cox, David; Landry, John; Jenkins, Madeline; Dieye, Ibrahima; Yao, Zhiwen; et al. (2020-06-18)The timed 4-stair climb (4SC) assessment has been used to measure function in Duchenne muscular dystrophy (DMD) practice and research. We sought to identify prognostic factors for changes in 4SC, assess their consistency across data sources, and the extent to which prognostic scores could be useful in DMD clinical trial design and analysis. Data from patients with DMD in the placebo arm of a phase 3 trial (Tadalafil DMD trial) and two real-world sources (Universitaire Ziekenhuizen, Leuven, Belgium [Leuven] and Cincinnati Children's Hospital Medical Center [CCHMC]) were analyzed. One-year changes in 4SC completion time and velocity (stairs/second) were analyzed. Prognostic models included age, height, weight, steroid use, and multiple timed function tests and were developed using multivariable regression, separately in each data source. Simulations were used to quantify impacts on trial sample size requirements. Data on 1-year changes in 4SC were available from the Tadalafil DMD trial (n = 92) Leuven (n = 67), and CCHMC (n = 212). Models incorporating multiple timed function tests, height, and weight significantly improved prognostic accuracy for 1-year change in 4SC (R2: 29%-36% for 4SC velocity, and 29%-34% for 4SC time) compared to models including only age, baseline 4SC and steroid duration (R2:8%-17% for 4SC velocity and 2%-13% for 4SC time). Measures of walking and rising ability contributed important prognostic information for changes in 4SC. In a randomized trial with equal allocation to treatment and placebo, adjustment for such a prognostic score would enable detection (at 80% power) of a treatment effect of 0.25 stairs/second with 100-120 patients, compared to 170-190 patients without prognostic score adjustment. Combining measures of ambulatory function doubled prognostic accuracy for 1-year changes in 4SC completion time and velocity. Randomized clinical trials incorporating a validated prognostic score could reduce sample size requirements by approximately 40%. Knowledge of important prognostic factors can also inform adjusted comparisons to external controls.