A randomized trial provided new evidence on the accuracy and efficiency of traditional vs. electronically annotated abstraction approaches in systematic reviews

Journal of Clinical Epidemiology

Volumn 115, Issue , 2019, Pages 77-89

  Li, Tianjing ^a   Saldanha, Ian J ^b   Jap, Jens ^b   Smith, Bryant T ^b   Canner, Joseph ^c   Hutfless, Susan M ^c   Branch, Vernal ^d   Carini, Simona ^e   Chan, Wiley ^f   de Bruijn, Berry ^g   Wallace, Byron C ^h   Walsh, Sandra A ⁱ   Whamond, Elizabeth J ^j   Murad, M Hassan ^k   Sim, Ida ^e   Berlin, Jesse A ^l   Lau, Joseph ^b   Dickersin, Kay ^a   Schmid, Christopher H ^b  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^b BROWN UNIVERSITY   (United States)

^c JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d University of California San Francisco ^*  (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f KAISER PERMANENTE   (United States)

^g NATIONAL RESEARCH COUNCIL CANADA   (Canada)

^h Northeastern University   (United States)

ⁱ High School Junior of Davis Senior High School   (United States)

^j Internal Medicine   (Canada)

^k MAYO CLINIC   (United States)

^l JOHNSON AND JOHNSON   (United States)

more..

Author keywords

Accuracy; Data abstraction; Efficiency; Randomized cross over trial; Software application; Systematic review

Indexed keywords

ARTICLE; ERROR; HUMAN; RANDOMIZED CONTROLLED TRIAL (TOPIC); SOFTWARE; SYSTEMATIC REVIEW; COMPARATIVE STUDY; CONTROLLED STUDY; CROSSOVER PROCEDURE; DOCUMENTATION; INFORMATION PROCESSING; ODDS RATIO; PROCEDURES; RANDOMIZATION; RANDOMIZED CONTROLLED TRIAL; YOUNG ADULT;

ABSTRACTING AND INDEXING; CROSS-OVER STUDIES; DATA COLLECTION; HUMANS; ODDS RATIO; RANDOM ALLOCATION; SOFTWARE; SYSTEMATIC REVIEWS AS TOPIC; YOUNG ADULT;

EID: 85070113987     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2019.07.005     Document Type: Article

References (25)

1. Committee on Standards for Systematic Reviews of Comparative Effectiveness Research, Board on Health Care Services. Eden, J., Levit, L., Berg, A., Morton, S., (eds.) Finding what works in health care: standards for systematic reviews, 2011, National Academies Press, Washington, DC.
2. Chandler, J., Churchill, R., Higgins, J., Lasserson, T., Tovey, D., Methodological expectations of Cochrane intervention reviews. Version 1.05, January 2018. Available at http://community.cochrane.org/mecir-manual. (Accessed 18 March 2019)
3. Buscemi, N., Hartling, L., Vandermeer, B., Tjosvold, L., Klassen, T.P., Single data extraction generated more errors than double data extraction in systematic reviews. J Clin Epidemiol 59 (2006), 697–703.
4. Gøtzsche, P.C., Hróbjartsson, A., Maric, K., Tendal, B., Data extraction errors in meta-analyses that use standardized mean differences. JAMA 298 (2007), 430–437.
5. Jones, A.P., Remmington, T., Williamson, P.R., Ashby, D., Smyth, R.L., High prevalence but low impact of data extraction and reporting errors were found in Cochrane systematic reviews. J Clin Epidemiol 58 (2005), 741–742.
6. Patient-Centered Outcomes Research Institute Methodology Committee. Draft methodology report: our questions, our decisions: standards for Patient-Centered Outcomes Research. July 23, 2012 version. Available at http://pcori.org/assets/MethodologyReport-Comment.pdf. (Accessed 18 March 2019)
7. Center for Reviews and Dissemination. Systematic reviews: CRD's guidance for undertaking reviews in health care. York, UK: York Publishing Services, Ltd. Available at https://www.york.ac.uk/media/crd/Systematic_Reviews.pdf. (Accessed 18 March 2019)
8. Methods guide for effectiveness and comparative effectiveness reviews. AHRQ Publication No. 10(14)-EHC063-EF. Rockville, MD. Available at https://effectivehealthcare.ahrq.gov/topics/cer-methods-guide/overview. (Accessed 18 March 2019)
9. Ip, S., Hadar, N., Keefe, S., Parkin, C., Iovin, R., Balk, E.M., et al. A Web-based archive of systematic review data. Syst Rev, 1, 2012, 15.
10. Li, T., Vedula, S.S., Hadar, N., Parkin, C., Lau, J., Dickersin, K., Innovations in data collection, management, and archiving for systematic reviews. Ann Intern Med 162 (2015), 287–294.
11. Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia. Available at www.covidence.org. (Accessed 18 March 2019)
12. Jap, J., Saldanha, I.J., Smith, B.T., Lau, J., Schmid, C., Li, T., On Behalf of the Data Abstraction Assistant Investigators. Features and functioning of Data Abstraction Assistant, a software application for data abstraction during systematic reviews. Res Synth Methods 10 (2019), 2–14.
13. Saldanha, I.J., Schmid, C.H., Lau, J., Dickersin, K., Berlin, J.A., Jap, J., et al. Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial. Syst Rev, 5(1), 2016, 196.
14. Saldanha IJ, Wen J, Schmid CH, Li T. Data Abstraction Assistant (DAA): What characteristics classify “experience” with data abstraction? 2016 Cochrane Colloquium, October 23-27, Seoul, South Korea.
15. Choi, M., Hector, M., Effectiveness of intervention programs in preventing falls: systematic review of recent 10 years and meta-analysis. J Am Med Dir Assoc 13:2 (2012), 188.e13–188.e21.
16. Navarese, E.P., Kolodziejczak, M., Schulze, V., Gurbel, P.A., Tantry, U., Lin, Y., et al. Effects of proprotein convertase subtilisin/kexin type 9 antibodies in adults with hypercholesterolemia: a systematic review and meta-analysis. Ann Intern Med 163 (2015), 40–51.
17. Fong, D.Y., Ho, J.W., Hui, B.P., Lee, A.M., Macfarlane, D.J., Leung, S.S., et al. Physical activity for cancer survivors: meta-analysis of randomised controlled trials. BMJ, 344, 2012, e70.
18. Appleton, K.M., Sallis, H.M., Perry, R., Ness, A.R., Churchill, R., Omega-3 fatty acids for depression in adults. Cochrane Database Syst Rev, 11, 2015, CD004692.
19. Harville, D.A., Maximum likelihood approaches to variance component estimation and to related problems. J Am Stat Assoc 72 (1977), 320–338.
20. Jennrich, R.I., Sampson, P.F., Newton-Raphson and related algorithms for maximum likelihood variance component estimation. Technometrics 18 (1976), 11–17.
21. Mathes, T., Klaßen, P., Pieper, D., Frequency of data extraction errors and methods to increase data extraction quality: a methodological review. BMC Med Res Methodol, 17, 2017, 152.
22. Dearing, J.W., Applying diffusion of innovation theory to intervention development. Res Soc Work Pract 19:5 (2009), 503–518.
23. Jonnalagadda, S.R., Goyal, P., Huffman, M.D., Automating data extraction in systematic reviews: a systematic review. Syst Rev, 4, 2015, 78.
24. Marshall, I.J., Kuiper, J., Wallace, B.C., RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. J Am Med Inform Assoc 23 (2016), 193–201.
25. Millard, L.A.C., Flach, P.A., Higgins, J.P.T., Machine learning to assist risk-of-bias assessments in systematic reviews. Int J Epidemiol 45 (2016), 266–277.