Gözlemsel sağlık araştırmaları için tamamlayıcı bir yan tutma riski değerlendirme aracı (BiRDA kontrol listesi): Metodolojik bir çalışma

Abstract

Sağlık araştırmalarında yan tutma önemli olup, bunu tarafsız bir şekilde ölçmek kolay değildir. Bu araştırmanın amacı; gözlemsel araştırmalar için kullanımı kolay olan, kapsamlı, maliyet ve zaman etkin bir yan tutma kontrol aracı geliştirilmekti. Bu araştırma, Haziran 2018 – Haziran 2020 tarihleri arasında yürütülen metodolojik bir çalışmaydı. Araştırmanın ana basamakları; yan tutma sorgulanacak maddeler için literatür taramak, uzman görüşleri, Delphi panelleri, bias risk değerlendirme aracının ana hatlarının oluşturulmak ve içerik geliştirilmek, araştırmanın istatistiksel analizini yapmak ve sonucunu raporlamaktı. Literatür taraması, araştırmacılar tarafından önceden belirlenmiş anahtar kelimeler ile yapıldı. Uzman görüşlerini alabilmek için “Uzman Değerlendirme Formu” geliştirilip kullanıldı. Kapsam geçerliliğini değerlendirmek için Kapsam Geçerlilik Oranı (KGO) kullanılmış olsa da esas olarak uzman görüşüne dayalı olarak yapıldı. Üç Delphi paneli gerçekleştirildi. Geliştirilen aracın adının Bias Risk Değerlendirme Aracı (BiRDA) olmasına karar verildi. Kesitsel araştırmalar için BiRDA-Ke, vaka kontrol araştırmaları için BİRDA-VK, kohort araştırmaları için BiRDA-Ko olarak kullanılması kabul edildi. 67 uzmana toplam 71 adet uzman değerlendirme formu gönderildi ve bu formların 44’üne geri dönüş yapıldı. Delphi panellerinin sonucunda; 67 maddelik BiRDA-Ke, 69 maddelik BiRDA-VK ve 70 maddelik Ko araçları geliştirildi. BiRDA araçları, eğitimde, araştırmaya hazırlık aşamasında veya yayın süreçlerinde kullanılabilir. Fakat, bu tür araçların kullanımı genellikle pratik olmadığı için mobil / internet uygulaması olarak kullanımı veya ileri çalışmalarda yapay zekâ teknolojileri ile kullanımı kolaylaştıracaktır.

Keywords

• Bias
• Sağlık Hizmetleri Araştırması
• Bilimsel Suistimal
• Bias Risk Değerlendirme Aracı
• BİRDA

A COMPLEMENTARY BIAS RISK ASSESSMENT TOOL (BIRAT CHECKLIST) FOR OBSERVATIONAL STUDIES: A METHODOLOGICAL STUDY

Abstract

Assessment of bias in health studies is important and not easy to measure objectively. The aim of this study was to develop an easy-to-use, comprehensive, cost effective and time efficient bias control tool for observational studies. This was a methodological study conducted between June 2018 – June 2020. Literature review to extract items, expert opinions, Delphi panels, construction of the framework and the content of the tool, statistical analysis and reporting of the study were the main steps. Literature review was conducted with prespecified keywords by researchers. “Expert Assessment Form” was used to evaluate expert opinions. Although Content Validity Ratio (CVR) was used to check the content validity, it was mainly based on consensus of experts. Three Delphi panels were carried out. The name of the developed tool was decided to be Bias Risk Assessment Tool (BiRAT). It was considered to use the abbreviations BiRAT-CS for cross-sectional studies, BiRAT-CC for case-control studies and BiRAT-Co for cohort studies. Descriptive statistics A total of 71 expert assessment forms were sent to 67 experts and 44 of them were received. As a result of the assessments made after the Delphi panels; 67-item BiRAT-CS, 69-item BiRAT-CC and 70-item BiRAT-Co were developed. BiRAT tools may be used in training, preparing for a study, or publication process. However, bias assessment tools should be used with mobile / online applications or artificial intelligence technologies for easier use and further development since the use of them were generally impractical.

Keywords

• Bias
• health care research
• scientific misconduct
• bias risk assessment tool
• BiRAT

References

1. Gilmartin-Thomas JF, Liew D, Hopper I. Observational studies and their utility for practice. Aust Prescr. 2018 Jun; 41(3):82–5. DOI: 10.18773/austprescr.2018.017
2. Observational Research – Research Methods in Psychology. [Internet]. [Accessed: 24.01.2023]. Available from: https://opentext.wsu.edu/carriecuttler/chapter/observational-research/
3. Definition of observational study - NCI Dictionary of Cancer Terms - NCI. [Internet]. [Accessed: 24.01.2023]. Available from: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/observational-study
4. Cataldo R, Arancibia M, Stojanova J, Papuzinski C. General concepts in biostatistics and clinical epidemiology: Observational studies with cross-sectional and ecological designs. Medwave. 2019 Sep 30; 19(08): e7698–e7698. DOI: 10.5867/medwave.2019.08.7698
5. Porta MS, Greenland S, Hernán M, Silva I dos S, Last JM, International Epidemiological Association, editors. A dictionary of epidemiology. Six edition. Oxford: Oxford University Press; 2014. p. 343.
6. Catalogue of Bias - Catalog of Bias. [Internet]. [Accessed: 24.01.2023]. Available from: https://catalogofbias.org/
7. Gabriel R. What is the best evidence and how to find it | BMJ Best Practice. [Internet]. [Accessed: 24.01.2023]. Available from: https://bestpractice.bmj.com/info/toolkit/discuss-ebm/what-is-the-best-evidence-and-how-to-find-it/
8. The New ICMJE Recommendations | EQUATOR Network. [Internet]. [Accessed: 24.01.2023]. Available from: https://www.equator-network.org/2013/08/29/the-new-icmje-recommendations/

9. RoB 2: A revised Cochrane risk-of-bias tool for randomized trials. [Internet]. [Accessed: 17.03.2019]. Available from: https://methods.cochrane.org/bias/resources/rob-2-revised-cochrane-risk-bias-tool-randomized-trials
10. Dreyer NA, Bryant A, Velentgas P. The GRACE Checklist: A validated assessment tool for high quality observational studies of comparative effectiveness. JMCP. 2016 Oct; 22(10): 1107–13. DOI: 10.18553/jmcp.2016.22.10.1107
11. Quality Assessment Tool for Quantitative Studies. Effective Public Healthcare Panacea Project. [Internet]. [Accessed: 24.01.2023]. Available from: https://www.ephpp.ca/quality-assessment-tool-for-quantitative-studies/
12. PubMed. [Internet]. [Accessed: 07.02.2023]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/
13. Cochrane. [Internet]. [Accessed: 07.02.2023]. Available from: https://www.cochrane.org/
14. Embase. [İnternet]. [Erişim tarihi: 07.02.2023]. Available from: https://www.embase.com
15. Yesilyurt S, Capraz C. Olcek gelistirme calismalarinda kullanilan kapsam gecerliligi icin bir yol haritasi. Erzincan Universitesi Egitim Fakultesi Dergisi (in Turkish). 2018;20(1):251–64. DOI: 10.17556/erziefd.297741
16. Ayre C, Scally A. Critical values for Lawshe's content validity ratio: revisiting the original methods of calculation. Measurement and Evaluation in Counseling and Development. 2014;47(1):79–86. DOI: 10.1177/074817561351380
17. Lawshe CH. A quantitative approach to content validity. Personnel psychology. 1975;28(4):563–75. DOI: 10.1111/j.1744-6570.1975.tb01393.x
18. Critical Appraisal Skills Programme (CASP) Checklists. [İnternet]. [Erişim tarihi: 14 Ocak 2020]. Mevcut: https://casp-uk.net/casp-tools-checklists/
19. The ROBINS-E tool (Risk Of Bias In Non-randomized Studies - of Exposures). [Internet]. [Accessed: 14 Jan 2020]. Available from: https://www.bristol.ac.uk/population-health-sciences/centres/cresyda/barr/riskofbias/robins-e/
20. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52:377–84. DOI: 10.1136/jech.52.6.377
21. Wells GA, Shea B, O’Connell D, Peterson J, Welch V et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. [Internet]. [Accessed: 14 Jan 2020]. Available from: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
22. Deeks J, Dinnes J, D'Amico R, Sowden A J, Sakarovitch C et al. Evaluating non-randomised intervention studies. Health Technol Assess. 2003;7(27):iii-x, pp1-173. doi: 10.3310/hta7270
23. Bracken MB. Reporting observational studies. British Journal of Obstetrics and Gynaecology. 1989;96(April):383–8. DOI: 10.1111/j.1471-0528.1989.tb02410.x
24. Cowley DE. Prostheses For Primary Total Hip Replacement. International Journal of Technology Assessment in Health Care. 1995;11(4):770–8. DOI: 10.1017/s026646230000920x
25. Durant RH. Checklist for the Evaluation of Research Articles. Journal of Adolescent Health. 1994;15:4–8. DOI: 10.1016/1054-139x(94)90381-6
26. Fowkes FGR, Fulton PM. Critical appraisal of published research: introductory guidelines. BMJ. 1991;302(May):1136–40. DOI: 10.1136/bmj.302.6785.1136
27. Hadorn DC, Baker D, Hodges JS, Hicks N. Rating the quality of evidence for clinical practice guidelines. J Clin Epidemiol. 1996;49(7):749–54. doi: 10.1016/0895-4356(96)00019-4
28. Cook TD, Campbell DT. Quasi-experimentation: Design and analysis issues for field setting. Chicago: Rand McNally; 1979.
29. Vickers A. Critical appraisal: how to read a clinical research paper. Complementary Therapies in Medicine. 1995;3:158–66. DOI: 10.1016/S0965-2299(95)80057-3
30. Zaza S, Agu LKW de, Briss PA, Truman BI, Hopkins DP et al. Data collection instrument and the guide to community preventive services. Am J Prev Med. 2000;18(99). DOI: 10.1016/s0749-3797(99)00122-1
31. Vet LBMHCW De, Patrick CACPDL, Bouter JALM, Terwee CB. COSMIN Risk of Bias checklist for systematic reviews of patient-reported outcome measures. Quality of Life Research. 2018;27(5):1171–9. DOI: 10.1007/s11136-017-1765-4
32. Chow EPF, Muessig KE, Yuan L, Wang Y, Zhang X et al. Risk behaviours among female sex workers in China: A systematic review and data synthesis. PLoS One. 2015;1–14. DOI: 10.1371/journal.pone.0120595
33. Hayden JA, Windt DA Van Der, Cartwright JL, Cote P, Bombardier C. Assessing bias in studies of prognostic factors. Annals of Internal Medicine. 2013;158:280–6. DOI: 10.7326/0003-4819-158-4-201302190-00009
34. Armijo-olivo S, Stiles CR, Frcpc NAH, Biondo PD et al. Assessment of study quality for systematic reviews: a comparison of the Cochrane Collaboration risk of bias tool and the Effective Public Health Practice Project quality assessment tool: methodological research. J Eval Clin Pract. 2012;18:12–8. DOI: 10.1111/j.1365-2753.2010.01516.x
35. Yildiz F. Gözlemsel sağlık araştırmalarında yan tutma (bias) kontrolü aracı geliştirme: Metodolojik bir çalışma [Development of a bias control tool in observational health research: A methodological study]. Thesis of Medical Specialty, Aydin Adnan Menderes University, Aydin, Turkiye, 2020. Available from: https://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=fl0Kw4p1rmMDotyKRdYv1IbD3HD3xITI7SNBet5IqAZuk7dzBgbCU2wJs2hFn6KE (Erişim tarihi: 25 Ocak 2023).
36. Karaca A, Orsal O, Duru P. Sağlık personellerinin tele-sağlık uygulamalarını benimsemesinde kolaylaştırıcılar ve engeller [Facilitators and barriers to the adoption of telehealth applications among healthcare professionals]. J. Nursology [Internet]. 2022;25(3):168-176. DOI: 10.5152/JANHS.2022.957610.
37. Eminoglu A, Orsal O, Duru P. Hastaların tele-sağlık uygulamalarını benimsemesinde kolaylaştırıcılar ve engeller: sistematik derleme [Facilitators and barriers to the adoption of telehealth applications among patients: a systematic review]. In: Ozdogan Y, editor. Sağlık Bilimleri Alanında Araştırmalar II [Research in Health Sciences II]. Konya: Eğitim yayınevi; 2021. pp. 121-140. (In Turkish).