Reporting and handling of incomplete outcome data in implant dentistry: A survey of randomized clinical trials.
bias
methodological study
methods
randomized controlled trial
systematic reviews
Journal
Journal of clinical periodontology
ISSN: 1600-051X
Titre abrégé: J Clin Periodontol
Pays: United States
ID NLM: 0425123
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
received:
04
07
2019
revised:
06
11
2019
accepted:
15
11
2019
pubmed:
21
11
2019
medline:
6
10
2020
entrez:
21
11
2019
Statut:
ppublish
Résumé
To assess the reporting and handling of incomplete outcome data in randomized clinical trials (RCTs) published in implant dentistry. We included RCTs on interventions related to the treatment with dental implants and presented any form of missing data. PubMed, SCOPUS and Cochrane databases were searched for studies published between May 2015 and May 2018. Reporting and handling of missing data at the study level were evaluated using a series of relevant questions. Descriptive data were reported, and univariable analyses were performed to evaluate the association of study variables with quality of reporting and data handling. One-hundred and thirty-seven RCT reports were included from the 7,116 initially retrieved publications. The reporting of incomplete outcome data varied greatly among the trials and for the different questions. The range of adequately reported questions was between 3.64% (question: comparison of baseline characteristics of all randomised participants) and 100% (question: explicit reporting of missing data). The complete case analysis was the most used (45.3%) approach for incomplete outcome data handling. Randomized studies in implant dentistry have room for improvement in both the reporting and the handling of incomplete outcome data.
Substances chimiques
Dental Implants
0
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
257-266Informations de copyright
© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Références
Akl, E. A., Briel, M., You, J. J., Lamontagne, F., Gangji, A., Cukierman-Yaffe, T., … Guyatt, G. H. (2009). LOST to follow-up Information in Trials (LOST-IT): A protocol on the potential impact. Trials, 10, 40. https://doi.org/10.1186/1745-6215-10-40
Akl, E. A., Briel, M., You, J. J., Sun, X., Johnston, B. C., Busse, J. W., …, Guyatt, G. H. (2012). Potential impact on estimated treatment effects of information lost to follow-up in randomised controlled trials (LOST-IT): systematic review. BMJ., 18(344), e2809. https://doi.org/10.1136/bmj.e2809
Bankhead, C., Aronson, J. K., & Nunan, D. (2017). Attrition bias. In: Catalogue Of Bias 2017. https://catalogofbias.org/biases/attrition-bias/
Bell, M. L., Kenward, M. G., Fairclough, D. L., & Horton, N. J. (2013). Differential dropout and bias in randomised controlled trials: When it matters and when it may not. BMJ, 346, e8668. https://doi.org/10.1136/bmj.e866
Carpenter, J. R., & Kenward, M. G. (2013). Multiple imputation and its application. Chichester, UK: John Wiley & Sons Ltd.
Donati, M., Ekestubbe, A., Lindhe, J., & Wennström, J. L. (2018). Marginal bone loss at implants with different surface characteristics - A 20-year follow-up of a randomized controlled clinical trial. Clinical Oral Implants Research, 29, 480-487. https://doi.org/10.1111/clr.13145
Fiero, M. H., Huang, S., Oren, E., & Bell, M. L. (2016). Statistical analysis and handling of missing data in cluster randomized trials: A systematic review. Trials, 17, 72. https://doi.org/10.1186/s13063-016-1201-z
Fitzmaurice, G. M., Molenberghs, G., & Lipsitz, S. R. (1995). Regression models for longitudinal binary responses with informative drop-outs. Journal of the Royal Statistical Society, 57(4), 691-704. https://doi.org/10.1111/j.2517-6161.1995.tb02056.x
Fleming, T. R. (2011). Addressing missing data in clinical trials. Annals of Internal Medicine, 154, 113-117. https://doi.org/10.1059/0003-4819-154-2-201101180-00010
Hewitt, C. E., Kumaravel, B., Dumville, J. C., & Torgerson, D. J. (2010). Trial attrition study group. Assessing the impact of attrition in randomized controlled trials. Journal of Clinical Epidemiology, 63, 1264-1270. https://doi.org/10.1016/j.jclinepi.2010.01.010
Hollis, S., & Campbell, F. (1999). What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ, 319, 670-674. https://doi.org/10.1136/bmj.319.7211.670
Lewin, A., Brondeel, R., Benmarhnia, T., Thomas, F., & Chaix, B. (2018). Attrition bias related to missing outcome data: A longitudinal simulation study. Epidemiology, 29, 87-95. https://doi.org/10.1097/EDE.0000000000000755
Little, R. J. A., D'Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., … Stern, H. (2012). The prevention and treatment of missing data in clinical trials. The New England Journal of Medicine, 367, 1355-1360. https://doi.org/10.1056/NEJMsr1203730
Little, R. J. A., D'Agostino, R., Dickersin, K., Emerson, S. S., Farrar, J. T., Frangakis, C., …Stern, H.; National Research Council. (2010). The prevention and treatment of missing data in clinical trials. Panel on Handling Missing Data in Clinical Trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. The National Academies Press.
Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. Hoboken, NJ: John Wiley & Sons.
Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Hoboken, NJ: Wiley.
May, G. S., Chir, B., DeMets, D. L., Friedman, L. M., Furberg, C., & Passamani, E. (1981). The randomized clinical trial: Bias in analysis. Circulation, 64, 669-673. https://doi.org/10.1161/01.CIR.64.4.669
Molenberghs, G., & Kenward, M. G. (2007). Missing data in clinical studies. Chichester, UK: John Wiley & Sons Ltd.
Powney, M., Williamson, P., Kirkham, J., & Kolamunnage-Dona, R. (2014). A review of the handling of missing longitudinal outcome data in clinical trials. Trials, 15, 237. https://doi.org/10.1186/1745-6215-15-237
Raboud, J. M., Montaner, J. S. G., Thorne, A., Singer, J., & Schechter, M. T. (1996). Impact of missing data due to dropouts on estimates of the treatment effect in a randomized trial of antiretroviral therapy for HIV-infected individuals. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology, 12, 46-55. https://doi.org/10.1097/00042560-199605010-00007
Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90, 106-121. https://doi.org/10.1080/01621459.1995.10476493
Robinson, K. A., Dennison, C. R., Wayman, D. M., Pronovost, P. J., & Needham, D. M. (2007). Systematic review identifies number of strategies important for retaining study participants. Journal of Clinical Epidemiology, 60, 757-765. https://doi.org/10.1016/j.jclinepi.2006.11.023
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581-592. https://doi.org/10.1093/biomet/63.3.581
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Hoboken, NJ: John Wiley & Sons.
Seaman, S. R., & White, I. R. (2013). Review of inverse probability weighting for dealing with missing data. Statistical Methods in Medical Research, 22, 278-295. https://doi.org/10.1177%2F0962280210395740
Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., … Henry, D. A. (2017). AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 21(358), j4008. https://doi.org/10.1136/bmj.j4008
Spineli, L. M., Fleming, P. S., & Pandis, N. (2015). Addressing missing participant outcome data in dental clinical trials. Journal of Dentistry, 43, 605-618. https://doi.org/10.1016/j.jdent.2015.03.007
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., … Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ, 338, b2393. https://doi.org/10.1136/bmj.b2393
Wolters, P. L., Martin, S., Merker, V. L., Tonsgard, J. H., Solomon, S. E., Baldwin, A., … Franklin, B. (2016). Patient-reported outcomes of pain and physical functioning in neurofibromatosis clinical trials. Neurology, 87, S4-S12. https://doi.org/10.1212/WNL.0000000000002927
Wood, A. M., White, I. R., & Thompson, S. G. (2004). Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clinical Trials, 1, 368-376. https://doi.org/10.1191%2F1740774504cn032oa
Yao, Y., Suo, T., Andersson, R., Cao, Y., Wang, C., Lu, J., & Chui, E. (2017). Dietary fibre for the prevention of recurrent colorectal adenomas and carcinomas. Cochrane Database of Systematic Reviews, 8(1), CD003430. https://doi.org/10.1002/14651858.CD003430.pub2