Skip to main content
Top
Published in: BMC Medicine 1/2019

Open Access 01-12-2019 | Diabetes | Correspondence

DIABRISK-SL trial: further consideration of age and impact of imputations

Authors: Efstathia Gkioni, Ketevan Glonti, Susanna Dodd, Carrol Gamble

Published in: BMC Medicine | Issue 1/2019

Login to get access

Abstract

Type 2 diabetes mellitus (T2DM) is a major cause of morbidity and mortality worldwide. Early interventions may help to delay or prevent onset of cardiometabolic endpoints of clinical importance to T2DM patients.
Wijesuriya et al. (BMC Med 15:146, 2017) published results of a randomised controlled trial in Sri Lanka testing the effect of two lifestyle modification programmes of varying intensity in participants aged 5–40 years with risk factors for T2DM. The intervention measured the impact of the two programmes on the primary composite endpoint consisting of various predictors of cardiometabolic disease. The authors concluded that the more intensive programme significantly reduced the incidence of predictors of cardiometabolic disease. Further, they delivered a large-scale intervention with restricted resources with widespread acceptance as demonstrated by the high uptake rate. However, we believe that further analysis is required to fully understand the potential for benefit, particularly in relation to age, retention and missing data.
Literature
1.
go back to reference Wijesuriya M, et al. A pragmatic lifestyle modification programme reduces the incidence of predictors of cardio-metabolic disease and dysglycaemia in a young healthy urban south Asian population: a randomised controlled trial. BMC Med. 2017;15:146.CrossRef Wijesuriya M, et al. A pragmatic lifestyle modification programme reduces the incidence of predictors of cardio-metabolic disease and dysglycaemia in a young healthy urban south Asian population: a randomised controlled trial. BMC Med. 2017;15:146.CrossRef
2.
go back to reference Groenwold RHH, Moons KGM, Vandenbroucke JP. Randomized trials with missing outcome data: how to analyze and what to report. CMAJ Can Med Assoc J. 2014;186(15):1153–7.CrossRef Groenwold RHH, Moons KGM, Vandenbroucke JP. Randomized trials with missing outcome data: how to analyze and what to report. CMAJ Can Med Assoc J. 2014;186(15):1153–7.CrossRef
3.
go back to reference Waters E, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev. 2011;12:CD001871. Waters E, et al. Interventions for preventing obesity in children. Cochrane Database Syst Rev. 2011;12:CD001871.
4.
go back to reference Klassen TP, et al. Children are not just small adults: the urgent need for high-quality trial evidence in children. PLoS Med. 2008;5(8):e172.CrossRef Klassen TP, et al. Children are not just small adults: the urgent need for high-quality trial evidence in children. PLoS Med. 2008;5(8):e172.CrossRef
5.
go back to reference Wijesuriya M, et al. High prevalence of cardio-metabolic risk factors in a young urban Sri-Lankan population. PLoS One. 2012;7(2):e31309.CrossRef Wijesuriya M, et al. High prevalence of cardio-metabolic risk factors in a young urban Sri-Lankan population. PLoS One. 2012;7(2):e31309.CrossRef
6.
go back to reference Madden L, et al. Questioning assent: how are children's views included as families make decisions about clinical trials? Child Care Health Dev. 2016;42(6):900–8.CrossRef Madden L, et al. Questioning assent: how are children's views included as families make decisions about clinical trials? Child Care Health Dev. 2016;42(6):900–8.CrossRef
7.
go back to reference Sahoo K, et al. Childhood obesity: causes and consequences. J Family Med Prim Care. 2015;4(2):187–92.CrossRef Sahoo K, et al. Childhood obesity: causes and consequences. J Family Med Prim Care. 2015;4(2):187–92.CrossRef
8.
go back to reference Wijesuriya M, et al. DIABRISK-SL prevention of cardio-metabolic disease with life style modification in young urban Sri Lankan's – study protocol for a randomized controlled trial. Trials. 2011;12:209.CrossRef Wijesuriya M, et al. DIABRISK-SL prevention of cardio-metabolic disease with life style modification in young urban Sri Lankan's – study protocol for a randomized controlled trial. Trials. 2011;12:209.CrossRef
9.
go back to reference Dodd S, White IR, Williamson P. A framework for the design, conduct and interpretation of randomised controlled trials in the presence of treatment changes. Trials. 2017;18:498.CrossRef Dodd S, White IR, Williamson P. A framework for the design, conduct and interpretation of randomised controlled trials in the presence of treatment changes. Trials. 2017;18:498.CrossRef
10.
go back to reference Kenward MG, Molenberghs G. Last observation carried forward: a crystal ball? J Biopharm Stat. 2009;19(5):872–88.CrossRef Kenward MG, Molenberghs G. Last observation carried forward: a crystal ball? J Biopharm Stat. 2009;19(5):872–88.CrossRef
11.
go back to reference Molnar FJ, Hutton B, Fergusson D. Does analysis using “last observation carried forward” introduce bias in dementia research? CMAJ. 2008;179(8):751–3.CrossRef Molnar FJ, Hutton B, Fergusson D. Does analysis using “last observation carried forward” introduce bias in dementia research? CMAJ. 2008;179(8):751–3.CrossRef
12.
go back to reference Little RJ, et al. The prevention and treatment of missing data in clinical trials. New Engl J Med. 2012;367(14):1355–60.CrossRef Little RJ, et al. The prevention and treatment of missing data in clinical trials. New Engl J Med. 2012;367(14):1355–60.CrossRef
13.
go back to reference Jørgensen AW, et al. Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial. PLoS One. 2014;9(11):e111964.CrossRef Jørgensen AW, et al. Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial. PLoS One. 2014;9(11):e111964.CrossRef
14.
go back to reference Molenberghs G, et al. Analyzing incomplete longitudinal clinical trial data. Biostatistics. 2004;5(3):445–64.CrossRef Molenberghs G, et al. Analyzing incomplete longitudinal clinical trial data. Biostatistics. 2004;5(3):445–64.CrossRef
15.
go back to reference Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3–15.CrossRef Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3–15.CrossRef
16.
go back to reference Nich C, Carroll KM. Intention-to-treat meets missing data: implications of alternate strategies for analyzing clinical trials data. Drug Alcohol Depend. 2002;68(2):121–30.CrossRef Nich C, Carroll KM. Intention-to-treat meets missing data: implications of alternate strategies for analyzing clinical trials data. Drug Alcohol Depend. 2002;68(2):121–30.CrossRef
17.
go back to reference Hayati Rezvan P, Lee KJ, Simpson JA. The rise of multiple imputation: a review of the reporting and implementation of the method in medical research. BMC Med Res Methodol. 2015;15:30.CrossRef Hayati Rezvan P, Lee KJ, Simpson JA. The rise of multiple imputation: a review of the reporting and implementation of the method in medical research. BMC Med Res Methodol. 2015;15:30.CrossRef
18.
go back to reference Sterne JAC, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.CrossRef Sterne JAC, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.CrossRef
Metadata
Title
DIABRISK-SL trial: further consideration of age and impact of imputations
Authors
Efstathia Gkioni
Ketevan Glonti
Susanna Dodd
Carrol Gamble
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medicine / Issue 1/2019
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/s12916-019-1361-2

Other articles of this Issue 1/2019

BMC Medicine 1/2019 Go to the issue