Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants

Sauzet O, Peacock JL (2017)
BMC Medical Research Methodology 17: 110.

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Background The analysis of perinatal outcomes often involves datasets with some multiple births. These are datasets mostly formed of independent observations and a limited number of clusters of size two (twins) and maybe of size three or more. This non-independence needs to be accounted for in the statistical analysis. Using simulated data based on a dataset of preterm infants we have previously investigated the performance of several approaches to the analysis of continuous outcomes in the presence of some clusters of size two. Mixed models have been developed for binomial outcomes but very little is known about their reliability when only a limited number of small clusters are present. Methods Using simulated data based on a dataset of preterm infants we investigated the performance of several approaches to the analysis of binomial outcomes in the presence of some clusters of size two. Logistic models, several methods of estimation for the logistic random intercept models and generalised estimating equations were compared. Results The presence of even a small percentage of twins means that a logistic regression model will underestimate all parameters but a logistic random intercept model fails to estimate the correlation between siblings if the percentage of twins is too small and will provide similar estimates to logistic regression. The method which seems to provide the best balance between estimation of the standard error and the parameter for any percentage of twins is the generalised estimating equations. Conclusions This study has shown that the number of covariates or the level two variance do not necessarily affect the performance of the various methods used to analyse datasets containing twins but when the percentage of small clusters is too small, mixed models cannot capture the dependence between siblings.
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BMC Medical Research Methodology
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17
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110
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Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
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Sauzet O, Peacock JL. Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants. BMC Medical Research Methodology. 2017;17: 110.
Sauzet, O., & Peacock, J. L. (2017). Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants. BMC Medical Research Methodology, 17, 110. doi:10.1186/s12874-017-0369-6
Sauzet, O., and Peacock, J. L. (2017). Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants. BMC Medical Research Methodology 17:110.
Sauzet, O., & Peacock, J.L., 2017. Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants. BMC Medical Research Methodology, 17: 110.
O. Sauzet and J.L. Peacock, “Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants”, BMC Medical Research Methodology, vol. 17, 2017, : 110.
Sauzet, O., Peacock, J.L.: Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants. BMC Medical Research Methodology. 17, : 110 (2017).
Sauzet, Odile, and Peacock, Janet L. “Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants”. BMC Medical Research Methodology 17 (2017): 110.
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Systematic review and simulation study of ignoring clustered data in surgical trials.
Dell-Kuster S, Droeser RA, Schäfer J, Gloy V, Ewald H, Schandelmaier S, Hemkens LG, Bucher HC, Young J, Rosenthal R., Br J Surg 105(3), 2018
PMID: 29405280

23 References

Daten bereitgestellt von Europe PubMed Central.

The statistical analysis of data from small groups.
Kenny DA, Mannetti L, Pierro A, Livi S, Kashy DA., J Pers Soc Psychol 83(1), 2002
PMID: 12088122
Analysis of a trial randomised in clusters.
Kerry SM, Bland JM., BMJ 316(7124), 1998
PMID: 9451271
Comparison of methods for analysing cluster randomized trials: an example involving a factorial design.
Peters TJ, Richards SH, Bankhead CR, Ades AE, Sterne JA., Int J Epidemiol 32(5), 2003
PMID: 14559762
High-frequency oscillatory ventilation for the prevention of chronic lung disease of prematurity.
Johnson AH, Peacock JL, Greenough A, Marlow N, Limb ES, Marston L, Calvert SA; United Kingdom Oscillation Study Group., N. Engl. J. Med. 347(9), 2002
PMID: 12200550
Late outcomes of a randomized trial of high-frequency oscillation in neonates.
Zivanovic S, Peacock J, Alcazar-Paris M, Lo JW, Lunt A, Marlow N, Calvert S, Greenough A, Zivanovic S, Peacock J, Alcazar-Paris M, Lo JW, Marlow N, Calvert S, Greenough A, Halliday H, Henderson J, Cunningham S, Vyas H, Kerry S, Dromgoole J, Coker B, Oedra R, Thomas F, D'eath T, Nguyen J, Lovestone J., N. Engl. J. Med. 370(12), 2014
PMID: 24645944
A survey of methods for analyzing clustered binary response data
Pendergast JF, Gange SJ, Newton MA, Lindstrom MJ, Palta M, Fisher MR., 1996
Regression models for twin studies: a critical review.
Carlin JB, Gurrin LC, Sterne JA, Morley R, Dwyer T., Int J Epidemiol 34(5), 2005
PMID: 16087687
Analysis of repeated pregnancy outcomes.
Louis GB, Dukic V, Heagerty PJ, Louis TA, Lynch CD, Ryan LM, Schisterman EF, Trumble A; Pregnancy Modeling Working Group., Stat Methods Med Res 15(2), 2006
PMID: 16615652
Analysis of neonatal clinical trials with twin births.
Shaffer ML, Kunselman AR, Watterberg KL., BMC Med Res Methodol 9(), 2009
PMID: 19245713
Analyzing binary outcome data with small clusters: A simulation study
Xu Y, Lee CF, Cheung YB., 2014
The continuing value of the Apgar score for the assessment of newborn infants.
Casey BM, McIntire DD, Leveno KJ., N. Engl. J. Med. 344(7), 2001
PMID: 11172187
Fitting Linear Mixed-Effects Models Using lme4
Bates D, Mächler M, Bolker B., 2015

Diggle PJ, Heagerty P, Liang K-Y, Zeger SL., 2002
The design of simulation studies in medical statistics.
Burton A, Altman DG, Royston P, Holder RL., Stat Med 25(24), 2006
PMID: 16947139
Dichotomising continuous data while retaining statistical power using a distributional approach.
Peacock JL, Sauzet O, Ewings SM, Kerry SM., Stat Med 31(26), 2012
PMID: 22865598
Gauss-hermite quadrature approximation for estimation in generalised linear mixed models
Pan J, Thompson R., 2003
Convergence of a stochastic approximation version of the EM algorithm
Delyon B, Lavielle M, Moulines E., 1999

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