Fitting dynamic models with forcing functions: Application to continuous glucose monitoring in insulin therapy

Statistics in Medicine

Volumn 30, Issue 18, 2011, Pages 2234-2250

  Lunn, D J ^a   Wei, C ^a   Hovorka, R ^b  

^a MRC BIOSTATISTICS UNIT   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Artificial pancreas; Autoregressive models; Dynamic models; Glucose kinetics; WinBUGS

Indexed keywords

GLUCOSE; INSULIN;

ARTICLE; BIOSENSOR; BLOOD GLUCOSE MONITORING; COMPUTER PROGRAM; DATA ANALYSIS; GLUCOSE BLOOD LEVEL; INSULIN TREATMENT; MATHEMATICAL MODEL; PREDICTION; REGRESSION ANALYSIS; RELIABILITY; STATISTICAL ANALYSIS;

BLOOD GLUCOSE; CHILD; DIABETES MELLITUS, TYPE 1; HUMANS; INSULIN; KINETICS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PANCREAS, ARTIFICIAL;

EID: 79960229278     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4254     Document Type: Article

References (57)

1. Atkinson MA, Eisenbarth GS. Type 1 diabetes: new perspectives on disease pathogenesis and treatment. The Lancet 2001; 358:221-229.
2. Diabetic Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long term complications in insulin-dependent diabetes mellitus. New England Journal of Medicine 1993; 329:977-986.
3. American Diabetes Association. Standards of medical care in diabetes-2010. Diabetes Care 2010; 33:S11-S96.
4. Pickup J, Keen H. Continuous subcutaneous insulin infusion at 25 years: evidence base for the expanding use of insulin pump therapy in type 1 diabetes. Diabetes Care 2002; 25:593-598.
5. Cryer PE. The barrier of hypoglycemia in diabetes. Diabetes 2008; 57:3169-3176.
6. Heinemann L. Variability of insulin absorption and insulin action. Diabetes Technology and Therapeutics 2002; 4:673-682.
7. Carroll MF, Schade DS. The dawn phenomenon revisited: implications for diabetes therapy. Endocrine Practice 2005; 11:55-64.
8. Riazi A, Pickup J, Bradley C. Daily stress and glycaemic control in Type 1 diabetes: individual differences in magnitude, direction, and timing of stress-reactivity. Diabetes Research and Clinical Practice 2004; 66:237-244.
9. Klonoff DC. Continuous glucose monitoring: roadmap for 21st century diabetes therapy. Diabetes Care 2005; 28:1231-1239.
10. Kamath A, Mahalingam A, Brauker J. Methods of evaluating the utility of continuous glucose monitor alerts. Journal of Diabetes Science and Technology 2010; 4:57-66.
11. Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase P, Clemons R, Fiallo-Scharer R, Fox LA, Gilliam LK, Hirsch IB, Huang ES, Kollman C, Kowalski AJ, Laffel L, Lawrence JM, Lee J, Mauras N, O'Grady M, Ruedy KJ, Tansey M, Tsalikian E, Weinzimer S, Wilson DM, Wolpert H, Wysocki T, Xing D. Continuous glucose monitoring and intensive treatment of type 1 diabetes. New England Journal of Medicine 2008; 359:1464-1476.
12. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. The effect of continuous glucose monitoring in well-controlled type 1 diabetes. Diabetes Care 2009; 32:1378-1383.
13. Hovorka R. Continuous glucose monitoring and closed-loop systems. Diabetic Medicine 2006; 23:1-12.
14. Steil GM, Rebrin K, Hariri F, Jinagonda S, Tadros S, Darwin C, Saad MF. Interstitial fluid glucose dynamics during insulin-induced hypoglycaemia. Diabetologia 2005; 48:1833-1840.
15. Pfeiffer EF, Meyerhoff C, Bischof F, Keck FS, Kerner W. On line continuous monitoring of subcutaneous tissue glucose is feasible by combining portable glucosensor with microdialysis. Hormone and Metabolic Research 1993; 25:121-124.
16. Sternberg F, Meyerhoff C, Mennel FJ, Mayer H, Bischof F, Pfeiffer EF. Does fall in tissue glucose precede fall in blood glucose? Diabetologia 1996; 39:609-612.
17. Wilinska ME, Chassin LJ, Acerini CL, Allen JM, Dunger DB, Hovorka R. Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes. Journal of Diabetes Science and Technology 2010; 4:132-144.
18. Diabetes Research in Children Network (DirecNet) Study Group. The accuracy of the CGMS in children with type 1 diabetes: results of the Diabetes Research in Children Network (DirecNet) accuracy study. Diabetes Technology and Therapeutics 2003; 5:781-789.
19. Diabetes Research in Children Network (DirecNet) Study Group. The accuracy of the GlucoWatch G2 biographer in children with type 1 diabetes: results of the Diabetes Research in Children Nnetwork (DirecNet) accuracy study. Diabetes Technology and Therapeutics 2003; 5:791-800.
20. Kovatchev B, Anderson S, Heinemann L, Clarke W. Comparison of the numerical and clinical accuracy of four continuous glucose monitors. Diabetes Care 2008; 31:1160-1164.
21. Keenan DB, Cartaya R, Mastrototaro JJ. Accuracy of a new real-time continuous glucose monitoring algorithm. Journal of Diabetes Science and Technology 2010; 4:111-118.
22. Kamath A, Mahalingam A, Brauker J. Analysis of time lags and other sources of error of the DexCom SEVEN continuous glucose monitor. Diabetes Technology and Therapeutics 2009; 11:689-695.
23. Beck RW, Buckingham B, Miller K, Wolpert H, Xing D, Block JM, Chase HP, Hirsch I, Kollman C, Laffel L, Lawrence JM, Milaszewski K, Ruedy KJ, Tamborlane WV. Factors predictive of use and of benefit from continuous glucose monitoring in type 1 diabetes. Diabetes Care 2009; 32:1947-1953.
24. Hovorka R, Allen JM, Elleri D, Chassin LJ, Harris J, Xing D, Kollman C, Hovorka T, Larsen AM, Nodale M, De Palma A, Wilinska ME, Acerini CL, Dunger DB. Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial. The Lancet 2010; 375:743-751.
25. Hovorka R, Wilinska ME, Chassin LJ, Dunger DB. Roadmap to the artificial pancreas. Diabetes Research and Clinical Practice 2006; 74:S178-S182.
26. Bequette BW. A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. Diabetes Technology and Therapeutics 2005; 7:28-47.
27. Bode B, Gross K, Rikalo N, Schwartz S, Wahl T, Page C, Gross T, Mastrototaro J. Alarms based on real-time sensor glucose values alert patients to hypo-and hyperglycemia: the Guardian continuous monitoring system. Diabetes Technology and Therapeutics 2004; 6:105-113.
28. Breton M, Kovatchev B. Analysis, modeling, and simulation of the accuracy of continuous glucose sensors. Journal of Diabetes Science and Technology 2008; 2:853-862.
29. Lauritzen SL, Dawid AP, Larsen BN, Leimer HG. Independence properties of directed Markov fields. Networks 1990; 20:491-505.
30. Spiegelhalter DJ. Bayesian graphical modelling: a case-study in monitoring health outcomes. Applied Statistics 1998; 47:115-133.
31. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 2000; 10:325-337.
32. Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: evolution, critique and future directions (with Discussion). Statistics in Medicine 2009; 28:3049-3067.
33. Lunn D. WinBUGS Differential Interface-worked examples, Department of Epidemiology and Public Health, Imperial College School of Medicine, London, 2004.
34. Press WH, Teukolsky SA, Vetterling WT, Flannery BP. Numerical Recipes in C++: The Art of Scientific Computing. (2nd edn). Cambridge University Press: Cambridge, U.K., 2002; 719-727.
35. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equations of state calculations by fast computing machines. Journal of Chemical Physics 1953; 21:1087-1091.
36. Hastings WK. Monte Carlo sampling-based methods using Markov chains and their applications. Biometrika 1970; 57:97-109.
37. Jayne SR, Tokmakian R. Forcing and sampling of ocean general circulation models: impact of high-frequency motions. Journal of Physical Oceanography 1997; 27:1173-1179.
38. Coffaro G, Sfriso A. Simulation model of Ulva rigida growth in shallow water of the Lagoon of Venice. Ecological Modelling 1997; 102:55-66.
39. Sunehag AL, Dalla Man C, Toffolo G, Haymond MW, Bier DM, Cobelli C. Beta-cell function and insulin sensitivity in adolescents from an OGTT. Obesity 2009; 17:233-239.
40. Rebrin K, Steil GM, van Antwerp WP, Mastrototaro JJ. Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. American Journal of Physiology 1999; 277:E561-E571.
41. Wilinska ME, Bodenlenz M, Chassin LJ, Schaller HC, Schaupp LA, Pieber TR, Hovorka R. Interstitial glucose kinetics in subjects with type 1 diabetes under physiologic conditions. Metabolism 2004; 53:1484-1491.
42. Besag J. Spatial interaction and the statistical analysis of lattice systems (with Discussion). Journal of The Royal Statistical Society, Series B 1974; 36:192-236.
43. Besag J, York JC, Mollié A. Bayesian image restoration, with two applications in spatial statistics (with Discussion). Annals of the Institute of Statistical Mathematics 1991; 43:1-59.
44. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit (with Discussion). Journal of The Royal Statistical Society, Series B 2002; 64:583-639.
45. Lunn D, Best N, Spiegelhalter D, Graham G, Neuenschwander B. Combining MCMC with 'sequential' PKPD modelling. Journal of Pharmacokinetics and Pharmacodynamics 2009; 36:19-38.
46. Johnson N. Systems of frequency curves generated by methods of translation. Biometrika 1949; 36:149-176.
47. Bhattacharjee M, Botting C, Sillanpää M. Bayesian biomarker identification based on marker-expression proteomics data. Genomics 2008; 92:384-392.
48. Carrigan G, Barnett A, Dobson A, Mishra G. Compensating for missing data from longitudinal studies using WinBUGS. Journal of Statistical Software 2007; 19(7).
49. Choi J, Fuentes M, Reich BJ. Spatial-temporal association between fine particulate matter and daily mortality. Computational Statistics and Data Analysis 2009; 53:2989-3000.
50. He Y, Zaslavsky A. Combining information from cancer registry and medical records data to improve analyses of adjuvant cancer therapies. Biometrics 2009; 65(3):946-952.
51. Jackson C, Best N, Richardson S. Bayesian graphical models for regression on multiple data sets with different variables. Biostatistics 2009; 10:335-351.
52. Jackson C, Best N, Richardson S. Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors. Journal of the Royal Statistical Society, Series A 2008; 171:159-178.
53. McCandless L, Douglas I, Evans S, Smeeth L. Cutting feedback in Bayesian regression adjustment for the propensity score. The International Journal of Biostatistics 2010; 6(2). Article 16. DOI: 10.2202/1557-4679.1205.
54. Mwalili S, Lesaffre E, Declerck D. A Bayesian ordinal logistic regression model to correct for inter-observer measurement error in a geographical oral health study. Journal of the Royal Statistical Society, Series C 2005; 54:77-93.
55. Ramsay JO, Hooker G, Campbell D, Cao J. Parameter estimation for differential equations: a generalized smoothing approach (with Discussion). Journal of The Royal Statistical Society, Series B 2007; 69:741-796.
56. Campbell D. Bayesian collocation tempering and generalized profiling for estimation of parameters from differential equation models. Ph.D. Thesis, McGill University, Montreal, Canada, 2007.
57. Plummer M. JAGS Version 1.0.3 Manual. IARC, Lyon, France, 2008.