Prediction and Inference With Missing Data in Patient Alert Systems

References

• Allison, P. D. (2000), “Multiple Imputation for Missing Data: A Cautionary Tale,” Sociological Methods & Research, 28, 301–309. DOI:10.1177/0049124100028003003.
   Web of Science ®Google Scholar
• Amemiya, T. (1984), “Tobit Models: A Survey,” Journal of Econometrics, 24, 3–61. DOI:10.1016/0304-4076(84)90074-5.
   Web of Science ®Google Scholar
• Antonio, C., and David, B. D. (2015), “Bayesian Multivariate Mixed-Scale Density Estimation,” Statistics and Its Interface, 8, 195–201.
   Web of Science ®Google Scholar
• Berger, J. O. (2013), Statistical Decision Theory and Bayesian Analysis, New York: Springer Science & Business Media.
   Google Scholar
• Bernaards, C. A., Belin, T. R., and Schafer, J. L. (2007), “Robustness of a Multivariate Normal Approximation for Imputation of Incomplete Binary Data,” Statistics in Medicine, 26, 1368–1382. DOI:10.1002/sim.2619.
   PubMed Web of Science ®Google Scholar
• Bhattacharya, A., and Dunson, D. B. (2012), “Simplex Factor Models for Multivariate Unordered Categorical Data,” Journal of the American Statistical Association, 107, 362–377. DOI:10.1080/01621459.2011.646934.
   PubMed Web of Science ®Google Scholar
• Buist, M. D., Jarmolowski, E., Burton, P. R., Bernard, S. A., Waxman, B. P., and Anderson, J. (1999), “Recognising Clinical Instability in Hospital Patients Before Cardiac Arrest or Unplanned Admission to Intensive Care. A Pilot Study in a Tertiary-Care Hospital,” The Medical Journal of Australia, 171, 22–25. DOI:10.5694/j.1326-5377.1999.tb123492.x.
   PubMed Web of Science ®Google Scholar
• Chib, S., and Greenberg, E. (1998), “Analysis of Multivariate Probit Models,” Biometrika, 85, 347–361. DOI:10.1093/biomet/85.2.347.
   Web of Science ®Google Scholar
• Dunson, D. B. (2000), “Bayesian Latent Variable Models for Clustered Mixed Outcomes,” Journal of the Royal Statistical Society, Series B, 62, 355–366. DOI:10.1111/1467-9868.00236.
   Google Scholar
• Dunson, D. B., and Park, J.-H. (2008), “Kernel Stick-Breaking Processes,” Biometrika, 95, 307–323. DOI:10.1093/biomet/asn012.
   PubMed Web of Science ®Google Scholar
• Efron, B. (1977), “The Efficiency of Cox’s Likelihood Function for Censored Data,” Journal of the American statistical Association, 72, 557–565. DOI:10.1080/01621459.1977.10480613.
   Web of Science ®Google Scholar
• Ferguson, T. S. (1973), “A Bayesian Analysis of Some Nonparametric Problems,” The Annals of Statistics, 1, 209–230. DOI:10.1214/aos/1176342360.
   Web of Science ®Google Scholar
• Finch, W. H. (2010), “Imputation Methods for Missing Categorical Questionnaire Data: A Comparison of Approaches,” Journal of Data Science, 8, 361–378.
   Google Scholar
• Friedman, J. (2001), “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, 29, 1189–1232. DOI:10.1214/aos/1013203451.
   Web of Science ®Google Scholar
• Gebregziabher, M., and DeSantis, S. M. (2010), “Latent Class Based Multiple Imputation Approach for Missing Categorical Data,” Journal of Statistical Planning and Inference, 140, 3252–3262. DOI:10.1016/j.jspi.2010.04.020.
   PubMed Web of Science ®Google Scholar
• Giudici, P., and Green, P. (1999), “Decomposable Graphical Gaussian Model Determination,” Biometrika, 86, 785–801. DOI:10.1093/biomet/86.4.785.
   Web of Science ®Google Scholar
• Green, P. J. (1995), “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination,” Biometrika, 82, 711–732. DOI:10.1093/biomet/82.4.711.
   Web of Science ®Google Scholar
• Green, P. J., and Hastie, D. I. (2009), “Reversible Jump MCMC,” Genetics, 155, 1391–1403.
   Google Scholar
• Griffiths, J. R., and Kidney, E. M. (2012), “Current Use of Early Warning Scores in UK Emergency Departments,” Emergency Medicine Journal, 29, 65–66. DOI:10.1136/emermed-2011-200508.
   PubMed Web of Science ®Google Scholar
• Gueorguieva, R. V., and Agresti, A. (2001), “A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses,” Journal of the American Statistical Association, 96, 1102–1112. DOI:10.1198/016214501753208762.
   Web of Science ®Google Scholar
• Heckman, J. J. (1976), “The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models,” in Annals of Economic and Social Measurement (Vol. 5, no. 4), Cambridge, MA: NBER, pp. 475–492.
   Google Scholar
• Helton, J., Johnson, J., Sallaberry, C., and Storlie, C. (2006), “Survey of Sampling-Based Methods for Uncertainty and Sensitivity Analysis,” Reliability Engineering and System Safety, 91, 1175–1209. DOI:10.1016/j.ress.2005.11.017.
   Web of Science ®Google Scholar
• Horton, N. J., and Kleinman, K. P. (2007), “Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models,” The American Statistician, 61, 79–90. DOI:10.1198/000313007X172556.
   PubMed Web of Science ®Google Scholar
• Horton, N. J., Lipsitz, S. R., and Parzen, M. (2003), “A Potential for Bias When Rounding in Multiple Imputation,” The American Statistician, 57, 229–232. DOI:10.1198/0003130032314.
   Web of Science ®Google Scholar
• Imai, K., and van Dyk, D. A. (2005), “A Bayesian Analysis of the Multinomial Probit Model Using Marginal Data Augmentation,” Journal of Econometrics, 124, 311–334. DOI:10.1016/j.jeconom.2004.02.002.
   Web of Science ®Google Scholar
• Ishwaran, H., and James, L. F. (2001), “Gibbs Sampling Methods for Stick-Breaking Priors,” Journal of the American Statistical Association, 96. DOI:10.1198/016214501750332758.
   Web of Science ®Google Scholar
• Kim, S., Tadesse, M. G., and Vannucci, M. (2006), “Variable Selection in Clustering via Dirichlet Process Mixture Models,” Biometrika, 93, 877–893. DOI:10.1093/biomet/93.4.877.
   Web of Science ®Google Scholar
• Kirkland, L. L., Malinchoc, M., O’Byrne, M., Benson, J. T., Kashiwagi, D. T., Burton, M. C., Varkey, P., and Morgenthaler, T. I. (2013), “A Clinical Deterioration Prediction Tool for Internal Medicine Patients,” American Journal of Medical Quality, 28, 135–142. DOI:10.1177/1062860612450459.
   PubMed Web of Science ®Google Scholar
• Kruschke, J. (2014), Doing Bayesian Data Analysis: A Tutorial With R, JAGS, and Stan, New York: Academic Press.
   Google Scholar
• Lesaffre, E., and Kaufmann, H. (1992), “Existence and Uniqueness of the Maximum Likelihood Estimator for a Multivariate Probit Model,” Journal of the American Statistical Association, 87, 805–811. DOI:10.1080/01621459.1992.10475282.
   Web of Science ®Google Scholar
• Li, F., Yu, Y., and Rubin, D. B. (2012), “Imputing Missing Data by Fully Conditional Models: Some Cautionary Examples and Guidelines,” Duke University Department of Statistical Science Discussion Paper 11-24.
   Google Scholar
• Lipsitz, S. R., and Ibrahim, J. G. (1996), “A Conditional Model for Incomplete Covariates in Parametric Regression Models,” Biometrika, 83, 916–922. DOI:10.1093/biomet/83.4.916.
   Web of Science ®Google Scholar
• McCulloch, R. E., Polson, N. G., and Rossi, P. E. (2000), “A Bayesian Analysis of the Multinomial Probit Model With Fully Identified Parameters,” Journal of Econometrics, 99, 173–193. DOI:10.1016/S0304-4076(00)00034-8.
   Web of Science ®Google Scholar
• Molenberghs, G., Beunckens, C., Sotto, C., and Kenward, M. G. (2008), “Every Missingness Not at Random Model Has a Missingness at Random Counterpart With Equal Fit,” Journal of the Royal Statistical Society, Series B, 70, 371–388. DOI:10.1111/j.1467-9868.2007.00640.x.
   Google Scholar
• Müller, P., Erkanli, A., and West, M. (1996), “Bayesian Curve Fitting Using Multivariate Normal Mixtures,” Biometrika, 83, 67–79.
   Web of Science ®Google Scholar
• Murray, J. S., and Reiter, J. P. (2016), “Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence,” Journal of the American Statistical Association, 111, 1466–1479. DOI:10.1080/01621459.2016.1174132.
   Web of Science ®Google Scholar
• Nobile, A. (1998), “A Hybrid Markov Chain for the Bayesian Analysis of the Multinomial Probit Model,” Statistics and Computing, 8, 229–242.
   Web of Science ®Google Scholar
• Peberdy, M. A., Kaye, W., Ornato, J. P., Larkin, G. L., Nadkarni, V., Mancini, M. E., Berg, R. A., Nichol, G., Lane-Trultt, T., and NRCPR Investigators (2003), “Cardiopulmonary Resuscitation of Adults in the Hospital: A Report of 14 720 Cardiac Arrests From the National Registry of Cardiopulmonary Resuscitation,” Resuscitation, 58, 297–308. DOI:10.1016/S0300-9572(03)00215-6.
   PubMed Web of Science ®Google Scholar
• Prytherch, D. R., Smith, G. B., Schmidt, P. E., and Featherstone, P. I. (2010), “ViEWS—Towards a National Early Warning Score for Detecting Adult Inpatient Deterioration,” Resuscitation, 81, 932–937. DOI:10.1016/j.resuscitation.2010.04.014.
   PubMed Web of Science ®Google Scholar
• Raftery, A. E., and Dean, N. (2006), “Variable Selection for Model-Based Clustering,” Journal of the American Statistical Association, 101, 168–178. DOI:10.1198/016214506000000113.
   Web of Science ®Google Scholar
• Raghunathan, T. E., Lepkowski, J. M., Van Hoewyk, J., and Solenberger, P. (2001), “A Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models,” Survey Methodology, 27, 85–96.
   Google Scholar
• Romero-Brufau, S., Huddleston, J. M., Naessens, J. M., Johnson, M. G., Hickman, J., Morlan, B. W., Jensen, J. B., Caples, S. M., Elmer, J. L., Schmidt, J. A., and Morgenthaler, T. I. (2014), “Widely Used Track and Trigger Scores: Are They Ready for Automation in Practice?,” Resuscitation, 85, 549–552. DOI:10.1016/j.resuscitation.2013.12.017.
   PubMed Web of Science ®Google Scholar
• Roy, J., Lum, K. J., Daniels, M. J., Zeldow, B., Dworkin, J., and Re, V. L., III (2017), “Bayesian Nonparametric Generative Models for Causal Inference With Missing at Random Covariates,” arXiv no. 1702.08496.
   Google Scholar
• Rubin, D. B. (1976), “Inference and Missing Data,” Biometrika, 63, 581–592. DOI:10.1093/biomet/63.3.581.
   Web of Science ®Google Scholar
• Schafer, J. L. (1997), Analysis of Incomplete Multivariate Data, Boca Raton, FL: CRC Press.
   Google Scholar
• Schein, R., Hazday, N., Pena, M., Ruben, B. H., and Sprung, C. L. (1990), “Clinical Antecedents to In-Hospital Cardiopulmonary Arrest,” Chest Journal, 98, 1388–1392. DOI:10.1378/chest.98.6.1388.
   PubMed Web of Science ®Google Scholar
• Sethuraman, J. (1994), “A Constructive Definition of Dirichlet Priors,” Statistica Sinica, 4, 639–650.
   Web of Science ®Google Scholar
• Shahbaba, B., and Neal, R. (2009), “Nonlinear Models Using Dirichlet Process Mixtures,” Journal of Machine Learning Research, 10, 1829–1850.
   Web of Science ®Google Scholar
• Si, Y., and Reiter, J. P. (2013), “Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys,” Journal of Educational and Behavioral Statistics, 38, 499–521. DOI:10.3102/1076998613480394.
   Web of Science ®Google Scholar
• Smith, G. B., Prytherch, D. R., Meredith, P., Schmidt, P. E., and Featherstone, P. I. (2013), “The Ability of the National Early Warning Score (NEWS) to Discriminate Patients at Risk of Early Cardiac Arrest, Unanticipated Intensive Care Unit Admission, and Death,” Resuscitation, 84, 465–470. DOI:10.1016/j.resuscitation.2012.12.016.
   PubMed Web of Science ®Google Scholar
• Stekhoven, D. J., and Bühlmann, P. (2012), “Missforest: Non-Parametric Missing Value Imputation for Mixed-Type Data,” Bioinformatics, 28, 112–118. DOI:10.1093/bioinformatics/btr597.
   PubMed Web of Science ®Google Scholar
• Storlie, C. B., Michalak, S. E., Quinn, H. M., Dubois, A. J., Wender, S. A., and Dubois, D. H. (2013), “A Bayesian Reliability Analysis of Neutron-Induced Errors in High Performance Computing Hardware,” Journal of the American Statistical Association, 108, 429–440. DOI:10.1080/01621459.2013.770694.
   Web of Science ®Google Scholar
• Storlie, C. B., Myers, S., Colligan, R. C., Weaver, A. L., Voigt, R., Croarkin, P. E., Leibson, C. L., Stoeckel, R. E., Katusic, S. K., and Port, J. D. (2017), “Model-Based Clustering With Mixed Continuous and Discrete Variables via Dirichlet Process Models,” arXiv no. 1703.08741.
   Google Scholar
• Storlie, C. B., Reich, B., Helton, J., Swiler, L., and Sallaberry, C. (2013), “Analysis of Computationally Demanding Models With Continuous and Categorical Inputs,” Reliability Engineering and System Safety, 113, 30–41. DOI:10.1016/j.ress.2012.11.018.
   Web of Science ®Google Scholar
• Talhouk, A., Doucet, A., and Murphy, K. (2012), “Efficient Bayesian Inference for Multivariate Probit Models With Sparse Inverse Correlation Matrices,” Journal of Computational and Graphical Statistics, 21, 739–757. DOI:10.1080/10618600.2012.679239.
   Web of Science ®Google Scholar
• Van Buuren, S., Brand, J. P., Groothuis-Oudshoorn, C., and Rubin, D. B. (2006), “Fully Conditional Specification in Multivariate Imputation,” Journal of Statistical Computation and Simulation, 76, 1049–1064. DOI:10.1080/10629360600810434.
   Web of Science ®Google Scholar
• Vermunt, J. K., Van Ginkel, J. R., Der Ark, V., Andries, L., and Sijtsma, K. (2008), “Multiple Imputation of Incomplete Categorical Data Using Latent Class Analysis,” Sociological Methodology, 38, 369–397. DOI:10.1111/j.1467-9531.2008.00202.x.
   Web of Science ®Google Scholar
• Wade, S., Dunson, D. B., Petrone, S., and Trippa, L. (2014), “Improving Prediction From Dirichlet Process Mixtures via Enrichment,” The Journal of Machine Learning Research, 15, 1041–1071.
   Web of Science ®Google Scholar
• Wong, F., Carter, C. K., and Kohn, R. (2003), “Efficient Estimation of Covariance Selection Models,” Biometrika, 90, 809–830. DOI:10.1093/biomet/90.4.809.
   Web of Science ®Google Scholar
• Xu, D., Daniels, M. J., and Winterstein, A. G. (2016), “Sequential BART for Imputation of Missing Covariates,” Biostatistics, 17, 589–602. DOI:10.1093/biostatistics/kxw009.
   PubMed Web of Science ®Google Scholar
• Zhang, X., Boscardin, W. J., and Belin, T. R. (2008), “Bayesian Analysis of Multivariate Nominal Measures Using Multivariate Multinomial Probit Models,” Computational Statistics & Data Analysis, 52, 3697–3708. DOI:10.1016/j.csda.2007.12.012.
   PubMed Web of Science ®Google Scholar