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From |
Cameron McIntosh <cnm100@hotmail.com> |

To |
STATA LIST <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Analysis of diagnostic properties of imputed ordinal score |

Date |
Wed, 12 Oct 2011 15:12:55 -0400 |

Roland, How exactly did you impute your ordinal missing values? I would suspect that you'd have to look at the variability in accuracy (sensitivity+specificity) across all imputed datasets, and construct CIs for the ROC models based on that variability (not sure if you're also using SSLRs but the approach would be similar). I'd also use more than 5 imputations -- at least 20 but perhaps even more. I would also strongly suggest having a look at the following papers. Pérez, A., Dennis, R.J., Gil, J.F., Rondón, M.A., & López, A. (2002). Use of the mean, hot deck and multiple imputation techniques to predict outcome in intensive care unit patients in Colombia. Statistics in Medicine, 21(24), 3885-3896. Long, Q., Zhang, X., & Hsu, C.-H. (2011). Nonparametric multiple imputation for receiver operating characteristics analysis when some biomarker values are missing at random. Statistics in Medicine, Early View. http://onlinelibrary.wiley.com/doi/10.1002/sim.4338/abstract;jsessionid=63E100FD9A64CCB7B6C8E6D57CA08581.d01t02 Liu, D., & Zhou, X.-H. (January 21, 2011). Semiparametric Estimation of the Covariate-Specific ROC Curve in Presence of Ignorable Verification Bias. UW Biostatistics Working Paper Series. Working Paper 374. Seattle, WA: University of Washington - Seattle Campus. http://www.bepress.com/cgi/viewcontent.cgi?article=1213&context=uwbiostat An, Y. (2011). Empirical Likelihood Confidence Intervals for ROC Curves with Missing Data. Mathematics Theses. Paper 95. http://digitalarchive.gsu.edu/math_theses/95 Janssen, K.J.M., Vergouwe, Y., Donders, A.R.T., Harrell, F.E., Jr., Chen, Q., Grobbee, D.E., & Moons, K.G.M. (2009). Dealing with Missing Predictor Values When Applying Clinical Prediction Models. Clinical Chemistry, 55, 994-1001. http://www.clinchem.org/cgi/reprint/55/5/994 http://www.clinchem.org/cgi/content/full/clinchem.2008.115345/DC1 Liu, D., & Zhou, X.-H. (2010). A model for adjusting for nonignorable verification bias in estimation of the ROC curve and its area with likelihood-based approach. Biometrics, 66(4), 1119-1128. Hope this helps, Cam > Date: Wed, 12 Oct 2011 11:16:29 +0200 > Subject: st: Analysis of diagnostic properties of imputed ordinal score > From: rolandersson@gmail.com > To: statalist@hsphsun2.harvard.edu > > We are analysing the diagnostic properties (sens, spec) of a clinical > score. We had to impute some missing values for variables that are > included in the score. We now have 5 imputed sets of data with a > clinical score with values from 1 to 14 and the corresponding > distribution of diseased and non-diseased patients. How can I analyse > the diagnostic properties of the score at different cut-off points > using appropriate method for the imputed sets of data? > > Roland Andersson > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Analysis of diagnostic properties of imputed ordinal score***From:*roland andersson <rolandersson@gmail.com>

**References**:**st: Analysis of diagnostic properties of imputed ordinal score***From:*roland andersson <rolandersson@gmail.com>

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