Document Zbl 1226.62121

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Under the missing at random assumption, we propose semiparametric working models to obtain low-dimensional summarized scores. An estimator for the mean total cost can be derived nonparametrically conditional on the summarized scores. We show that when either the two working models for cost-survival process or the model for censoring distribution is correct, the estimator is consistent and asymptotically normal. Small-sample performance of the proposed method is evaluated via simulation studies. Finally, our approach is applied to analyze a real data set in health economics.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A5975686">Cited in <strong>3</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A62P10" title="MSC2020">62P10</a> </td> <td class="space"> Applications of statistics to biology and medical sciences; meta analysis </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62G05" title="MSC2020">62G05</a> </td> <td class="space"> Nonparametric estimation </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62G20" title="MSC2020">62G20</a> </td> <td class="space"> Asymptotic properties of nonparametric inference </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62N01" title="MSC2020">62N01</a> </td> <td class="space"> Censored data models </td> </tr><tr> <td class="mono"> 65C60 </td> <td class="space"> Computational problems in statistics (MSC2010) </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Adouble+robust">double robust</a>; <a href="/?q=ut%3Akernel+estimation">kernel estimation</a>; <a href="/?q=ut%3Amissing+at+random">missing at random</a>; <a href="/?q=ut%3Asemiparametric+estimation">semiparametric estimation</a></div>  <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div 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1226.62121 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1111/j.1541-0420.2010.01540.x" aria-label="DOI for “Estimating mean cost using auxiliary covariates”" title="10.1111/j.1541-0420.2010.01540.x">DOI</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Andersen, Cox’s regression model for counting processes: A large sample study, Annals of Statistics 10 pp 1100– (1982) · <a href="/0526.62026" class="nowrap">Zbl 0526.62026</a> · <a href="https://doi.org/10.1214/aos/1176345976" class="nowrap">doi:10.1214/aos/1176345976</a></td> </tr><tr> <td>[2]</td> <td class="space">Bang, Doubly robust estimation in missing data and causal inference models, Biometrics 61 pp 962– (2005) · <a href="/1087.62121" class="nowrap">Zbl 1087.62121</a> · <a 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