Lifetime Data Anal - Evaluating hospital readmission rates in dialysis facilities; adjusting for hospital effects.

Tópicos

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Resumo

Motivated by the national evaluation of readmission rates among kidney dialysis facilities in the United States, we evaluate the impact of including discharging hospitals on the estimation of facility-level standardized readmission ratios (SRRs). The estimation of SRRs consists of two steps. First, we model the dependence of readmission events on facilities and patient-level characteristics, with or without an adjustment for discharging hospitals. Second, using results from the models, standardization is achieved by computing the ratio of the number of observed events to the number of expected events assuming a population norm and given the case-mix in that facility. A challenging aspect of our motivating example is that the number of parameters is very large and estimation of high-dimensional parameters is troublesome. To solve this problem, we propose a structured Newton-Raphson algorithm for a logistic fixed effects model and an approximate EM algorithm for the logistic mixed effects model. We consider a re-sampling and simulation technique to obtain p-values for the proposed measures. Finally, our method of identifying outlier facilities involves converting the observed p-values to Z-statistics and using the empirical null distribution, which accounts for overdispersion in the data. The finite-sample properties of proposed measures are examined through simulation studies. The methods developed are applied to national dialysis data. It is our great pleasure to present this paper in honor of Ross Prentice, who has been instrumental in the development of modern methods of modeling and analyzing life history and failure time data, and in the inventive applications of these methods to important national data problem.

Resumo Limpo

motiv nation evalu readmiss rate among kidney dialysi facil unit state evalu impact includ discharg hospit estim facilitylevel standard readmiss ratio srrs estim srrs consist two step first model depend readmiss event facil patientlevel characterist without adjust discharg hospit second use result model standard achiev comput ratio number observ event number expect event assum popul norm given casemix facil challeng aspect motiv exampl number paramet larg estim highdimension paramet troublesom solv problem propos structur newtonraphson algorithm logist fix effect model approxim em algorithm logist mix effect model consid resampl simul techniqu obtain pvalu propos measur final method identifi outlier facil involv convert observ pvalu zstatist use empir null distribut account overdispers data finitesampl properti propos measur examin simul studi method develop appli nation dialysi data great pleasur present paper honor ross prentic instrument develop modern method model analyz life histori failur time data invent applic method import nation data problem

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