A frequent problem in estimating logistic regression models is a failure of the likelihood maximization algorithm to converge. In most cases, this failure is a consequence of data patterns known as complete or quasi-complete separation. For these patterns, the maximum likelihood estimates simply do not exist. In this paper, I examine how and why complete or quasi-complete separation occur, and the effects they produce in output from SAS ® procedures.I then describe and evaluate several possible solutions.
By providing a forum for clear communications among all providers, discharge rounds have streamlined the care of complex trauma patients. As health care resources become ever more constrained, this sort of multidisciplinary effort is a viable option for senior physicians to directly impact hospital performance.
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