TY - JOUR
T1 - Length of stay as a stochastic process
T2 - A general approach and application to hospitalization for schizophrenia
AU - Eaton, William W.
AU - Whitmore, G. A.
N1 - Funding Information:
†This work was completed while Eaton was supported by NIMH grant #MH25691. We are grateful to Morton Kramer and Irving Goldberg of the NIMH Biometry Branch for granting us access to (unidentified) files of the Maryland Psychiatric Case Register. Figure 3 was redrawn from Tweedie (1957), and permission to reproduce it is thankfully acknowledged.
PY - 1977/1/1
Y1 - 1977/1/1
N2 - A general approach to the study of length of stay (LOS) for hospitalization is presented. Data on first hospitalization for schizophrenia from the Maryland Psychiatric Case Register are applied to discussions of the Life Table and seven stochastic models of the LOS process. As far as possible, prior applications of the various models to this process are reviewed, and the models are conceptualized on the individual and aggregate level. The models are the exponential, mixed exponential, type XI, Weibull, gamma, lognormal and Inverse Gaussian. The lognormal and Inverse Gaussian show the best fits to the data in terms of the maximum absolute deviation. However, the Inverse Gaussian is superior due to its attractive statistical characterization. Special attention is given to the relatively new Inverse Gaussian, and there is a brief section on LOS and theory verification. Recommendations are made for future LOS research.
AB - A general approach to the study of length of stay (LOS) for hospitalization is presented. Data on first hospitalization for schizophrenia from the Maryland Psychiatric Case Register are applied to discussions of the Life Table and seven stochastic models of the LOS process. As far as possible, prior applications of the various models to this process are reviewed, and the models are conceptualized on the individual and aggregate level. The models are the exponential, mixed exponential, type XI, Weibull, gamma, lognormal and Inverse Gaussian. The lognormal and Inverse Gaussian show the best fits to the data in terms of the maximum absolute deviation. However, the Inverse Gaussian is superior due to its attractive statistical characterization. Special attention is given to the relatively new Inverse Gaussian, and there is a brief section on LOS and theory verification. Recommendations are made for future LOS research.
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U2 - 10.1080/0022250X.1977.9989877
DO - 10.1080/0022250X.1977.9989877
M3 - Article
AN - SCOPUS:84947885725
SN - 0022-250X
VL - 5
SP - 273
EP - 292
JO - The Journal of Mathematical Sociology
JF - The Journal of Mathematical Sociology
IS - 2
ER -