Survival analysis and competing risk models of hospital length of stay and discharge destination: The effect of distributional assumptions

Health Services and Outcomes Research Methodology

Volumn 7, Issue 3-4, 2007, Pages 109-124

  Sá, Carla ^a   Dismuke, Clara E ^b   Guimarães, Paulo ^c  

^a UNIVERSITY OF MINHO   (Portugal)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^c UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

Hazard functions; Outcomes; Unobserved heterogeneity

Indexed keywords

ARTICLE; EXPLANATORY VARIABLE; FEMALE; HEALTH CARE UTILIZATION; HEALTH SERVICES RESEARCH; HOSPITAL BILLING; HOSPITAL DISCHARGE; HUMAN; INTENSIVE CARE; LENGTH OF STAY; MAJOR CLINICAL STUDY; MALE; OUTCOME ASSESSMENT; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; SURVIVAL;

EID: 35948949024     PISSN: 13873741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10742-007-0020-9     Document Type: Article

References (31)

1. Allison, P.D.: Event History Analysis. Regression for Longitudinal Event Data. Beverly Hills, Sage (1984)
2. Bedford, T., Lindqvist, B. H.: The identifiability problem for repairable systems subject to competing risks. Adv. Appl. Prob. 36, 774-790 2004
3. Burns, L., Wholey, D.R.: The effects of patient, hospital and physician characteristics on length of stay and mortality. Med. Care 29 (3), 251-271 (1991)
4. Cameron, A.C.M Trivedi, P.K.: Microeconometrics: methods and applications. Cambridge, Cambridge University Press (2005)
5. Canoodt, L.J., Knickman, J.R.: The effect of hospital characteristics and organizational factors on pre- and prospective lengths of hospital stay. Health Serv. Res. 19(5), 561-585 (1984)
6. Charlson, M.E., Pompei P., Ales K.L.: A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 40(5), 373-383 (1987)
7. Clark, D.E., Ryan, L.M.: Concurrent prediction of hospital mortality and length of stay from risk factors on admission. Health Serv. Res. 37(3), 631-645 (2002)
8. Clarke, A., Rosen, R.: Length of stay. How short should hospital care be? Euro. J. Public Health 11(2), 166-170 (2001)
9. Clegg, L., Cai, J., Sen, P., Kupper, L.: Misspecification of marginal hazard models in multivariate failure time data. Sankhya: Indian J. Stat. 62, 25-42 (2000)
10. Cox, D.R.: The analysis of exponentially distributed lifetimes with two types of failure. J. R. Stat. Soc. Ser. B 21, 411-421 (1959)
11. Culebras, A., Case, C.S., Masdeu, J.C., Fox, A.J., Bryan, R.N., Grossman, C.B., Lee, D.H., Adams, H.P., Thies, W.: Practice Guidelines for the Use of Imaging in Transient Ischemic Attacks and Acute Stroke A Report of the Stroke Council American Heart Association. Stroke 28(7), 1480-1497 (1997)
12. Cutler, D.M.: The incidence of adverse medical outcomes under prospective payment. Econometrica 63(1), 29-50 (1995)
13. David, H.A., Moeschberger, M.L.: The Theory of Competing Risks. London, Griffon (1978)
14. Dismuke, C.E., Guimaraes, P.: Has the caveat of case-mix based payment influenced the quality of inpatient hospital care in Portugal? Appl. Econ. 34, 1301-13070 (2002)
15. Elbers, C., Ridder, G.: True and Tpurious duration dependence: the identifiability of the proportional hazard model. Rev. Econ. Stat. 49(3), 403-409 (1982)
16. Venn, P., Davies, P.: Note: variations in length of stay. J. Health Econ. 9(2), 223-243 (1990)
17. Gooley, T.A., Leisenring W., Crowley J., Storer B.E.: Estimation of failure probabilities in the presence of competing risks: New representations of old estimators. Stat. Med. 18(6), 695-706 (1999)
18. Hamilton, B.H., Hamilton, V.H., Mayo, N.E.: What are the costs of queuing for hip fracture surgery in Canada? J. Health Econ. 15(2), 161-185 (1996)
19. Hamilton, B.H., Hamilton, V.: Estimating surgical volume-outcome relationships applying survival models: Accounting for frailty and hospital fixed effects. Health Econ. 6(4), 383-395 (1997)
20. Heckman, J., Singer, B.: A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52(2), 271-320 (1984)
21. Heckman, J.J., Honoré, B.E.: The identifiability of the competing risks model. Biometrika 76(2), 325-330 (1989)
22. Ho, V., Hamilton, B., Roos, L.L.: Multiple approaches to assessing the effects of delays for hip fracture patients in the United States and Canada. Health Serv. Res. 34(7), 1499-1518 (2000)
23. Hougaard, P.: Life table methods for heterogeneous populations: distributions describing the heterogeneity. Biometrika 71(1), 75-83 (1984)
24. Jones. A.: Health econometrics. In: Newhouse, J.P., Culyer, A.J. (eds.) Handbook of Health Economics. Amsterdam: The Netherlands (2000)
25. Lancaster, T.: The Econometric Analysis of Transition Data. Cambridge, Cambridge University Press (1990)
26. Patient Classification Systems Europe. Country Update. 19th Patient Classification Systems Europe Conference, Washington DC, 08.-11. October 2003.
27. Picone, G., Wilson, R.M., Chou, S.Y.: Analysis of hospital length of stay and discharge destination using hazard functions with unmeasured heterogeneity. Health Econ. 12(12), 1021-1034 (2003)
28. Prentice, R.L., Kalbfleisch, J.D., Peterson, A.V., Flournoy, N., Farewell, V.T., Breslow, N. E.: The analysis of failure times in the presence of competing risks. Biometrics 34, 541-554 (1978)
29. Rodriguez, G.: 2000. Survival AnaHysis. Available at: http://data.princeton.edu//wws509/notes/c7.pdf
30. Shi, L.: Patient and hospital characteristics associated with average length of stay. Health Care Manage. Rev. 21(2), 46-61 (1996)
31. Therneau, T.M., Grambsch, P.M.: Modeling Survival Data: Extending the Cox Model. New York, Springer (2000)