Validation of models for predicting the use of health technologies

Medical Decision Making

Volumn 18, Issue 3, 1998, Pages 311-319

  Scalon, João D ^a   Freire, Sergio M ^a   Cunha, Tacio A ^a  

^a NONE

Author keywords

Bootstrap; Forecasting; Health facility planning; Health technology; Linear models; Model validation; Neonatal intensive care; Regression analysis

Indexed keywords

ARTICLE; ARTIFICIAL VENTILATION; BLOOD GAS ANALYSIS; COVARIANCE; DECISION MAKING; FEMALE; HEALTH CARE; HEALTH CARE FACILITY; HEALTH CARE UTILIZATION; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL TECHNOLOGY; NEWBORN; NEWBORN INTENSIVE CARE; POSITIVE END EXPIRATORY PRESSURE; PROGNOSIS; REGRESSION ANALYSIS; STATISTICAL MODEL; VALIDATION PROCESS;

BLOOD GAS ANALYSIS; BRAZIL; DECISION SUPPORT TECHNIQUES; FEMALE; FORECASTING; HEALTH SERVICES NEEDS AND DEMAND; HUMANS; INFANT, NEWBORN; INTENSIVE CARE, NEONATAL; LINEAR MODELS; MALE; OXYGEN INHALATION THERAPY; POSITIVE-PRESSURE RESPIRATION; PROGNOSIS; REPRODUCIBILITY OF RESULTS; RESPIRATION, ARTIFICIAL; RETROSPECTIVE STUDIES;

EID: 0031781225     PISSN: 0272989X     EISSN: None     Source Type: Journal    
DOI: 10.1177/0272989X9801800309     Document Type: Article

References (43)

1. Jennet B. High Technology Medicine: Benefits and Burdens. Oxford, U.K.: Oxford University Press, 1986.
2. Almeida RT, Utilização de Tecnologias Neonatais [Utilization of Neonatal Technologies]. Master of science thesis, Biomedical Engineering Program/COPPE, Federal University of Rio de Janeiro: Rio de Janeiro, Brazil, 1988. [Abstract in English]
3. Scalon JD, Vidal Melo MF, Panerai RB. A method for predicting the utilization of medical technology in neonatal care. Technol Health Care. 1996;3:273-8.
4. Shuman LJ, Speas RD Jr, Young JP. Operations Research in Health Care. Baltimore, MD: Johns Hopkins University Press, 1975.
5. Fetter RF, Shin J, Freeman JL, Averill RF, Thompson JD. Case mix definition by diagnosis related groups. Med Care. 1980; 18(suppl. 2):1-52.
6. Cullen DJ, Civetta JM, Briggs BA, et al. Therapeutic intervention scoring system: a method for quantitative comparison of patient care. Crit Care Med. 1974;2:57-65.
7. Knauss WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification. Crit Care Med. 1985;13:818-28.
8. Le Gall JR, Loirat P, Alperovitch A, et al. A simplified acute physiologic score for ICU patients. Crit Care Med. 1984;12:973-83.
9. Lemcshow S, Teres D, Avrunin JS, Gage RW. Refining intensive care unit outcome prediction by using changing probabilities of mortality. Crit Care Med. 1988;16:470-7.
10. Pollack M, Ruttiman E, Getson PR. Accurate prediction of the outcome of paediatric intensive care: a new quantitative method. N Engl J Med. 1987;316:134-9.
11. Patterson CC, Halliday HL. Prediction of outcome shortly after delivery for the very low birthweight (≤1500 g) infant. Paediatr Perinat Epidemiol. 1988;2:221-8.
12. Georgieff MK, Mills MM, Bhatt P. Validation of two scoring systems which assess the degree of physiologic instability in critically ill newborn infants. Crit Care Med. 1989;17:17-21.
13. Tarnow-Mordi W, Ogston S, Wilkinson AR, et al. Predicting deatli from initial disease severity in very low birthweight infants: a method for comparing the performance of neonatal units. BMJ. 1990;300:1611-4.
14. Gray JE, Richardson DK, McCormick MC, Workman-Daniels K, Goldman DA. Neonatal therapeutic intervention scoring system: a therapy-based severity-of-illness index. Pediatrics. 1992;90:561-7.
15. Horbar JD, Onnsdad L, Wright E, et al. Predicting mortality risk for infants weighing 501 to 1500 grams at birth: a National Institutes of Health Neonatal Research Network report. Crit Care Med. 1993;21:12-8.
16. Richardson DK, Gray JA, McCormick MC, Workman-Daniels K, Goldman DA. Score for neonatal acute physiology: a physiologic severity index for neonatal intensive care. Pediatrics. 1993;91:617-23.
17. Richardson DK, Phibbs CS, Gray JE, McCormick MC, Workman-Daniels K, Goldman DA. Birthweight and illness severity: independent predictors of neonatal mortality. Pediatrics. 1993;91:969-75.
18. The International Neonatal Network. The CRIB (clinical risk index to babies) score: a tool for assessing initial neonatal risk and comparing performance of neonatal intensive care units. Lancet. 1993;342:193-8.
19. Rautonen J, Mäkelä A, Boyd H, Apajasalo M, Pohjavuori M. CRIB and SNAP: assessing the risk of death for preterm neonates. Lancet. 1994;343:1272-3.
20. Cox DR. A note on data-splitting for the evaluation of significance levels. Biometrics. 1975;62:441-4.
21. Miller AJ. Selection of subsets of regression variables. J Roy Statist Soc A. 1984:147:389-425.
22. Efron B. Bootstrap methods: another look at the jackknife. Ann Statist. 1979;7:1-26.
23. Draper NR, Smith H. Applied Regression Analysis. New York: John Wiley, 1981.
24. Bendel RB, Afifi AA. Comparison of stopping rules in forward stepwise regression. J Am Statist Assoc. 1977;72:46-53.
25. Efron B, Tibshirani RJ. An introduction to the Bootstrap, New York: Chapman and Hall, 1993.r
26. Leger C, Politis DN, Romano JP. Bootstrap technology and pplications. Technometrics. 1992;34:378-98.
27. Mammen E. When does bootstrap work?: asymptotic results and simulation. Lecture Notes Statist. 1992;77:1-196.
28. Swanepoel JWH. A review of bootstrap methods. Afr Statist J. 1990;24:1-34.
29. Manteiga WG, Sanchez JMP, Romo J. The bootstrap: a review. Comput Statist. 1994;9:165-205.
30. Almeida RT, Panerai RB, Carvalho M, Lopes JMA. Analysis of multiple technologies in neonatal care. Int J Technol Assess Health Care. 1991;7:22-9.
31. Altman DG, Andersen PK. Bootstrap investigation of the stability of a cox regression model. Stat Med. 1989;8:771-83.
32. Belsley DA, Kuh E, Welsh RE. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley, 1980.
33. Sauerbrei W, Schumacher W. A bootstrap resampling procedure for model building: application to the Cox regression model. Stat Med. 1992;11:2093-109.
34. Mick R, Ratain MJ. Bootstrap validation of pharmacodynamic models defined via stepwise linear regression. Clin Pharmacol Ther. 1994;56:217-22.
35. Freedman DA. Bootstrapping regression models. Ann Statist. 1981;9:1218-28.
36. Bickel PJ, Freedman DA. Bootstrapping regression models with many parameters. In: Bickel PJ, Doksun K, Hodges JL (eds). A Festschrift for Erich Lehman. Belmont, CA: Wadsworth, 1983:28-48.
37. Freedman DA, Peters SC. Bootstrapping a regression equation: some empirical results. J Am Statist Assoc. 1984;79:97-106.
38. Weber NC. On resampling techniques for regression models. Statist Probab Lett. 1984;2:257-8.
39. Stine RA. Bootstrap prediction intervals for regression. J Am Statist Assoc. 1985;80:1026-31.
40. Hall P. Unusual properties of bootstrap confidence intervals in regression problems. Probab Theory Related Fields. 1989; 76:613-7.
41. Breiman L. The little bootstrap and other methods for dimensionality selection in regression: X-fixed prediction error. J Am Statist Assoc. 1992;87:738-54.
42. Fisher G, Sim AB. Some finite sample theory for bootstrap regression estimates. J Statist Plann Inference. 1995;43:289-300.
43. Shao J. Bootstrap model selection. J Am Statist Assoc. 1996;91:655-65.