A Flexible Alternative to the Cox Proportional Hazards Model for Assessing the Prognostic Accuracy of Hospice Patient Survival

PLoS ONE

Volumn 7, Issue 10, 2012, Pages

  Miladinovic, Branko ^a   Kumar, Ambuj ^a   Mhaskar, Rahul ^a   Kim, Sehwan ^b   Schonwetter, Ronald ^b   Djulbegovic, Benjamin ^a,c  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

^b HPC Healthcare   (United States)

^c H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; CONTROLLED STUDY; FEMALE; HEALTH CARE QUALITY; HOSPICE CARE; HOSPITAL ADMISSION; HUMAN; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PALLIATIVE THERAPY; PATIENT CARE; PROBABILITY; PROPORTIONAL HAZARDS MODEL; RETROSPECTIVE STUDY; SURVIVAL RATE; VALIDATION PROCESS;

AGED; AGED, 80 AND OVER; FEMALE; FLORIDA; HOSPICES; HUMANS; KAPLAN-MEIER ESTIMATE; MALE; MIDDLE AGED; PALLIATIVE CARE; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; TIME FACTORS;

EID: 84867660682     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0047804     Document Type: Article

References (41)

1. Royston P, Moons KG, Altman DG, Vergouwe Y, (2009) Prognosis and prognostic research: Developing a prognostic model. BMJ 338: b604.
2. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, et al. (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21: 128-138.
3. Vickers AJ, (2011) Prediction models: revolutionary in principle, but do they do more good than harm? J Clin Oncol 29: 2951-2952.
4. Moons KG, Altman DG, Vergouwe Y, Royston P, (2009) Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 338: b606.
5. Steinhauser KE, Christakis NA, Clipp EC, McNeilly M, McIntyre L, et al. (2000) Factors considered important at the end of life by patients, family, physicians, and other care providers. JAMA 284: 2476-2482.
6. Christakis NA, Lamont EB, (2000) Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study. BMJ 320: 469-472.
7. Chow E, Harth T, Hruby G, Finkelstein J, Wu J, et al. (2001) How accurate are physicians' clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review. Clin Oncol (R Coll Radiol) 13: 209-218.
8. Cox DR, Oakes D (1984) Analysis of survival data. London;New York: Chapman and Hall. viii, 201 p.p.
9. Mallett S, Royston P, Waters R, Dutton S, Altman DG, (2010) Reporting performance of prognostic models in cancer: a review. BMC Med 8: 21.
10. Downing M, Lau F, Lesperance M, Karlson N, Shaw J, et al. (2007) Meta-analysis of survival prediction with Palliative Performance Scale. J Palliat Care 23: 245-252; discussion 252-244.
11. Lau F, Cloutier-Fisher D, Kuziemsky C, Black F, Downing M, et al. (2007) A systematic review of prognostic tools for estimating survival time in palliative care. J Palliat Care 23: 93-112.
12. Royston P, Parmar MK, (2002) Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med 21: 2175-2197.
13. Anderson F, Downing GM, Hill J, Casorso L, Lerch N, (1996) Palliative performance scale (PPS): a new tool. J Palliat Care 12: 5-11.
14. Lau F, Downing M, Lesperance M, Karlson N, Kuziemsky C, et al. (2009) Using the Palliative Performance Scale to provide meaningful survival estimates. J Pain Symptom Manage 38: 134-144.
15. Harrold J, Rickerson E, Carroll JT, McGrath J, Morales K, et al. (2005) Is the palliative performance scale a useful predictor of mortality in a heterogeneous hospice population? J Palliat Med 8: 503-509.
16. Olajide O, Hanson L, Usher BM, Qaqish BF, Schwartz R, et al. (2007) Validation of the palliative performance scale in the acute tertiary care hospital setting. J Palliat Med 10: 111-117.
17. Head B, Ritchie CS, Smoot TM, (2005) Prognostication in hospice care: can the palliative performance scale help? J Palliat Med 8: 492-502.
18. Fainsinger RL, Demoissac D, Cole J, Mead-Wood K, Lee E, (2000) Home versus hospice inpatient care: discharge characteristics of palliative care patients in an acute care hospital. J Palliat Care 16: 29-34.
19. Morita T, Tsunoda J, Inoue S, Chihara S, (2001) Effects of high dose opioids and sedatives on survival in terminally ill cancer patients. J Pain Symptom Manage 21: 282-289.
20. Virik K, Glare P, (2002) Validation of the palliative performance scale for inpatients admitted to a palliative care unit in Sydney, Australia. J Pain Symptom Manage 23: 455-457.
21. Morita T, Tsunoda J, Inoue S, Chihara S, (1999) Validity of the palliative performance scale from a survival perspective. J Pain Symptom Manage 18: 2-3.
22. Lau F, Bell H, Dean M, Downing M, Lesperance M, (2008) Use of the Palliative Performance Scale in survival prediction for terminally ill patients in Western Newfoundland, Canada. J Palliat Care 24: 282-284.
23. Lau F, Maida V, Downing M, Lesperance M, Karlson N, et al. (2009) Use of the Palliative Performance Scale (PPS) for end-of-life prognostication in a palliative medicine consultation service. J Pain Symptom Manage 37: 965-972.
24. Ho F, Lau F, Downing MG, Lesperance M, (2008) A reliability and validity study of the Palliative Performance Scale. BMC Palliat Care 7: 10.
25. Altman DG, Royston P, (2000) What do we mean by validating a prognostic model? Stat Med 19: 453-473.
26. Altman DG, Vergouwe Y, Royston P, Moons KG, (2009) Prognosis and prognostic research: validating a prognostic model. BMJ 338: b605.
27. Kalbfleisch JD, Prentice RL (2002)The statistical analysis of failure time data. Hoboken, N.J.: J. Wiley. xiii, 439 p.p.
28. Binquet C, Abrahamowicz M, Mahboubi A, Jooste V, Faivre J, et al. (2008) Empirical study of the dependence of the results of multivariable flexible survival analyses on model selection strategy. Stat Med 27: 6470-6488.
29. Royston P, (2001) Flexible parametric alternatives to the Cox model, and more. Stata Journal 1: 1-28.
30. Schwarz G, (1978) Estimating Dimension of a Model. Annals of Statistics 6: 461-464.
31. Durrleman S, Simon R, (1989) Flexible regression models with cubic splines. Stat Med 8: 551-561.
32. Stata Version 11 [computer program]. 9 ed. College Station, TX: Stata Corporation; 2010.
33. Royston P (2011) Flexible parametric survival analysis using stata: beyond the Cox model. College Station, TX: Stata Press.
34. Lambert PC, Royston P, (2009) Further development of flexible parametric models for survival analysis. The Stata Journal 9: 265-290.
35. Jackson C (2012) Flexible parametric survival models.
36. Cook NR, Buring JE, Ridker PM, (2006) The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med 145: 21-29.
37. Graf E, Schmoor C, Sauerbrei W, Schumacher M, (1999) Assessment and comparison of prognostic classification schemes for survival data. Stat Med 18: 2529-2545.
38. Hu B, Palta M, Shao J, (2006) Properties of R(2) statistics for logistic regression. Stat Med 25: 1383-1395.
39. Altman DG, (2009) Prognostic models: a methodological framework and review of models for breast cancer. Cancer Invest 27: 235-243.
40. Royston P, (2006) Explained variation for survival models. The Stata Journal 6: 1-14.
41. Royston P, Sauerbrei W, (2004) A new measure of prognostic separation in survival data. Stat Med 23: 723-748.