Regression analysis of incomplete medical cost data
The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process...
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| Published in | Statistics in medicine Vol. 22; no. 7; pp. 1181 - 1200 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
15.04.2003
Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0277-6715 1097-0258 |
| DOI | 10.1002/sim.1377 |
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| Abstract | The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special features of the medical cost data, especially the presence of death and censoring, pose major challenges in the construction of plausible statistical models and the development of the corresponding inference procedures. In this paper, we propose several classes of regression models which formulate the effects of possibly time‐dependent covariates on the marginal mean of cost accumulation in the presence of death or on the conditional means of cost accumulation given specific survival patterns. We then develop estimating equations for these models by combining the approach of generalized estimating equations for longitudinal data with the inverse probability of censoring weighting technique. The resultant estimators are shown to be consistent and asymptotically normal with simple variance estimators. Simulation studies indicate that the proposed inference procedures behave well in practical situations. An application to data taken from a large cancer study reveals that the Medicare enrollees who are diagnosed with less aggressive ovarian cancer tend to accumulate medical cost at lower rates than those with more aggressive disease, but tend to have higher lifetime costs because they live longer. Copyright © 2003 John Wiley & Sons, Ltd. |
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| AbstractList | The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special features of the medical cost data, especially the presence of death and censoring, pose major challenges in the construction of plausible statistical models and the development of the corresponding inference procedures. In this paper, we propose several classes of regression models which formulate the effects of possibly time-dependent covariates on the marginal mean of cost accumulation in the presence of death or on the conditional means of cost accumulation given specific survival patterns. We then develop estimating equations for these models by combining the approach of generalized estimating equations for longitudinal data with the inverse probability of censoring weighting technique. The resultant estimators are shown to be consistent and asymptotically normal with simple variance estimators. Simulation studies indicate that the proposed inference procedures behave well in practical situations. An application to data taken from a large cancer study reveals that the Medicare enrollees who are diagnosed with less aggressive ovarian cancer tend to accumulate medical cost at lower rates than those with more aggressive disease, but tend to have higher lifetime costs because they live longer.The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special features of the medical cost data, especially the presence of death and censoring, pose major challenges in the construction of plausible statistical models and the development of the corresponding inference procedures. In this paper, we propose several classes of regression models which formulate the effects of possibly time-dependent covariates on the marginal mean of cost accumulation in the presence of death or on the conditional means of cost accumulation given specific survival patterns. We then develop estimating equations for these models by combining the approach of generalized estimating equations for longitudinal data with the inverse probability of censoring weighting technique. The resultant estimators are shown to be consistent and asymptotically normal with simple variance estimators. Simulation studies indicate that the proposed inference procedures behave well in practical situations. An application to data taken from a large cancer study reveals that the Medicare enrollees who are diagnosed with less aggressive ovarian cancer tend to accumulate medical cost at lower rates than those with more aggressive disease, but tend to have higher lifetime costs because they live longer. The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special features of the medical cost data, especially the presence of death and censoring, pose major challenges in the construction of plausible statistical models and the development of the corresponding inference procedures. In this paper, we propose several classes of regression models which formulate the effects of possibly time-dependent covariates on the marginal mean of cost accumulation in the presence of death or on the conditional means of cost accumulation given specific survival patterns. We then develop estimating equations for these models by combining the approach of generalized estimating equations for longitudinal data with the inverse probability of censoring weighting technique. The resultant estimators are shown to be consistent and asymptotically normal with simple variance estimators. Simulation studies indicate that the proposed inference procedures behave well in practical situations. An application to data taken from a large cancer study reveals that the Medicare enrollees who are diagnosed with less aggressive ovarian cancer tend to accumulate medical cost at lower rates than those with more aggressive disease, but tend to have higher lifetime costs because they live longer. The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special features of the medical cost data, especially the presence of death and censoring, pose major challenges in the construction of plausible statistical models and the development of the corresponding inference procedures. In this paper, we propose several classes of regression models which formulate the effects of possibly time‐dependent covariates on the marginal mean of cost accumulation in the presence of death or on the conditional means of cost accumulation given specific survival patterns. We then develop estimating equations for these models by combining the approach of generalized estimating equations for longitudinal data with the inverse probability of censoring weighting technique. The resultant estimators are shown to be consistent and asymptotically normal with simple variance estimators. Simulation studies indicate that the proposed inference procedures behave well in practical situations. An application to data taken from a large cancer study reveals that the Medicare enrollees who are diagnosed with less aggressive ovarian cancer tend to accumulate medical cost at lower rates than those with more aggressive disease, but tend to have higher lifetime costs because they live longer. Copyright © 2003 John Wiley & Sons, Ltd. |
| Author | Lin, D. Y. |
| Author_xml | – sequence: 1 givenname: D. Y. surname: Lin fullname: Lin, D. Y. email: lin@bios.unc.edu organization: Department of Biostatistics, University of North Carolina, CB#7420 McGavran-Greenberg, Chapel Hill, NC 27599-7420, U.S.A |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14631384$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/12652561$$D View this record in MEDLINE/PubMed |
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| References | Lin DY. Proportional means regression for censored medical costs. Biometrics 2000; 56:775-778. O'Hagan A, Stevens JW, Montmartin J. Baysian cost-effectiveness analysis from clinical trial data. Statistics in Medicine 2001; 20:733-753. McCullagh P, Nelder JA. Generalized Linear Models. 2nd edn. Chapman & Hall: New York, 1989. Lin DY. Linear regression of censored medical costs. Biostatistics 2000; 1:35-47. Breslow NE. Contribution to the discussion of the paper by D. R. Cox. Journal of the Royal Statistical Society, Series B 1972; 34:216-217. Little RJA. Modelling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 1995; 90:1112-1121. Etzioni RD, Urban N, Baker M. Estimating the costs attributable to a disease with application to ovarian cancer. Journal of Clinical Epidemiology 1996; 49:95-103. Lin DY, Fleming TR, Wei LJ. Confidence bands for survival curves under the proportional hazards model. Biometrika 1994; 81:73-81. Lin DY, Etzioni R, Feuer EJ, Wax Y. Estimating medical costs from incomplete follow-up data. Biometrics 1997; 53:419-434. Zhao H, Tsiatis AA. A consistent estimator for the distribution of quality-adjusted survival time. Biometrika 1997; 84:339-348. Briggs AH, Wonderling DE, Mooney CZ. Pulling cost-effectiveness analysis up by its bootstraps; a nonparametric approach to confidence interval estimation. Health Economics 1997; 6:327-340. Willan A. Analysis, sample size and power for estimating incremental net health benefit from clinical trial data. Controlled Clinical Trials 2001; 22:228-237. Therneau TM, Grambsch PM. Modelling Survival Data: Extending the Cox Model. Springer: New York, 2000. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22. Lin DY, Ying Z. A simple nonparametric estimator of the bivariate survival function under univariate censoring. Biometrika 1993; 80:573-581. Koul H, Susarla V, van Ryzin J. Regression analysis with randomly right-censored data. Annals of Statistics 1981; 9:1276-1288. Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry data base. Medical Care 1993; 31:732-747. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association 1952; 47:663-685. Lin DY, Wei LJ, Ying Z. Model-checking techniques based on cumulative residuals. Biometrics 2002; 58:1-12. Willan A, O'Brien BJ. Confidence intervals for cost-effectiveness ratios: an application of Fieller's theorem. Health Economics 1996; 5:297-305. Cox DR. Regression models and life-tables (with discussion). Journal of the Royal Statistical Society, Series B 1972; 34:187-220. Cook RJ, Lawless JF. Marginal analysis of recurrent events and a terminating event. Statistics in Medicine 1997; 16:911-924. Bang H, Tsiatis AA. Estimating medical costs with censored data. Biometrika 2000; 87:329-343. 2002; 58 1952; 47 1997; 84 1995; 90 1986; 73 2000 1997; 53 2000; 56 2000; 87 1993; 31 1981; 9 1997; 16 1993; 80 2000; 1 1992 2001; 22 1994; 81 1996; 5 1997; 6 1972; 34 1996; 49 1989 2001; 20 e_1_2_1_6_2 e_1_2_1_7_2 e_1_2_1_4_2 e_1_2_1_2_2 e_1_2_1_11_2 e_1_2_1_22_2 e_1_2_1_3_2 e_1_2_1_12_2 e_1_2_1_23_2 e_1_2_1_20_2 e_1_2_1_10_2 e_1_2_1_21_2 Breslow NE (e_1_2_1_14_2) 1972; 34 e_1_2_1_15_2 e_1_2_1_16_2 e_1_2_1_13_2 e_1_2_1_24_2 Cox DR (e_1_2_1_5_2) 1972; 34 e_1_2_1_25_2 e_1_2_1_19_2 e_1_2_1_8_2 e_1_2_1_17_2 e_1_2_1_9_2 e_1_2_1_18_2 |
| References_xml | – reference: Little RJA. Modelling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 1995; 90:1112-1121. – reference: Cox DR. Regression models and life-tables (with discussion). Journal of the Royal Statistical Society, Series B 1972; 34:187-220. – reference: Lin DY, Wei LJ, Ying Z. Model-checking techniques based on cumulative residuals. Biometrics 2002; 58:1-12. – reference: Therneau TM, Grambsch PM. Modelling Survival Data: Extending the Cox Model. Springer: New York, 2000. – reference: Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22. – reference: Bang H, Tsiatis AA. Estimating medical costs with censored data. Biometrika 2000; 87:329-343. – reference: Lin DY. Linear regression of censored medical costs. Biostatistics 2000; 1:35-47. – reference: Zhao H, Tsiatis AA. A consistent estimator for the distribution of quality-adjusted survival time. Biometrika 1997; 84:339-348. – reference: Etzioni RD, Urban N, Baker M. Estimating the costs attributable to a disease with application to ovarian cancer. Journal of Clinical Epidemiology 1996; 49:95-103. – reference: McCullagh P, Nelder JA. Generalized Linear Models. 2nd edn. Chapman & Hall: New York, 1989. – reference: Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry data base. Medical Care 1993; 31:732-747. – reference: Briggs AH, Wonderling DE, Mooney CZ. Pulling cost-effectiveness analysis up by its bootstraps; a nonparametric approach to confidence interval estimation. Health Economics 1997; 6:327-340. – reference: Lin DY, Ying Z. A simple nonparametric estimator of the bivariate survival function under univariate censoring. Biometrika 1993; 80:573-581. – reference: Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. 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| SubjectTerms | Aged Biological and medical sciences censoring economic evaluation generalized estimating equations health care Health Care Costs Humans inverse probability of censoring weighting Longitudinal Studies Medical sciences Models, Economic Models, Statistical pattern-mixture models Regression Analysis Stochastic Processes |
| Title | Regression analysis of incomplete medical cost data |
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