The use of probabilistic decision models in technology assessment
Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Briggs, Andrew [verfasserIn] Sculpher, Mark [verfasserIn] Dawson, Jill [verfasserIn] Fitzpatrick, Ray [verfasserIn] Murray, David [verfasserIn] Malchau, Henrik [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2004 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Applied health economics and health policy - [S.l.] : Springer International, 2004, 3(2004), 2 vom: Juni, Seite 79-89 |
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| Übergeordnetes Werk: |
volume:3 ; year:2004 ; number:2 ; month:06 ; pages:79-89 |
| Links: |
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| DOI / URN: |
10.2165/00148365-200403020-00004 |
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SPR032968183 |
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| 520 | |a Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. | ||
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| 700 | 1 | |a Sculpher, Mark |e verfasserin |4 aut | |
| 700 | 1 | |a Dawson, Jill |e verfasserin |4 aut | |
| 700 | 1 | |a Fitzpatrick, Ray |e verfasserin |4 aut | |
| 700 | 1 | |a Murray, David |e verfasserin |4 aut | |
| 700 | 1 | |a Malchau, Henrik |e verfasserin |4 aut | |
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10.2165/00148365-200403020-00004 doi (DE-627)SPR032968183 (SPR)00148365-200403020-00004-e DE-627 ger DE-627 rakwb eng 610 ASE Briggs, Andrew verfasserin aut The use of probabilistic decision models in technology assessment 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. Revision Rate (dpeaa)DE-He213 Probabilistic Sensitivity Analysis (dpeaa)DE-He213 Revision Procedure (dpeaa)DE-He213 Late Failure (dpeaa)DE-He213 Revision Risk (dpeaa)DE-He213 Sculpher, Mark verfasserin aut Dawson, Jill verfasserin aut Fitzpatrick, Ray verfasserin aut Murray, David verfasserin aut Malchau, Henrik verfasserin aut Enthalten in Applied health economics and health policy [S.l.] : Springer International, 2004 3(2004), 2 vom: Juni, Seite 79-89 (DE-627)481906118 (DE-600)2180637-8 1179-1896 nnns volume:3 year:2004 number:2 month:06 pages:79-89 https://dx.doi.org/10.2165/00148365-200403020-00004 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2004 2 06 79-89 |
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10.2165/00148365-200403020-00004 doi (DE-627)SPR032968183 (SPR)00148365-200403020-00004-e DE-627 ger DE-627 rakwb eng 610 ASE Briggs, Andrew verfasserin aut The use of probabilistic decision models in technology assessment 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. Revision Rate (dpeaa)DE-He213 Probabilistic Sensitivity Analysis (dpeaa)DE-He213 Revision Procedure (dpeaa)DE-He213 Late Failure (dpeaa)DE-He213 Revision Risk (dpeaa)DE-He213 Sculpher, Mark verfasserin aut Dawson, Jill verfasserin aut Fitzpatrick, Ray verfasserin aut Murray, David verfasserin aut Malchau, Henrik verfasserin aut Enthalten in Applied health economics and health policy [S.l.] : Springer International, 2004 3(2004), 2 vom: Juni, Seite 79-89 (DE-627)481906118 (DE-600)2180637-8 1179-1896 nnns volume:3 year:2004 number:2 month:06 pages:79-89 https://dx.doi.org/10.2165/00148365-200403020-00004 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2004 2 06 79-89 |
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10.2165/00148365-200403020-00004 doi (DE-627)SPR032968183 (SPR)00148365-200403020-00004-e DE-627 ger DE-627 rakwb eng 610 ASE Briggs, Andrew verfasserin aut The use of probabilistic decision models in technology assessment 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. Revision Rate (dpeaa)DE-He213 Probabilistic Sensitivity Analysis (dpeaa)DE-He213 Revision Procedure (dpeaa)DE-He213 Late Failure (dpeaa)DE-He213 Revision Risk (dpeaa)DE-He213 Sculpher, Mark verfasserin aut Dawson, Jill verfasserin aut Fitzpatrick, Ray verfasserin aut Murray, David verfasserin aut Malchau, Henrik verfasserin aut Enthalten in Applied health economics and health policy [S.l.] : Springer International, 2004 3(2004), 2 vom: Juni, Seite 79-89 (DE-627)481906118 (DE-600)2180637-8 1179-1896 nnns volume:3 year:2004 number:2 month:06 pages:79-89 https://dx.doi.org/10.2165/00148365-200403020-00004 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2004 2 06 79-89 |
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10.2165/00148365-200403020-00004 doi (DE-627)SPR032968183 (SPR)00148365-200403020-00004-e DE-627 ger DE-627 rakwb eng 610 ASE Briggs, Andrew verfasserin aut The use of probabilistic decision models in technology assessment 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. Revision Rate (dpeaa)DE-He213 Probabilistic Sensitivity Analysis (dpeaa)DE-He213 Revision Procedure (dpeaa)DE-He213 Late Failure (dpeaa)DE-He213 Revision Risk (dpeaa)DE-He213 Sculpher, Mark verfasserin aut Dawson, Jill verfasserin aut Fitzpatrick, Ray verfasserin aut Murray, David verfasserin aut Malchau, Henrik verfasserin aut Enthalten in Applied health economics and health policy [S.l.] : Springer International, 2004 3(2004), 2 vom: Juni, Seite 79-89 (DE-627)481906118 (DE-600)2180637-8 1179-1896 nnns volume:3 year:2004 number:2 month:06 pages:79-89 https://dx.doi.org/10.2165/00148365-200403020-00004 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2004 2 06 79-89 |
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10.2165/00148365-200403020-00004 doi (DE-627)SPR032968183 (SPR)00148365-200403020-00004-e DE-627 ger DE-627 rakwb eng 610 ASE Briggs, Andrew verfasserin aut The use of probabilistic decision models in technology assessment 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. Revision Rate (dpeaa)DE-He213 Probabilistic Sensitivity Analysis (dpeaa)DE-He213 Revision Procedure (dpeaa)DE-He213 Late Failure (dpeaa)DE-He213 Revision Risk (dpeaa)DE-He213 Sculpher, Mark verfasserin aut Dawson, Jill verfasserin aut Fitzpatrick, Ray verfasserin aut Murray, David verfasserin aut Malchau, Henrik verfasserin aut Enthalten in Applied health economics and health policy [S.l.] : Springer International, 2004 3(2004), 2 vom: Juni, Seite 79-89 (DE-627)481906118 (DE-600)2180637-8 1179-1896 nnns volume:3 year:2004 number:2 month:06 pages:79-89 https://dx.doi.org/10.2165/00148365-200403020-00004 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2004 2 06 79-89 |
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Enthalten in Applied health economics and health policy 3(2004), 2 vom: Juni, Seite 79-89 volume:3 year:2004 number:2 month:06 pages:79-89 |
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Enthalten in Applied health economics and health policy 3(2004), 2 vom: Juni, Seite 79-89 volume:3 year:2004 number:2 month:06 pages:79-89 |
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In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Revision Rate</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Probabilistic Sensitivity Analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Revision Procedure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Late Failure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Revision Risk</subfield><subfield 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Briggs, Andrew |
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The use of probabilistic decision models in technology assessment |
| abstract |
Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. |
| abstractGer |
Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. |
| abstract_unstemmed |
Abstract There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery. A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992–2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between £1000 and £16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to £20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient. This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity. |
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| title_short |
The use of probabilistic decision models in technology assessment |
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Sculpher, Mark Dawson, Jill Fitzpatrick, Ray Murray, David Malchau, Henrik |
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10.2165/00148365-200403020-00004 |
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2024-07-03T15:46:16.343Z |
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| score |
7.40018 |