Abstract
Payers increasingly require evidence of a statistically significant difference in overall survival (OS) for reimbursement of new cancer therapies. At the same time, it becomes increasingly costly to design clinical trials that measure OS endpoints instead of progression-free survival (PFS) endpoints. While PFS is often an imperfect proxy for OS effects, it is also faster and cheaper to measure accurately. This study develops a general cost-benefit framework that quantifies the competing trade-offs of the use of PFS versus that of OS in oncology reimbursement. We then apply this general framework to the illustrative case of metastatic renal cell carcinoma (mRCC). In the particular case of mRCC, the framework demonstrates that the net benefit to society from basing reimbursement decisions on PFS endpoints could be between $271 and $1271 million in the United States, or between €171 and €1128 million in Europe. In longevity terms, waiting for OS data in this case would result in a net loss of 3549–14,557 life-years among US patients, or 6785–27,993 life-years for European patients. While more stringent standards for medical evidence improve accuracy, they also impose countervailing costs on patients in terms of foregone health gains. These costs must be weighed against the benefits of greater accuracy. The magnitudes of the costs and benefits may vary across tumor types and need to be quantified systematically.
- 1
For simplicity, we assume that the median OS improvement recorded in the trial is exactly equal to the benefit enjoyed by patients who use the drug. It is possible to relax this assumption, assuming we have patient-level data on OS and PFS that can be used to estimate the resulting costs.
- 2
Mathematically, this result follows by computing the difference between the net benefit of using PFS and the net benefit of using OS. The resulting difference can be simplified using two facts: Pr(BOS>0, BPFS≤0)+Pr(BOS>0, BPFS>0)=Pr(BOS>0); and Pr(BOS>0, BPFS≤0)E(BOS∣BPFS≤0, BOS>0)+Pr(BOS>0, BPFS>0)E(BOS∣BOS>0, BPFS≤0)=Pr(BOS>0)E(BOS∣BOS>0).
Appendix
Technical Appendix: Method of estimating the likely delay in waiting for a statistically significant result based on trial data and a statistically significant PFS result.
The alternative method of estimating delay in the availability of evidence based on OS endpoints simulates the additional time it would take to generate OS data, based on estimates of the following inputs: (I-1) the hazard rate into mortality for patients on the drug; (I-2) the hazard rate into mortality from patients in the control arm of a trial; and (I-3) the rate at which patients could be recruited into a trial designed to measure overall survival. The first two parameters determine how many patients would need to be recruited in order to identify enough deaths to adequately power a study of overall survival, and the third determines how long it would take to recruit this many patients.
US (SEER) | EU* (IARC) | ||
---|---|---|---|
Registry estimate of incidence of RCC1 | 11,654 | 15,912 | |
Population incidence of RCC2 | 41,621 | 79,560 | |
Proportion of new cases mRCC3 | 19% | 7908 | 15,116 |
Proportion of current RCC progressing to mRCC4 | 40% | 13,485 | 25,777 |
Total number of cases of mRCC per year | 21,393 | 40,894 | |
First line treatment eligible patients5 | 74% | 15,831 | 30,261 |
Second line treatment eligible patients5 | 52% | 8232 | 15,736 |
*EU (27 countries; population 505 million). http://seer.cancer.gov/data/ (National Cancer Institute 2013); http://globocan.iarc.fr/ (Ferlay et al. 2013).
1Histological subtypes ICD-O-3 (8260, 8310, 8312, 8316-20, 8510, 8959).
2Inflated by 1 over registry representation (SEER=28%, IARC=15%).
3Proportion of incident cases that are metastatic SEER (Gupta et al. 2008).
4KOL input.
5From the DUKE ACORN Study.
Publication date (PFS) | Publication date (OS) | Cumulative mean net delay | Author, year (PFS) | Author, year (PFS) | |
---|---|---|---|---|---|
IL2+IFN+CRA | Apr-04 | Aug-06 | 28 | Atzipodian 2004 | Atzipodian 2006 |
Sorafenib | Jan-07 | Jul-09 | 30 | Escudier 2007 | Escudier 2009 |
Sunitinib | Jan-07 | Aug-09 | 30 | Motzer 2007 | Motzer 2009 |
Bevacizumab | Dec-07 | May-10 | 30 | Escudier 2007 | Escudier 2010 |
Everolimus | Aug-08 | Sep-10 | 29 | Motzer 2008 | Motzer 2010 |
Axitinib | Dec-11 | May-13 | 27 | Rini 2011 | Motzer 2013 |
Model parameters | Data from studies up to 2006 (n=22) | Data from studies up to 2011 (n=41) |
---|---|---|
Positive PFS, positive OS | 64% | 61% |
Negative PFS, positive OS | 21% | 11% |
Negative PFS, negative OS | 6% | 10% |
Positive PFS, negative OS | 9% | 18% |
Conditional probabilities: | ||
Positive OS, when positive PFS | 88% | 77% |
Negative OS when positive PFS | 12% | 23% |
Mean effect on OS, when OS is positive | 5.58 months | 4.83 months |
Mean effect on OS when OS is negative | (1.4) months | (6.93) months |
Of course, these three parameters are fundamentally unknown in a scenario where the OS trial has not yet been conducted. Therefore, we make the following assumptions: (A-1) the hazard rate into disease-progression for patients treated in the observed PFS trial is equal to the hazard rate into mortality for patients that would be treated in a hypothetical OS trial; (A-2) the recruitment rate of a hypothetical OS trial would be the same as the corresponding rate for the existing PFS trial; and (A-3) the anticipated overall survival rate in the control arm of the PFS trial is the same as this rate in a hypothetical OS trial. While these assumptions may not be bulletproof, they are made necessary in cases where data on previously published OS trials are unavailable.
A: United States | ||||
---|---|---|---|---|
Incremental cost estimates | Median months1 | Cost per month (US$)2,3 | Median drug cost (US$) | |
Sunitinib | 11.0 | 3192 | 35,112 | |
Interferon alpha | 5.0 | 2184 | 10,920 | |
Sorafenib | 4.7 | 4471 | 21,014 | |
Axitinib | 6.7 | 5101 | 34,177 | |
Sunitinib versus Interferon alpha | 24,192 | |||
Axitinib versus sorafenib | 13,163 | |||
B: Europe | ||||
Incremental cost estimates | Median months1 | Cost per month (GBP)2,4 | Drug cost (GBP) | (Euro) |
Sunitinib | 11.0 | 2097 | 23,068 | |
Interferon alpha | 5.0 | 1435 | 7174 | |
Sorafenib | 4.7 | 2980 | 14,006 | |
Axitinib | 6.7 | 3517 | 23,564 | |
Sunitinib versus Interferon alpha | 15,893 | 17,721 | ||
Axitinib versus sorafenib | 9558 | 10,657 |
1Median months per patient taken from trials (I, ii) – IRC.
2Dosage by drug [sunitinib: oral daily 4 weeks on, 2 weeks off; interferon alpha subcutaneously 3 times weekly; sorafenib: oral twice daily; axitinib: oral twice daily].
3US cost data taken from Red Book 2011.
4UK cost data from Monthly Index of Medical Specialties (MIMS) Prescribing Guide 2011.
Using inputs (I-1) and (I-2), we can perform a standard power calculation to determine the number of patients that would have to be recruited into an OS study in order to observe the requisite number of deaths to achieve statistical significance. In the case of the sunitinib trial, control patients received IFN-alpha, with a median PFS of 11 months and 5 months, for the treatment and control groups, respectively, and a resulting mortality hazard ratio of 0.54. Based on these event rates, a log rank test with alpha (two-sided) of 0.05 and power of 0.80 implies the number of events required for a statistically significant OS difference across arms is 435. Using the same method, the number of events to achieve the statistically significant PFS difference was 139. Using (I-3), we then estimate the length of time it takes to achieve a sample of this size. Finally, this yields the estimated length of time required for an OS study.
Appendix Table 5 shows that significance would be achieved for PFS (Table 5A) by May 2005 (i.e. 2005m5), and for OS (Table 5B) by November 2007 (i.e. 2007m11). This implies a net delay of 30 months.
Month | #pats | #C-events | #events | Power | |
---|---|---|---|---|---|
1 | 2004m8 | 7 | 1 | 1 | 0.03751 |
2 | 2004m9 | 16 | 1 | 2 | 0.05552 |
3 | 2004m10 | 32 | 3 | 4 | 0.08436 |
4 | 2004m11 | 72 | 6 | 9 | 0.14123 |
5 | 2004m12 | 124 | 12 | 18 | 0.24387 |
6 | 2005m1 | 173 | 20 | 31 | 0.38755 |
7 | 2005m2 | 253 | 31 | 49 | 0.562 |
8 | 2005m3 | 334 | 46 | 74 | 0.73664 |
9 | 2005m4 | 411 | 65 | 104 | 0.86818 |
10 | 2005m5 | 518 | 86 | 139 | 0.94709 |
11 | 2005m6 | 639 | 113 | 183 | 0.98396 |
The bolded row indicates the point at which a statistically significant result is achieved (Power is ≥0.95).
Month | #pats | #C-events | #events | Power | |
---|---|---|---|---|---|
1 | 2004m8 | 7 | 1 | 1 | 0.02754 |
2 | 2004m9 | 16 | 1 | 1 | 0.03047 |
3 | 2004m10 | 32 | 1 | 2 | 0.03424 |
4 | 2004m11 | 72 | 2 | 3 | 0.04012 |
5 | 2004m12 | 124 | 3 | 6 | 0.04875 |
6 | 2005m1 | 173 | 6 | 10 | 0.0598 |
7 | 2005m2 | 253 | 9 | 15 | 0.07406 |
8 | 2005m3 | 334 | 13 | 23 | 0.09245 |
9 | 2005m4 | 411 | 19 | 33 | 0.11484 |
10 | 2005m5 | 518 | 25 | 45 | 0.14208 |
11 | 2005m6 | 639 | 34 | 59 | 0.17548 |
12 | 2005m7 | 708 | 43 | 76 | 0.21367 |
13 | 2005m8 | 732 | 53 | 94 | 0.25335 |
14 | 2005m9 | 745 | 63 | 112 | 0.29256 |
15 | 2005m10 | 750 | 73 | 129 | 0.33055 |
16 | 2005m11 | 750 | 82 | 147 | 0.36683 |
17 | 2005m12 | 750 | 91 | 163 | 0.40122 |
18 | 2006m1 | 750 | 100 | 179 | 0.43372 |
19 | 2006m2 | 750 | 109 | 195 | 0.46436 |
20 | 2006m3 | 750 | 117 | 210 | 0.49319 |
21 | 2006m4 | 750 | 125 | 225 | 0.52027 |
22 | 2006m5 | 750 | 133 | 240 | 0.54567 |
23 | 2006m6 | 750 | 141 | 254 | 0.56949 |
24 | 2006m7 | 750 | 148 | 267 | 0.59179 |
25 | 2006m8 | 750 | 155 | 281 | 0.61267 |
26 | 2006m9 | 750 | 162 | 293 | 0.63221 |
27 | 2006m10 | 750 | 169 | 306 | 0.65049 |
28 | 2006m11 | 750 | 175 | 318 | 0.66759 |
29 | 2006m12 | 750 | 181 | 330 | 0.6836 |
30 | 2007m1 | 750 | 188 | 341 | 0.69857 |
31 | 2007m2 | 750 | 193 | 353 | 0.71258 |
32 | 2007m3 | 750 | 199 | 364 | 0.7257 |
33 | 2007m4 | 750 | 205 | 374 | 0.73799 |
34 | 2007m5 | 750 | 210 | 384 | 0.7495 |
35 | 2007m6 | 750 | 215 | 394 | 0.76029 |
36 | 2007m7 | 750 | 220 | 404 | 0.77041 |
37 | 2007m8 | 750 | 225 | 414 | 0.7799 |
38 | 2007m9 | 750 | 230 | 423 | 0.78881 |
39 | 2007m10 | 750 | 234 | 432 | 0.79718 |
40 | 2007m11 | 750 | 239 | 440 | 0.80504 |
41 | 2007m12 | 750 | 243 | 449 | 0.81244 |
For the axitinib trial, control patients received sorafenib, with a median PFS of 6.7 months and 4.7 months, for the treatment and control groups respectively, and a resulting Hazard Ratio of 0.67. A log rank test with alpha (two-sided) of 0.05 and power of 0.80 implies the number of events required for a statistically significant OS difference across arms is 418. The number of events to achieve the statistically significant PFS difference was 266. Using (I-3), we then estimate the length of time it takes to achieve a sample of this size. Finally, this yields the estimated length of time required for an OS study.
Appendix Table 6 shows that significance would be achieved for PFS (Table 6A) by January 2010 (i.e. 2010m1), and for OS (Table 6B) by December 2011 (i.e. 2011m12). This yields a net delay of 23 months.
Month | #pats | #C-events | #events | Power | |
---|---|---|---|---|---|
1 | 2008m9 | 3 | 1 | 1 | 0.03028 |
2 | 2008m10 | 7 | 1 | 1 | 0.03676 |
3 | 2008m11 | 17 | 2 | 3 | 0.04687 |
4 | 2008m12 | 27 | 3 | 5 | 0.06111 |
5 | 2009m1 | 44 | 5 | 8 | 0.08084 |
6 | 2009m2 | 78 | 9 | 15 | 0.11244 |
7 | 2009m3 | 128 | 15 | 25 | 0.16395 |
8 | 2009m4 | 169 | 23 | 39 | 0.23486 |
9 | 2009m5 | 209 | 33 | 56 | 0.31877 |
10 | 2009m6 | 243 | 44 | 76 | 0.40906 |
11 | 2009m7 | 300 | 57 | 98 | 0.50479 |
12 | 2009m8 | 351 | 71 | 125 | 0.6025 |
13 | 2009m9 | 399 | 87 | 153 | 0.69298 |
14 | 2009m10 | 459 | 105 | 185 | 0.77277 |
15 | 2009m11 | 514 | 124 | 219 | 0.83925 |
16 | 2009m12 | 570 | 144 | 256 | 0.89086 |
17 | 2010m1 | 605 | 165 | 294 | 0.92778 |
18 | 2010m2 | 653 | 185 | 332 | 0.95311 |
19 | 2010m3 | 709 | 207 | 372 | 0.97048 |
20 | 2010m4 | 714 | 227 | 410 | 0.98139 |
21 | 2010m5 | 716 | 245 | 444 | 0.98779 |
22 | 2010m6 | 718 | 261 | 475 | 0.99167 |
23 | 2010m7 | 723 | 275 | 502 | 0.99412 |
24 | 2010m8 | 723 | 287 | 527 | 0.99571 |
25 | 2010m9 | 723 | 297 | 549 | 0.99676 |
Month | #pats | #C-events | #events | Power | |
---|---|---|---|---|---|
1 | 2008m9 | 3 | 1 | 1 | 0.02678 |
2 | 2008m10 | 7 | 1 | 1 | 0.02881 |
3 | 2008m11 | 17 | 1 | 1 | 0.03167 |
4 | 2008m12 | 27 | 1 | 2 | 0.03531 |
5 | 2009m1 | 44 | 2 | 3 | 0.03985 |
6 | 2009m2 | 78 | 3 | 5 | 0.04629 |
7 | 2009m3 | 128 | 5 | 8 | 0.05562 |
8 | 2009m4 | 169 | 7 | 12 | 0.0676 |
9 | 2009m5 | 209 | 10 | 18 | 0.08168 |
10 | 2009m6 | 243 | 14 | 24 | 0.09766 |
11 | 2009m7 | 300 | 18 | 32 | 0.11622 |
12 | 2009m8 | 351 | 23 | 41 | 0.13799 |
13 | 2009m9 | 399 | 29 | 52 | 0.16259 |
14 | 2009m10 | 459 | 36 | 63 | 0.19028 |
15 | 2009m11 | 514 | 43 | 77 | 0.22115 |
16 | 2009m12 | 570 | 51 | 91 | 0.25485 |
17 | 2010m1 | 605 | 60 | 107 | 0.29036 |
18 | 2010m2 | 653 | 69 | 123 | 0.32711 |
19 | 2010m3 | 709 | 79 | 140 | 0.36551 |
20 | 2010m4 | 714 | 88 | 158 | 0.40373 |
21 | 2010m5 | 716 | 98 | 175 | 0.43981 |
22 | 2010m6 | 718 | 107 | 192 | 0.47368 |
23 | 2010m7 | 723 | 116 | 209 | 0.50549 |
24 | 2010m8 | 723 | 125 | 225 | 0.53525 |
25 | 2010m9 | 723 | 133 | 240 | 0.56291 |
26 | 2010m10 | 723 | 141 | 255 | 0.5886 |
27 | 2010m11 | 723 | 149 | 270 | 0.61244 |
28 | 2010m12 | 723 | 157 | 284 | 0.63455 |
29 | 2011m1 | 723 | 164 | 297 | 0.65505 |
30 | 2011m2 | 723 | 171 | 311 | 0.67406 |
31 | 2011m3 | 723 | 178 | 323 | 0.69169 |
32 | 2011m4 | 723 | 184 | 336 | 0.70804 |
33 | 2011m5 | 723 | 191 | 348 | 0.72321 |
34 | 2011m6 | 723 | 197 | 359 | 0.7373 |
35 | 2011m7 | 723 | 203 | 371 | 0.75037 |
36 | 2011m8 | 723 | 208 | 382 | 0.76252 |
37 | 2011m9 | 723 | 214 | 392 | 0.77382 |
38 | 2011m10 | 723 | 219 | 402 | 0.78433 |
39 | 2011m11 | 723 | 224 | 412 | 0.79411 |
40 | 2011m12 | 723 | 229 | 422 | 0.80323 |
41 | 2012m1 | 723 | 234 | 431 | 0.81173 |
42 | 2012m2 | 723 | 238 | 440 | 0.81965 |
43 | 2012m3 | 723 | 243 | 449 | 0.82706 |
44 | 2012m4 | 723 | 247 | 457 | 0.83398 |
45 | 2012m5 | 723 | 251 | 465 | 0.84045 |
46 | 2012m6 | 723 | 255 | 473 | 0.84651 |
47 | 2012m7 | 723 | 259 | 481 | 0.85218 |
48 | 2012m8 | 723 | 262 | 488 | 0.8575 |
49 | 2012m9 | 723 | 266 | 496 | 0.86249 |
50 | 2012m10 | 723 | 269 | 503 | 0.86717 |
51 | 2012m11 | 723 | 273 | 509 | 0.87157 |
Symbols Used
the number of months until the obsolescence of the drug;
the number of months until the arrival of OS data;
the number of months until the arrival of PFS data;
the value of a life-year;
the number of patients who are likely to initiate the drug during every month in which it is available;
the incremental cost of using the drug for its prescribed duration.
ICD-03 Code | Description |
---|---|
8260 | Papillary adenocarcinoma, NOS – KIDNEY |
8312 | Renal cell carcinoma |
8316 | Cyst-associated renal cell carcinoma |
8317 | Renal cell carcinoma, chromophobe type |
8318 | Renal cell carcinoma, sarcomatoid |
8319 | Collecting duct carcinoma |
8320 | Granular cell carcinoma – KIDNEY |
8510 | Medullary carcinoma, NOS – KIDNEY |
8959 | Malignant cystic nephroma (KIDNEY & RENAL) |
References
Arrondeau, J., H. K. Gan, A. R. Raak, X. Paoletti and C. Le Tourneau (2010) “Development of Anti-Cancer Drugs,” Discov Med, 10(53):355–362.Search in Google Scholar
Atzpodien, J., H. Kirchner, U. Jonas, L. Bergmann, H. Schott, H. Heynemann, P. Fornara, S. A. Loening, J. Roigas, S. C. Muller, H. Bodenstein, S. Pomer, B. Metzner, U. Rebmann, R. Oberneder, M. Siebels, T. Wandert, T. Puchberger, M. Reitz and Group Prospectively Randomized Trial of the German Cooperative Renal Carcinoma Chemoimmunotherapy (2004) “Interleukin-2- and interferon alfa-2a-based Immunochemotherapy in Advanced Renal Cell Carcinoma: A Prospectively Randomized Trial of the German Cooperative Renal Carcinoma Chemoimmunotherapy Group (DGCIN),” Journal of Clinical Oncology, 22(7):1188–1194.10.1200/JCO.2004.06.155Search in Google Scholar
Atzpodien, J., H. Kirchner, U. Rebmann, M. Soder, U. Gertenbach, M. Siebels, J. Roigas, R. Raschke, S. Salm, B. Schwindl, S. C. Muller, S. Hauser, C. Leiber, E. Huland, H. Heinzer, S. Siemer, B. Metzner, H. Heynemann, P. Fornara and M. Reitz (2006) “Interleukin-2/interferon-alpha2a/13-retinoic Acid-Based Chemoimmunotherapy in Advanced Renal Cell Carcinoma: Results of a Prospectively Randomised Trial of the German Cooperative Renal Carcinoma Chemoimmunotherapy Group (DGCIN),” British Journal of Cancer, 95(4):463–469.10.1038/sj.bjc.6603271Search in Google Scholar
Berry, D. A. (2012) “Bayesian Approaches for Comparative Effectiveness Research,” Clinical Trials, 9(1):37–47.10.1177/1740774511417470Search in Google Scholar
Broglio, K. R. and D. A. Berry (2009) “Detecting an Overall Survival Benefit that is Derived from Progression-Free Survival,” Journal of the National Cancer Institute, 101(23):1642–1649.10.1093/jnci/djp369Search in Google Scholar
Chambers, J. D., P. J. Neumann and M. J. Buxton (2010) “Does Medicare Have an Implicit Cost-Effectiveness Threshold?” Medical Decision Making, 30(4):E14–E27.10.1177/0272989X10371134Search in Google Scholar
Colleoni, M., A. Giobbie-Hurder, M. M. Regan, B. Thurlimann, H. Mouridsen, L. Mauriac, J. F. Forbes, R. Paridaens, I. Lang, I. Smith, J. Chirgwin, T. Pienkowski, A. Wardley, K. N. Price, R. D. Gelber, A. S. Coates and A. Goldhirsch (2011) “Analyses Adjusting for Selective Crossover show Improved Overall Survival with Adjuvant Letrozole Compared with Tamoxifen in the BIG 1-98 study,” Journal of Clinical Oncology, 29(9):1117–1124.10.1200/JCO.2010.31.6455Search in Google Scholar
Davis, S., P. Tappenden and A. Cantrell (2012) A Review of Studies Examining the Relationship Between Progression-Free Survival and Overall-Survival in Advanced or Metatstatic Cancer. In Report by the Decision Support Unit. Sheffield, UK: School of Health and Related Research, University of Sheffield.Search in Google Scholar
Delea, T. E., A. Khuu, D. Y. Heng, T. Haas and D. Soulieres (2012) “Association Between Treatment Effects on Disease Progression End Points and Overall Survival in Clinical Studies of Patients with Metastatic Renal Cell Carcinoma,” British Journal of Cancer, 107(7):1059–1068.10.1038/bjc.2012.367Search in Google Scholar
Duh, M. S., E. Dial, T. K. Choueiri, A. A. Fournier, L. Antras, D. Rodermund, M. P. Neary and W. K. Oh (2009) “Cost Implications of IV versus Oral Anti-Angiogenesis Therapies in Patients with Advanced Renal Cell Carcinoma: Retrospective Claims Database Analysis,” Current Medical Research and Opinion, 25(8):2081–2090.10.1185/03007990903084800Search in Google Scholar
Escudier, B., T. Eisen, W. M. Stadler, C. Szczylik, S. Oudard, M. Siebels, S. Negrier, C. Chevreau, E. Solska, A. A. Desai, F. Rolland, T. Demkow, T. E. Hutson, M. Gore, S. Freeman, B. Schwartz, M. Shan, R. Simantov, R. M. Bukowski and Target Study Group (2007a) “Sorafenib in Advanced Clear-Cell Renal-Cell Carcinoma,” New England Journal of Medicine, 356(2):125–134.10.1056/NEJMoa060655Search in Google Scholar
Escudier, B., A. Pluzanska, P. Koralewski, A. Ravaud, S. Bracarda, C. Szczylik, C. Chevreau, M. Filipek, B. Melichar, E. Bajetta, V. Gorbunova, J. O. Bay, I. Bodrogi, A. Jagiello-Gruszfeld, N. Moore and Avoren Trial investigators (2007b) “Bevacizumab Plus Interferon alfa-2a for Treatment of Metastatic Renal Cell Carcinoma: A Randomised, Double-Blind Phase III trial,” Lancet 370(9605):2103–2111.10.1016/S0140-6736(07)61904-7Search in Google Scholar
Escudier, B., T. Eisen, W. M. Stadler, C. Szczylik, S. Oudard, M. Staehler, S. Negrier, C. Chevreau, A. A. Desai, F. Rolland, T. Demkow, T. E. Hutson, M. Gore, S. Anderson, G. Hofilena, M. Shan, C. Pena, C. Lathia and R. M. Bukowski (2009) “Sorafenib for Treatment of Renal Cell Carcinoma: Final Efficacy and Safety Results of the Phase III Treatment Approaches in Renal Cancer Global Evaluation Trial,” Journal of Clinical Oncology, 27(20):3312–3318.10.1200/JCO.2008.19.5511Search in Google Scholar
Escudier, B., J. Bellmunt, S. Negrier, E. Bajetta, B. Melichar, S. Bracarda, A. Ravaud, S. Golding, S. Jethwa and V. Sneller (2010) “Phase III Trial of Bevacizumab plus Interferon alfa-2a in Patients with Metastatic Renal Cell Carcinoma (AVOREN): Final Analysis of Overall Survival.” Journal of Clinical Oncology, 28(13):2144–2150.10.1200/JCO.2009.26.7849Search in Google Scholar
Ferlay, J, H. R. Shin, F. Bray, D. Forman, C. Mathers and D. M. Parkin (2013) GLOBOCAN 2008 v2.0. International Agency for Research on Cancer 2010. Available at: http://globocan.iarc.fr/ (accessed July 15, 2013).Search in Google Scholar
Gerber, D. (2008) “Targeted Therapies: A New Generation of Cancer Treatments,” American Family Physician, 1(77):311–319.Search in Google Scholar
Gupta, K., J. D. Miller, J. Z. Li, M. W. Russell and C. Charbonneau (2008) “Epidemiologic and Socioeconomic Burden of Metastatic Renal Cell Carcinoma (mRCC): A Literature Review,” Cancer Treatment Reviews, 34(3):193–205.10.1016/j.ctrv.2007.12.001Search in Google Scholar
Harrison, M. R., D. J. George, M. S. Walker, C. Chen, B. Korytowsky, D. T. Kirkendall, E. J. Stepanski and A. P. Abernethy (2013) “’Real World’ Treatment of Metastatic Renal Cell Carcinoma in a Joint Community- Academic Cohort: Progression-Free Survival Over Three Lines of Therapy,” Clin Genitourin Cancer, 12(13):002.10.1016/j.clgc.2013.05.002Search in Google Scholar
Jha, Prabhat and Anne Mills (2002) Improving Health Outcomes of The Poor: The Report of Working Group 5 of The Commission on Macroeconomics and Health. Geneva, CH: World Health Organization.Search in Google Scholar
Lopes, Maria, Maruit Chulikavit, Rohan Parikh, Lee Stern, Zhimei Liu and Jaqueline Rogerio (2012) “Budget Impact of Everolimus in Treating Metastatic Renal Cell Carcinoma,” American Journal of Pharmacy Benefits, 4(Special Issue):SP41–SP48.Search in Google Scholar
Mason, A., M. Drummond, S. Ramsey, J. Campbell and D. Raisch (2010) “Comparison of Anticancer Drug Coverage Decisions in the United States and United Kingdom: Does the Evidence Support the Rhetoric?” Journal of Clinical Oncology, 28(20):3234–3238.10.1200/JCO.2009.26.2758Search in Google Scholar
MIMS. (2013) MIMS: Tables. Haymarket Medical Media 2013. Available at: http://www.mims.co.uk/tables/ (accessed July 15, 2013).Search in Google Scholar
Motzer, R. J., T. E. Hutson, P. Tomczak, M. D. Michaelson, R. M. Bukowski, O. Rixe, S. Oudard, S. Negrier, C. Szczylik, S. T. Kim, I. Chen, P. W. Bycott, C. M. Baum and R. A. Figlin (2007) “Sunitinib versus Interferon Alfa in Metastatic Renal-Cell Carcinoma,” New England Journal of Medicine, 356(2):115–124.10.1056/NEJMoa065044Search in Google Scholar
Motzer, R. J., B. Escudier, S. Oudard, T. E. Hutson, C. Porta, S. Bracarda, V. Grunwald, J. A. Thompson, R. A. Figlin, N. Hollaender, G. Urbanowitz, W. J. Berg, A. Kay, D. Lebwohl, A. Ravaud and Record- Study Group (2008) “Efficacy of Everolimus in Advanced Renal Cell Carcinoma: A Double-Blind, Randomised, Placebo-Controlled Phase III Trial,” Lancet, 372(9637):449–456.10.1016/S0140-6736(08)61039-9Search in Google Scholar
Motzer, R. J., T. E. Hutson, P. Tomczak, M. D. Michaelson, R. M. Bukowski, S. Oudard, S. Negrier, C. Szczylik, R. Pili, G. A. Bjarnason, X. Garcia-del-Muro, J. A. Sosman, E. Solska, G. Wilding, J. A. Thompson, S. T. Kim, I. Chen, X. Huang and R. A. Figlin (2009) “Overall Survival and Updated Results for Sunitinib Compared with Interferon Alfa in Patients with Metastatic Renal Cell Carcinoma,” Journal of Clinical Oncology, 27(22):3584–3590.10.1200/JCO.2008.20.1293Search in Google Scholar
Motzer, R. J., B. Escudier, S. Oudard, T. E. Hutson, C. Porta, S. Bracarda, V. Grunwald, J. A. Thompson, R. A. Figlin, N. Hollaender, A. Kay, A. Ravaud and Record- Study Group (2010) “Phase 3 Trial of Everolimus for Metastatic Renal Cell Carcinoma: Final Results and Analysis of Prognostic Factors,” Cancer, 116(18):4256–4265.10.1002/cncr.25219Search in Google Scholar
Motzer, R. J., B. Escudier, P. Tomczak, T. E. Hutson, M. D. Michaelson, S. Negrier, S. Oudard, M. E. Gore, J. Tarazi, S. Hariharan, C. Chen, B. Rosbrook, S. Kim and B. I. Rini (2013) “Axitinib versus Sorafenib as Second-Line Treatment for Advanced Renal Cell Carcinoma: Overall Survival Analysis and Updated Results from a Randomised Phase 3 Trial,” Lancet Oncology, 14(6):552–562.10.1016/S1470-2045(13)70093-7Search in Google Scholar
National Cancer Institute. (2013) SEER Data, 1973-2010. National Institutes of Health 2013. Available at: http://seer.cancer.gov/data/ (accessed July 15, 2013).Search in Google Scholar
Philipson, Berndt, E. A. Gottschalk and E. Sun (2007) “A Cost-Benefit Analysis of The Food and Drug Administration,” Journal of Public Economics, V:1–46.Search in Google Scholar
Philipson, T., M. Eber, D. N. Lakdawalla, M. Corral, R. Conti and D. P. Goldman (2012) “An Analysis of Whether Higher Health Care Spending in the United States versus Europe is’Worth It’ in The Case of Cancer,” Health Affairs (Millwood), 31(4):667–675.10.1377/hlthaff.2011.1298Search in Google Scholar
Philipson Tomas, J., Eric Sun, Dana Goldman and B. Jena Anupam (2012) “A Reexamination of the Costs of Medical R&D Regulation,” Forum for Health Economics and Policy, 15:1–29.10.1515/fhep-2012-0020Search in Google Scholar
PhRMA (2012). Report: Nearly 1,000 Medicines and Vaccines in Testing Offer Hope in the Fight Against Cancer 2012. Available at: http://www.phrma.org/sites/default/files/pdf/phrmamedicinesindevelopmentcancer2012.pdf.Search in Google Scholar
Remak, E., C. Charbonneau, S. Negrier, S. T. Kim and R. J. Motzer (2008) “Economic Evaluation of Sunitinib Malate for The First-Line Treatment of Metastatic Renal Cell Carcinoma,” Journal of Clinical Oncology, 26(24):3995–4000.10.1200/JCO.2007.13.2662Search in Google Scholar
Rini, B. I., B. Escudier, P. Tomczak, A. Kaprin, C. Szczylik, T. E. Hutson, M. D. Michaelson, V. A. Gorbunova, M. E. Gore, I. G. Rusakov, S. Negrier, Y. C. Ou, D. Castellano, H. Y. Lim, H. Uemura, J. Tarazi, D. Cella, C. Chen, B. Rosbrook, S. Kim and R. J. Motzer (2011) “Comparative Effectiveness of Axitinib versus Sorafenib in Advanced Renal Cell Carcinoma (AXIS): A Randomised Phase 3 Trial,” Lancet, 378(9807):1931–1939.10.1016/S0140-6736(11)61613-9Search in Google Scholar
Seabury, S. A., D. P. Goldman, J. R. Maclean, J. R. Penrod and D. N. Lakdawalla (2012) “Patients Value Metastatic Cancer Therapy more Highly Than is Typically Shown Through Traditional Estimates,” Health Affairs, 31(4):691–699.10.1377/hlthaff.2012.0174Search in Google Scholar
Sherrill, B., M. Amonkar, Y. Wu, C. Hirst, S. Stein, M. Walker and J. Cuzick (2008) “Relationship Between Effects on Time-to-Disease Progression and Overall Survival in Studies of Metastatic Breast Cancer,” British Journal of Cancer, 99(10):1572–1578.10.1038/sj.bjc.6604759Search in Google Scholar
Sutton, A. J. and K. R. Abrams (2001) “Bayesian Methods in Meta-Analysis and Evidence Synthesis,” Statistical Methods in Medical Research, 10(4):277–303.10.1177/096228020101000404Search in Google Scholar
Truven Health Analytics. (2013) RED BOOK Online 2013. Available at: http://redbook.com/redbook/online/.Search in Google Scholar
Viscusi, W. Kip and Joseph E. Aldy (2003) “The Value of A Statistical Life: A Critical Review of Market Estimates Throughout the World,” Journal of Risk and Uncertainty, 27(1):5–76.10.1023/A:1025598106257Search in Google Scholar
Yabroff, K. R., E. B. Lamont, A. Mariotto, J. L. Warren, M. Topor, A. Meekins and M. L. Brown (2008) “Cost of Care for Elderly Cancer Patients in the United States,” Journal of the National Cancer Institute, 100(9):630–641.10.1093/jnci/djn103Search in Google Scholar
©2014 by De Gruyter