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Licensed Unlicensed Requires Authentication Published by De Gruyter June 26, 2014

A Cost-Benefit Analysis of Using Evidence of Effectiveness in Terms of Progression Free Survival in Making Reimbursement Decisions on New Cancer Therapies

  • Warren Stevens EMAIL logo , Tomas Philipson , Yanyu Wu , Connie Chen and Darius Lakdawalla

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.


Corresponding author: Warren Stevens, Precision Health Economics, 11100 Santa Monica Blvd Suite 500, Los Angeles, CA 90025, USA, Phone: +310-984-7714, e-mail:

  1. 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. 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(BOSBPFS≤0, BOS>0)+Pr(BOS>0, BPFS>0)E(BOSBOS>0, BPFS≤0)=Pr(BOS>0)E(BOSBOS>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.

Appendix Table 1

Estimating the Number of Potential Beneficiaries from A New Anti-Cancer Drug Targeted at mRCC.

US (SEER)EU* (IARC)
Registry estimate of incidence of RCC111,65415,912
Population incidence of RCC241,62179,560
Proportion of new cases mRCC319%790815,116
Proportion of current RCC progressing to mRCC440%13,48525,777
Total number of cases of mRCC per year21,39340,894
First line treatment eligible patients574%15,83130,261
Second line treatment eligible patients552%823215,736

*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.

Appendix Table 2

Estimating the Delay Between the Publication of Phase III Clinical Trial Data on PFS Outcomes and Phase III Trial Data on OS Outcomes.

Publication date (PFS)Publication date (OS)Cumulative mean net delayAuthor, year (PFS)Author, year (PFS)
IL2+IFN+CRAApr-04Aug-0628Atzipodian 2004Atzipodian 2006
SorafenibJan-07Jul-0930Escudier 2007Escudier 2009
SunitinibJan-07Aug-0930Motzer 2007Motzer 2009
BevacizumabDec-07May-1030Escudier 2007Escudier 2010
EverolimusAug-08Sep-1029Motzer 2008Motzer 2010
AxitinibDec-11May-1327Rini 2011Motzer 2013
Appendix Table 3

Table Showing Probability Data Extrapolated from Kernel Density Distributions from all Studies up to 2006 (n=22) and all Studies up to 2011 (n=41).

Model parametersData from studies up to 2006 (n=22)Data from studies up to 2011 (n=41)
Positive PFS, positive OS64%61%
Negative PFS, positive OS21%11%
Negative PFS, negative OS6%10%
Positive PFS, negative OS9%18%
Conditional probabilities:
 Positive OS, when positive PFS88%77%
 Negative OS when positive PFS12%23%
 Mean effect on OS, when OS is positive5.58 months4.83 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.

Appendix Table 4

Review of Estimates of Incremental Drug only and Total Health Care Costs for Patients with mRCC by Drug Therapy.

A: United States
Incremental cost estimatesMedian months1Cost per month (US$)2,3Median drug cost (US$)
Sunitinib11.0319235,112
Interferon alpha5.0218410,920
Sorafenib4.7447121,014
Axitinib6.7510134,177
Sunitinib versus Interferon alpha24,192
Axitinib versus sorafenib13,163
B: Europe
Incremental cost estimatesMedian months1Cost per month (GBP)2,4Drug cost (GBP)(Euro)
Sunitinib11.0209723,068
Interferon alpha5.014357174
Sorafenib4.7298014,006
Axitinib6.7351723,564
Sunitinib versus Interferon alpha15,89317,721
Axitinib versus sorafenib955810,657

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.

Appendix Table 5A

Events (and months) from Beginning of Trial Until a Statistically Significant Difference Between PFS Endpoints is Achieved in the Sunitinib versus IFN-alpha Trial.

Month#pats#C-events#eventsPower
12004m87110.03751
22004m916120.05552
32004m1032340.08436
42004m1172690.14123
52004m1212412180.24387
62005m117320310.38755
72005m225331490.562
82005m333446740.73664
92005m4411651040.86818
102005m5518861390.94709
112005m66391131830.98396

The bolded row indicates the point at which a statistically significant result is achieved (Power is ≥0.95).

Appendix Table 5B

Estimated Events (and months) from Beginning of Trial Until a Statistically Significant Difference Between OS Endpoints is Achieved in the Sunitinib versus IFN-alpha Trial.

Month#pats#C-events#eventsPower
12004m87110.02754
22004m916110.03047
32004m1032120.03424
42004m1172230.04012
52004m12124360.04875
62005m11736100.0598
72005m22539150.07406
82005m333413230.09245
92005m441119330.11484
102005m551825450.14208
112005m663934590.17548
122005m770843760.21367
132005m873253940.25335
142005m9745631120.29256
152005m10750731290.33055
162005m11750821470.36683
172005m12750911630.40122
182006m17501001790.43372
192006m27501091950.46436
202006m37501172100.49319
212006m47501252250.52027
222006m57501332400.54567
232006m67501412540.56949
242006m77501482670.59179
252006m87501552810.61267
262006m97501622930.63221
272006m107501693060.65049
282006m117501753180.66759
292006m127501813300.6836
302007m17501883410.69857
312007m27501933530.71258
322007m37501993640.7257
332007m47502053740.73799
342007m57502103840.7495
352007m67502153940.76029
362007m77502204040.77041
372007m87502254140.7799
382007m97502304230.78881
392007m107502344320.79718
402007m117502394400.80504
412007m127502434490.81244

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.

Appendix Table 6A

Events (and Months) from Beginning of Trial Until a Statistically Significant Difference Between PFS Endpoints is Achieved in the Axitinib versus Sorafenib Trial.

Month#pats#C-events#eventsPower
12008m93110.03028
22008m107110.03676
32008m1117230.04687
42008m1227350.06111
52009m144580.08084
62009m2789150.11244
72009m312815250.16395
82009m416923390.23486
92009m520933560.31877
102009m624344760.40906
112009m730057980.50479
122009m8351711250.6025
132009m9399871530.69298
142009m104591051850.77277
152009m115141242190.83925
162009m125701442560.89086
172010m16051652940.92778
182010m26531853320.95311
192010m37092073720.97048
202010m47142274100.98139
212010m57162454440.98779
222010m67182614750.99167
232010m77232755020.99412
242010m87232875270.99571
252010m97232975490.99676
Appendix Table 6B

Estimated Events (and Months) from Beginning of Trial Until a Statistically Significant Difference Between OS Endpoints is Achieved in the Axitinib versus Sorafenib Trial.

Month#pats#C-events#eventsPower
12008m93110.02678
22008m107110.02881
32008m1117110.03167
42008m1227120.03531
52009m144230.03985
62009m278350.04629
72009m3128580.05562
82009m41697120.0676
92009m520910180.08168
102009m624314240.09766
112009m730018320.11622
122009m835123410.13799
132009m939929520.16259
142009m1045936630.19028
152009m1151443770.22115
162009m1257051910.25485
172010m1605601070.29036
182010m2653691230.32711
192010m3709791400.36551
202010m4714881580.40373
212010m5716981750.43981
222010m67181071920.47368
232010m77231162090.50549
242010m87231252250.53525
252010m97231332400.56291
262010m107231412550.5886
272010m117231492700.61244
282010m127231572840.63455
292011m17231642970.65505
302011m27231713110.67406
312011m37231783230.69169
322011m47231843360.70804
332011m57231913480.72321
342011m67231973590.7373
352011m77232033710.75037
362011m87232083820.76252
372011m97232143920.77382
382011m107232194020.78433
392011m117232244120.79411
402011m127232294220.80323
412012m17232344310.81173
422012m27232384400.81965
432012m37232434490.82706
442012m47232474570.83398
452012m57232514650.84045
462012m67232554730.84651
472012m77232594810.85218
482012m87232624880.8575
492012m97232664960.86249
502012m107232695030.86717
512012m117232735090.87157

Symbols Used

  1. the number of months until the obsolescence of the drug;

  2. the number of months until the arrival of OS data;

  3. the number of months until the arrival of PFS data;

  4. the value of a life-year;

  5. the number of patients who are likely to initiate the drug during every month in which it is available;

  6. the incremental cost of using the drug for its prescribed duration.

Appendix Table 7

Diagnostic Codes for Renal Cell Carcinoma (RCC).

ICD-03 CodeDescription
8260Papillary adenocarcinoma, NOS – KIDNEY
8312Renal cell carcinoma
8316Cyst-associated renal cell carcinoma
8317Renal cell carcinoma, chromophobe type
8318Renal cell carcinoma, sarcomatoid
8319Collecting duct carcinoma
8320Granular cell carcinoma – KIDNEY
8510Medullary carcinoma, NOS – KIDNEY
8959Malignant cystic nephroma (KIDNEY & RENAL)

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Published Online: 2014-6-26
Published in Print: 2014-1-1

©2014 by De Gruyter

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