Supplement: Cost-Effectiveness of Tolvaptan in Autosomal Dominant Polycystic Kidney
Disease
Table of Contents:
Text:
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Mortality in End-Stage Renal Disease
Modeling CKD Progression
Model Validation
Selected Model Assumptions
Additional Sensitivity Analyses
Probabilistic Sensitivity Analysis
Modeling Tolvaptan Discontinuation
Tables:
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Supplement Table 1: Annual Rate of Mortality in Patients with ESRD and ADPKD
Supplement Table 2: Inputs for Probabilistic Sensitivity Analysis
Supplement Table 3: Costs and Distributions for ESRD
Figures:
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Supplement Figure 1: Survival by Age of Development of ESRD in Patients With ADPKD
Supplement Figure 2: Differences in Progression to ESRD in a Population According to
Within-Individual Variation in CKD Progression
Supplement Figure 3: Modeled Progression to ESRD Under Different Assumptions About
Within-Individual Variation in CKD Progression
Supplement Figure 4: Estimated Cost of Healthy Year
Supplement Figure 5: Quality of Life in Healthy Individuals With Age
Supplement Figure 6: Cost-Effectiveness of Tolvaptan at Different Ages of Initiation
Supplement Figure 7: Cost-Effectiveness of Tolvaptan at Different Starting GFRs
Supplement Figure 8: Sensitivity Analysis – Cost-Effectiveness at Different Rates of
Reduction in CKD Progression From Tolvaptan
Supplement Figure 9: Sensitivity Analysis – Cost-Effectiveness if Tolvaptan Therapy
Changes Quality of Life
Supplement Figure 10: One-Way Sensitivity Analysis of Remaining Inputs in Women
Supplement Figure 11: One-Way Sensitivity Analysis of Remaining Inputs in Men
Supplement Figure 12: Probabilistic Sensitivity Analysis Results in Women (Base-Case
Tolvaptan Price)
Supplement Figure 13: Probabilistic Sensitivity Analysis Results in Men (Base-Case
Tolvaptan Price)
Supplement Figure 14: Probabilistic Sensitivity Analysis Results in Women (Tolvaptan Price
at 10% of Base Case)
Supplement Figure 15: Probabilistic Sensitivity Analysis Results in Men (Tolvaptan Price at
10% of Base Case)
Mortality in End-Stage Renal Disease (ESRD)
We define end-stage renal disease (ESRD) as an estimated glomerular filtration rate (eGFR) less
than 15 ml/min/1.73m2. Once patients develop ESRD, mortality each year is a function of 1) age
of development of ESRD; and 2) years since development of ESRD. Mortality rates were
calculated from the United States Renal Data System (USRDS) report of all-cause mortality in
the ESRD population, and include all ESRD patients in the United States. Consequently, the
average mortality rate includes mortality from the mix of ESRD patient modalities (such as
peritoneal dialysis or kidney transplant) at each age range in the United States.
We derived mortality rates in patients with Autosomal Dominant Polycystic Kidney Disease
(ADPKD) who develop ESRD using the following steps:
1) We calculated the mortality rate for all patients with ESRD for each age group at onset
and time interval since development of ESRD reported by USRDS, assuming a constant
hazard of death within each time interval. For example, USRDS publishes ESRD
mortality in the 1st 3 years of ESRD, then mortality in the 1st 5 years of ESRD. To
determine mortality rates in years 3 – 5, we divided survival to 5 years by survival to 3
years and calculated the associated annual rate of death. Annual rates were calculated
using the most recent survival data available.
2) Using the method described in step 1, we also estimated mortality rates for all patients
with ESRD and for all diabetics with ESRD for each reported time interval since ESRD
onset. Because, in the USRDS report, mortality for diabetics with ESRD was not
separated by age, in this step mortality rates for each time interval since development of
ESRD (for all patients and for all diabetics) included an aggregate of all age groups.
3) For each interval since onset of ESRD, we calculated a mortality rate for all non-diabetics
with ESRD. These mortality rates were derived from the aggregate (including all ages)
mortality in all ESRD patients and in all diabetics with ESRD calculated in step 2) using
the following equation:
𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝐸𝑆𝑅𝐷 = (𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝐷𝑀 ∗ 𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛𝐷𝑀 + 𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝑁𝑜𝑛𝐷𝑀 ∗ 𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛𝑁𝑜𝑛𝐷𝑀 )
Or,
𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝑁𝑜𝑛𝐷𝑀 =
(𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝐸𝑆𝑅𝐷 − 𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝐷𝑀 ∗ 𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛𝐷𝑀 )
⁄𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛
𝑁𝑜𝑛𝐷𝑀
4) A mortality hazard comparing mortality in non-diabetics with ESRD to all patients with
ESRD was calculated for each time interval since onset of ESRD by dividing mortality in
non-diabetics by mortality for all ESRD patients. Because we used mortality rates for
diabetics and all ESRD patients that included patients of all ages, these mortality hazards
for non-diabetics were not age specific.
5) For each age group and interval since onset of ESRD, we estimated a mortality rate for
non-diabetics by multiplying mortality for all ESRD patients (of a given age group and
time since onset of ESRD derived from step 1) by the mortality hazard for non-diabetics.
For this step, we assumed that the relative mortality difference between persons with and
without diabetes does not differ in different ages of ESRD onset and is constant over the
interval.
6) Finally, to estimate mortality in patients with ADPKD, we multiplied the mortality
among non-diabetics for each age group and dialysis duration interval by 0.57 (the
mortality hazard for ADPKD compared to non-diabetics with ESRD.(1) (See
Supplement Table 1 and Supplement Figure 1 for detail on ESRD mortality
estimates).
An analysis of data provided by the USRDS for 2010 suggests only slight differences in age of
ESRD onset between diabetics and non-diabetics. Specifically, diabetics are slightly older upon
ESRD onset, although in non-diabetics a larger proportion of ESRD onset occurs over 75 years
of age. Consequently, any potential bias due to aggregation of age groups is likely to be small.
To account for a range of uncertainty due to potential differences between the composition s of
ESRD patients of a given age group nationally and within modeled cohorts, we applied a range
of +- 20% for mortality in ESRD in deterministic sensitivity analysis. Estimates for probabilistic
sensitivity analysis were based from varying the proportion of transplant recipients and mix of
patient co-morbidities in the general ESRD population (details available from authors upon
request).
Modeling CKD Progression
In the base case, we assumed that patients had stage 2 CKD with an eGFR of 80 ml/min/1.73m2.
Probability of progression from stages 2 to 3a, 3a to 3b, 3b to 4, and 4 to 5 (ESRD) were
obtained from a separate microsimulation model implemented in SAS software. The mean rate of
eGFR decline in the base case was obtained from the annual rate of GFR decline reported in the
placebo group from the TEMPO trial.(2) The standard deviation of annual rate of eGFR decline
in the population was obtained from an observational cohort of 229 patients with ADPKD that
was not a part of the TEMPO trial. (3)
Both cohort analyses reporting average rates of GFR decline were of limited duration. For
example, the mean duration of follow-up in the TEMPO trial was 3 years, while the average
period of follow-up in the observational cohort of patients with ADPKD was 7.8 years.(2, 3)
There are few reports in the literature about within person variation in rate of CKD progression
over a longer period of time. For example, if a person’s GFR declines by 3 ml/min/1.73m2 in one
decade it is unknown whether they can be expected to progress faster, slower, or at the same rate
over the next decade. The degree of correlation in rate of decline within individuals has a
significant impact on timing and the proportion of a cohort ultimately progressing to more
advanced stages of CKD and ESRD. Supplement Figure 2 illustrates that if individuals are
assigned a single rate of GFR decline from a population distribution, progression to ESRD
occurs slightly faster but ultimately in fewer people compared to a scenario where individuals are
assigned a new rate of GFR decline every 6 years. When including these differences in our base
case Markov model, the median time to development of ESRD is similar in the two scenarios,
but the scenario where GFR decline is allowed to vary within individuals (i.e. they are assigned a
new GFR decline from the population distribution every 6 years) leads to more patients
progressing to ESRD before they die. (Supplement Figure 3)
We chose to model CKD progression allowing for variation in GFR decline within individuals
(every 6 years) because the eventual rates of progression to ESRD (80%-90%) are more
consistent with observations in actual ADPKD populations(4) than the 60%-70% rate of
progression to ESRD observed if there is no variability in CKD progression within patients.
Model Validation
To assess the face validity of our model, we compared the median ages of development of ESRD
resulting from a hypothetical cohort from our model with median ages reported in two
observational studies. For this analysis, we assumed an average annual decline in eGFR decline
of 2.4 ml/min/1.73m2, which is what was reported from a subset of a large observational study of
patients with ADPKD.(3) This average rate of eGFR decline is less than that which we used in
our base case, since our base case is based on the population studied in the TEMPO trial which
had a more rapid eGFR decline.
Assuming an average rate of eGFR decline of 2.4 ml/min/1.73m2, we determined the median age
of developing ESRD for a balanced cohort of 40 year old men and women with ADPKD whose
starting eGFR was 80 ml/min/1.73m2. To enable us to compare our simulated estimate to the
median ages of ESRD reported in the literature, we used the Kaplan-Meier method, where
patients who die in each 3-month interval of our simulation are “censored”. We compared our
median ages of ESRD to the weighted averages across men and women reported from a large
observational study of patients in the United States and a large observational study of patients in
Europe.(4, 5) The observational study of patients in Europe reported median renal survival
separately for patients with PKD1 and PKD2 mutations. To calculate an average median renal
survival for the ADPKD population, we weighted their reported median renal survivals by their
observed prevalence of PKD1 and PKD2 mutations in the population (15% PKD2 and 85%
PKD1).
In our simulated cohort of 40 year-olds with ADPKD, a starting eGFR of 80 ml/min/1.73m2 and
an average annual decline in eGFR of 2.4 ml/min/1.73m2, the median age of developing ESRD
was 56 years-old. This was comparable to the average observed in the European Cohort (57
years-old), and was only slightly below the average observed in the cohort from the United
States (59 years-old). The similarity between age of developing ESRD in our simulated cohort
and that observed in two population studies supports the accuracy of our model.
Selected Model Assumptions
Cost of healthy year:
Medical costs associated with the healthy state (i.e. no history of CKD) were obtained from
published literature(6). To avoid bias associated with the categories used for cost reporting, we
interpolated a smoothed cost curve. This was done with the following assumptions:
1) Costs increase linearly with age between reported categories
2) Costs reported for each age group represent the cost incurred at the median age of that
cohort.
Supplement Figure 4 illustrates a comparison of the reported and interpolated health care costs.
Additional Cost of Stage 3 CKD, Stage 4 CKD, and ESRD:
The incremental increase in medical costs due to stages 3 and 4 CKD were obtained from Smith
et al.(7). They represent the incremental cost associated with CKD observed in a managed care
population (Kaiser Permanente Northwest), after adjusting for demographic and comorbid
conditions in a multivariate analysis. These costs were derived from departmental expenditures,
administrative costs, indirect costs and joint costs multiplied by utilization volume. They are not
specific to ADPKD.
The cost of ESRD was obtained from the average paid per patient by Medicare in 2010 reported
by the USRDS Atlas of ESRD. We used Medicare costs because it is the primary payer for
ESRD. Because the USRDS Atlas also includes outpatient prescription drug costs for patients
with and without prior kidney transplant, these were included in the cost estimates.
Quality of life in a healthy year:
Quality of life in the healthy population varied with sex and age due to the addition of comorbidities that occur with population aging. These age-based quality-adjusted life year (QALY)
weight estimates were obtained from the Beaver Dam Health Outcomes Study (8) and were used
as a baseline from which to add any additional quality of life decrements associated with chronic
kidney disease and tolvaptan therapy. To allow for a continuous decrement of quality-of-life
weights with aging, rather than step-wise, we made the following assumptions:
1) The QALY weights associated with a given age range was equal to the QALY weight for
individuals at the median age within that range.
2) QALY weights decrease linearly between any two age ranges.
From these assumptions, we interpolated QALYs for each age and sex. Supplement Figure 5
illustrates the interpolated vs. reported values.
Quality of life based on CKD stage:
Quality of life has been shown to decline due to burden of CKD. While the decline in quality of
life is smaller in early-moderate stage 3 CKD, it becomes more significant as CKD advances.
Estimates of quality of life in CKD came from several sources. Rizk et al. described the quality
of life in patients with ADPKD, and found that in those with stage 2 CKD there was no
difference in quality of life compared to the general population.(9) Consequently, we used the
age-based quality of life in the general population for patients with ADPKD and stage 2 CKD.
However, Rizk et al. did find decreased quality of life in more advanced CKD. Gorodetskaya et
al. published “Health-related Quality of Life and Estimates of Utility in Chronic Kidney
Disease” where they described the loss in quality-of-life associated with stages 3, 4, and 5
(ESRD) CKD(10) after adjusting for age, gender, and diabetes. Because our model further
delineates between stages 3a and 3b, we estimated quality of life for these states. To do this we
assumed:
1) Quality of life reported for stage 3 CKD represented the quality of life experienced in
patients with an eGFR of 45 ml/min/1.73m2 (0.87).
2) Quality of life in stage 3 CKD declined in a linear fashion
3) Quality of life associated with stage 4 CKD (0.85) began at GFR of 30 (i.e. entry into
stage 4)
With these assumptions, we first calculated the average QALY weight associated with stage 3b
CKD. This was the average of the QALY weight for patients with an eGFR of 30 ml/min/1.73m2
and those with an eGFR of 45 ml/min/1.73m2
𝑄𝐴𝐿𝑌 𝑤𝑒𝑖𝑔ℎ𝑡 𝑓𝑜𝑟 𝑠𝑡𝑎𝑔𝑒 3𝑏 = 0.86 =
(0.85 + 0.87)⁄
2
Based on this calculation, the QALY difference associated with a 15 ml/min/1.73m2 change in
eGFR was 0.02. Consequently, the average QALY weight for patients with an eGFR of 60
ml/min/1.73m2 would be: 0.87 + 0.02 = 0.89, leading to an average QALY weight for stage 3a
CKD of:
𝑄𝐴𝐿𝑌 𝑤𝑒𝑖𝑔ℎ𝑡 𝑓𝑜𝑟 𝑠𝑡𝑎𝑔𝑒 3𝑎 = 0.88 =
(0.87 + 0.89)⁄
2
Assuming patients surveyed by Gorodetskaya et al. were evenly distributed across stage 3 CKD,
the QALY weights we use for CKD stages 3a and 3b yield an average stage 3 QALY weight
equal to that reported by Gorodetskaya et al. Additionally, the calculated QALY weight for an
eGFR of 60 ml/min/1.73m2 of 0.89 is comparable to the QALY weight reported by
Gorodetskaya et al. for patients with an eGFR > 60 ml/min/1.73m2.
Quality of life loss in patients with ESRD (0.77) was derived from the time tradeoff analysis by
Gorodetskaya et al. (10) and was comparable to the range reported by Tengs et al. (0.50 to 0.84)
(11) for various dialysis-related conditions. Quality of life for CKD stages 3a-4 and ESRD were
not specific to ADPKD.
The analysis adopted a societal perspective, considered only direct healthcare costs, and
discounted all healthcare costs and benefits at 3% annually. We used TreeAge software to
implement the decision model and to perform the deterministic sensitivity analyses as well as the
probabilistic sensitivity analyses.
Additional Sensitivity Analyses
We conducted the following additional analyses to assess the model's sensitivity to underlying
assumptions:
Sensitivity to accelerating rate of eGFR decline:
We examined how the cost-effectiveness of tolvaptan would change if eGFR accelerates over
time rather than progressing linearly. We tested a 40 percent increase in the rate of eGFR decline
upon progression to CKD stage 4. We conducted two versions of this analysis. In both versions,
aside from eGFR decline, all other model inputs were identical to the base case. In the first
version, we assumed that the decline in eGFR for CKD stages 2 through 3b were the same as in
the base case (-3.7 ml/min/1.73m2 in patients not on tolvaptan and -2.72 ml/min/1.73m2 in
patients treated with tolvaptan). After patients progress to CKD stage 4, we assumed a 40%
increase in rate of CKD progression in both groups, with the new rates of decline being -5.18
ml/min/1.73m2 in patients not on tolvaptan and -3.81 ml/min/1.73m2 in patients on tolvaptan. In
this scenario, the ICER for men and women pooled was $717,300/QALY gained, approximately
3.5% lower than the ICER in the base case. This is consistent with our finding (manuscript figure
3a) that tolvaptan is more cost-effective in patients whose CKD progresses more rapidly.
In a second version of the accelerating CKD progression sensitivity analysis, we used model
calibration to approximate a scenario where the average rates of decline in eGFR between the
base case and the "acceleration" scenario are similar. To do this, we selected – through model
calibration – rates of decline for CKD stages 2-3b and 4 (with the decline in stage 4 being 40%
above the decline in stages 2-3b) that yielded a median age of progression to ESRD in men and
women not receiving tolvaptan of 58, as was observed in the base case. The actual rates of eGFR
decline obtained from this process were -3.5 ml/min/1.73m2 for stages 2-3b and -4.9
ml/min/1.73m2 for stage 4. This sensitivity analysis showed a pooled ICER in men and women
of $741,700/QALY gained, which is only 0.3% different from the base case.
Sensitivity to measurement of kidney disease progression:
The TEMPO trial reports the effect of tolvaptan on kidney disease progression in the following
two ways: 1) decline in eGFR, and; 2) change in the reciprocal of serum creatinine. The two
results were comparable. Using decline in eGFR, the placebo group had an average annual
decline in eGFR of -3.7 ml/min/1.73m2, while the tolvaptan group had an average annual decline
of -2.72 ml/min/1.73m2. The decline in the treatment group was 0.98ml/min/1.73m2/year slower,
reflecting a 26.5 percent reduction in the rate of kidney disease progression. Using the reciprocal
of serum creatinine, the slope in the placebo group was -3.81 (mg per milliliter)-1, while the slope
in the treatment group was -2.61 (mg per milliliter)-1. The decline in the treatment group was 1.2
(mg per milliliter)-1 per year slower, reflecting a 31.5 percent reduction in the rate of kidney
disease progression.
We chose to use the slope of eGFR decline in our primary analysis due to the familiarity of
eGFR decline among clinicians. However, in a sensitivity analysis we examined how our
findings change when using data from the reciprocal of serum creatinine. This analysis
demonstrated an incremental cost-effectiveness ratio from tolvaptan therapy of $624,300/QALY
gained in men and $586,700 per QALY gained in women. The ICER in a pooled cohort of men
and women was $604,600 per QALY gained, which is approximately 19% less than the ICER
from the base case. This difference is due to a combination of a slightly larger treatment effect
and more rapid rate of baseline kidney disease progression in the reciprocal of serum creatinine
group.
Sensitivity to treatment effect heterogeneity:
To determine how treatment effect heterogeneity might influence our results, we conducted a
sensitivity analysis where we separated patients treated with tolvaptan into three equal sized
populations. In the first, the mean decline in eGFR was –2.2 ml/min/1.73m2. In the second, the
mean decline in eGFR was –2.72 ml/min/1.73m2. In the third, the mean decline in eGFR was –
3.2 ml/min/1.73m2. Standard deviations of eGFR declines in the 3 populations were assumed to
be the same as in the baseline analysis. These 3 different “treatment effects” were chosen to
reflect heterogeneity reported from TEMPO, while preserving the same overall mean treatment
effect averaged across the three populations. The costs and QALYs gained in the three treatment
groups were averaged together and then compared to the costs and QALYs gained in patients
from the base case who were not placed on tolvaptan.
This analysis found that tolvaptan was less cost effective after considering possible treatment
effect heterogeneity. The average incremental cost-effectiveness ratio among the three different
groups in a pooled population of men and women was $831,000 per QALY gained, which is
11.6% higher than the base case of $744,100 per QALY gained.
Probabilistic Sensitivity Analysis
Distributions for probabilistic sensitivity analysis were obtained – when possible – from
uncertainty in estimates reported in the literature. (See Supplement Table 2) In instances where
a wide range of estimates have been reported in the literature, the distribution variance was
increased to reflect this. Assumptions regarding cost of ESRD were derived from national
averages for different age groups published by the USRDS.
The degree of uncertainty about ESRD costs for probabilistic sensitivity analysis was estimated
by determining a range of costs associated with varying mixes of transplant recipients and
privately versus publically insured recipients (upper 95%CI for cost assumed 10% fewer
transplants and 10% more privately insured). This uncertainty is likely correlated across age
groups. For instance, if there are 5% fewer transplant recipients and 5% fewer privately insured
individuals in our modeled cohort compared to prevalent ESRD patients in the United States in
one age group, a similar difference is likely to be present in the other age groups. However,
because older patients are less likely to receive transplants and are less likely to be privately
insured, the magnitude of the cost uncertainty (as a share of average ESRD costs) associated with
a given percentage point reduction (or increase) in the proportion of patients with transplants or
privately insured is smaller in older individuals. To account for this attenuation of uncertainty at
older ages while maintaining a realistic correlation of uncertainty across age groups, we modeled
ESRD cost uncertainty in the following manner:
1) We defined a single uncertainty distribution in terms of the percentage above/below the
base case estimates of ESRD costs for 40-49 year-olds. In this case, a normal distribution
with mean 1.0 and 95% confidence interval spanning 0.84-1.16. This corresponded to +/16% of the base case value for 40-49 year-olds.
2) We drew from the normal distribution defined in 1) and subtracted 1.0 to get the
percentage difference from the base case
3) We then multiplied this percentage difference by age specific multipliers defined in
Supplement Table 3, given the lower range of uncertainty in costs for older individuals
with ESRD, especially those on Medicare.
4) We then added 1.0 to the number in 3) and multiplied by the base case age-specific
ESRD costs, shown for convenience in Supplement Table 3 to produce a distribution of
age-specific ESRD costs that were correlated across age groups and had lower
uncertainty for older individuals with ESRD.
Uncertainty about quality of life in different CKD stages is likely correlated. We took the
following steps to incorporate this correlation in our probabilistic sensitivity analyses:
1) For each simulation, we drew QALY weight estimates in stage 3a CKD from the
uncertainty distribution for that stage (described in supplement table 2).
2) We determined the number of standard deviations the stage 3a QALY weight estimate is
away from the stage 3a mean QALY weight, preserving plus and minus signs for a
deviation above or below the mean respectively.
3) For each QALY weight mean and distribution in more advanced CKD stages (i.e., 3b, 4,
and 5), we applied the (standardized) deviation from the mean drawn from the stage 3a
uncertainty distribution to calculate a QALY weight for the more advanced CKD stage.
The following equation illustrates this calculation for determining the QALY weight in
stage 4:
QALYstage4 = MeanQALYstage4 +SDStage4*[(QALY3a – MeanQALY3a)/SD3a]
4) For any PSA sample where a QALY weight that was above 1.0, we truncated the weight
at 1.0.
Modeling Tolvaptan Discontinuation
In an exploratory analysis we determined the cost-effectiveness of treatment with tolvaptan if
some patients continue to stop taking tolvaptan after 3-years. For this analysis, we assumed that
patients continue to withdrawal from treatment at the same annual rate as was observed in the
TEMPO trial. The following methods were used prior to conducting this analysis.
1) In the TEMPO trial, 15.4% of patients randomized to tolvaptan discontinued therapy
over a 3-year follow-up period. From this, we estimated an annual probability of
tolvaptan discontinuation by assuming that the rate of discontinuation was constant
throughout the trial using the following equation:
1
𝐴𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑑𝑖𝑠𝑐𝑜𝑛𝑡𝑖𝑛𝑢𝑎𝑡𝑖𝑜𝑛 = 1 − (1 − 0.154)(3)
2) Next, we estimated the annual decline in eGFR among patients who actually take
tolvaptan. This additional step was required because, as discussed in the main text,
our base case model uses the treatment effect reported for the cohort randomized to
tolvaptan based on intention to treat. For the estimation, we assumed that patients not
taking tolvaptan experience the rate of eGFR decline observed in the placebo cohort
of the TEMPO trial (–3.70 ml/min/1.73m2/year). Assuming an annual probability of
medication discontinuation of 5.4% (estimated from the step above), we used a builtin Excel Solver (distributed with Excel 2010) to identify the rate of eGFR decline
among individuals taking tolvaptan that would yield an average decline in eGFR
(including those who take tolvaptan and those who discontinue therapy) of –2.72
ml/min/1.73m2/year, observed in the treatment arm of the TEMPO trial. The solver
function is illustrated below:
2.72 =
[(1 − 𝑝𝐷𝑖𝑠𝑐) ∗ 𝑥 + (𝑝𝐷𝑖𝑠𝑐 ∗ 3.7)] + [(1 − 𝑝𝐷𝑖𝑠𝑐)2 ∗ 𝑥 + (𝑝𝐷𝑖𝑠𝑐 2 ∗ 3.7)] + [(1 − 𝑝𝐷𝑖𝑠𝑐)3 ∗ 𝑥 + (𝑝𝐷𝑖𝑠𝑐 3 ∗ 3.7)]⁄
3
Where pDisc is the proportion of patients discontinuing tolvaptan per year.
2.72 is the average eGFR decline (in ml/min/1.73m2), and “x” is the rate of eGFR
decline in patients taking tolvaptan.
3) Using the estimated annual probabilities of medication discontinuation, along with
the annual rate of eGFR decline among those not taking tolvaptan and those taking
the medication, we then undertook a separate microsimulation to determine the
probability of progression to subsequent stages of CKD in each year in a hypothetical
cohort of 100,000 individuals with an eGFR of 80 ml/min/1.73m2 who are initially on
tolvaptan therapy but who discontinue therapy at a rate of 5.4% per year. These
probabilities were entered into the Markov model for cohorts initiating tolvaptan
therapy.
4) Finally, in the Markov model, costs among those in the tolvaptan treatment strategy
were adjusted each year to reflect the proportion of patients in that year who remain
on tolvaptan therapy.
Because tolvaptan slows CKD progression, patients who remain on tolvaptan live longer (on
average) than those who must discontinue treatment. Due to limitations in our model structure,
we did not adjust tolvaptan costs to account for the increased proportion of survivors being on
tolvaptan. Instead, we assumed that the medication costs for the cohort of patients in the
tolvaptan treatment strategy are simply a function of the proportion of patients in that cohort who
remain on treatment and those who discontinue treatment. This assumption would be expected to
bias our findings in favor of tolvaptan being more cost-effective, since (from a cost standpoint)
we underestimate the proportion of patients in later years who are actually on tolvaptan.
Additionally, in this analysis we assumed that patients who discontinue tolvaptan due so at the
beginning of a given year. This assumption also biases the discontinuation analysis in favor of
tolvaptan.
Supplement Table 1: Annual Rate of Mortality in Patients with ESRD and ADPKD
Age at
Dialysis
Initiation
30-39
40-49
50-59
60-64
65-69
70-74
75-79
80-84
85+
0 - 90
days
0.052
0.080
0.115
0.161
0.202
0.272
0.341
0.421
0.557
90 days - 1yr
0.041
0.055
0.081
0.105
0.133
0.169
0.219
0.261
0.347
1 - 2 yr
0.029
0.044
0.065
0.078
0.102
0.123
0.149
0.194
0.236
2 - 3 yr
0.031
0.040
0.062
0.082
0.098
0.123
0.150
0.188
0.248
3 - 5 yr
0.024
0.038
0.062
0.083
0.106
0.130
0.162
0.200
0.268
5 - 10 yr
0.020
0.033
0.055
0.082
0.110
0.138
0.185
0.220
0.260
Note: We assume that patients surviving beyond 10 years continue to experience the 5 – 10
year annual rate of mortality.
Supplement Table 2: Inputs for Probabilistic Sensitivity Analysis
Intervention Parameters:
Percent reduction in rate of CKD progression from
Tolvaptan - %
(Targeted Mean Value)
26
16
37
beta
Change in quality of life due to Tolvaptan - QALY
1.00
0.96
1.04
normal
Cost of tolvaptan per month1
5760
4000
7000
gamma
0.154
0.10
0.22
beta
Proportion of patients who discontinue tolvaptan
Annual cost of laboratory tests and
Natural History Parameters:
monitoring1
Range (95% CI)
Distribution
62.82
(+-20%)
gamma
Age based probability of death in healthy individuals
(U.S. Life tables)
(+-10%)
normal
Increased risk of death (CKD stage 3a) - hazard ratio
1.20
1.10
1.30
normal
Increased risk of death (CKD stage 3b) - hazard ratio
1.80
1.70
1.90
normal
Increased risk of death (CKD stage 4) - hazard ratio
3.20
3.00
3.40
normal
(see supplement text)
0.77
1.27
lognormal
Probability of death from ESRD
(ml/min/1.73m 2/yr)
Rate of CKD progression
CKD-Related Quality of life assumptions
3.70
(Targeted Mean QALY)
3.00
4.40
Range (95% CI)
normal
QALY - CKD stage 3a†
0.88
0.78
0.98
normal
QALY - CKD stage
3b†
0.86
0.74
0.98
normal
QALY - CKD Stage
4†
0.85
0.72
0.98
normal
0.56
0.98
Range (95% CI)
normal
1,833
1,493
2,201
gamma
Annual added cost of Stage 3b CKD
4507
3,676
5,409
gamma
Annual added cost of Stage 4 CKD
5,844
1,000
14,054
gamma
ESRD†
CKD-Related Cost assumptions1
Annual added cost of Stage 3a CKD
Cost of ESRD
0.77
(Targeted Mean $)
(see supplement text)
All normal distributions are truncated to be greater than or equal to zero.
1
Costs are in 2010 USD.
† Distributions for chronic kidney disease (CKD) and end-stage renal disease (ESRD) QALYs are truncated to be
less than or equal to 1. After adjusting QALYs due to “change in quality of life due to tolvaptan” the final QALY is
truncated to be less than or equal to 1.
Supplement Table 3: Costs and Distributions for ESRD
Age-Based Multiplier applied to the percentage difference drawn from a
Age
single uncertainty distribution in the Probabilistic Sensitivity Analysis1
30-39
1.00
40-49
1.00
50-59
0.85
60-64
0.74
65-69
0.46
70-74
0.30
75-79
0.18
80-84
0.10
85+
0.07
1
Multiplied by the cost drawn from the probabilistic sensitivity analysis
2
Includes an estimate of Medicare Part D Costs
ESRD Cost2
63,780
66,827
71,413
73,102
76,458
82,098
85,189
89,549
90,623
Supplement Figure 1: Survival by Age of Development of ESRD in Patients With ADPKD
Supplement Figure 2: Differences in Progression to ESRD in a Population According to
Within-Individual Variation in CKD Progression
Note: Both simulations include an average decline in eGFR of –3.7 ml/min/1.73m2/year, with a
population standard deviation of 2.8.
Supplement Figure 3: Modeled Progression to ESRD Under Different Assumptions About
Within-Individual Variation in CKD Progression
Note: All simulations include an average decline in eGFR of –3.7 ml/min/1.73m2/year, with a
population standard deviation of 2.8.
Supplement Figure 4: Estimated Cost of Healthy Year
Estimates based on reported costs in $2010 of $3,474 for people 50-64, 10,948 for ages 65-74,
and 18,968 for 75+.
Supplement Figure 5: Quality of Life in Healthy Individuals With Age
Note: data derived from Beaver Dam Health Outcomes Study where for men aged 45-54, 55-64,
65-74, 75-84, and 84+, QALYs are: 0.0941, 0.874, 0.841, 0.838, 0.817 respectively. For women
aged 45-54, 55-64, 65-74, 75-84, and 84+, QALYs are: 0.901, 0.871, 0.833, 0.792, 0.8
respectively.
Supplement Figure 6: Cost-Effectiveness of Tolvaptan at Different Ages of Initiation
Note: eGFR decline measured in ml/min/1.73m2.
To examine whether the cost-effectiveness of tolvaptan in younger patients is affected by their
likelihood of having higher eGFR, we simulated a cohort of 30 year-old men and women with a
starting eGFR of 90 ml/min/1.73m2 (compared to 80 ml/min/1.73m2 in the base case). In men
the incremental cost effectiveness ratio (ICER) was $754,500 per QALY gained, while in women
it was $723,300 per QALY gained. In a pooled cohort of men and women the ICER was
$738,400 per QALY gained, which is within 1% of the pooled ICER in the base case. This
suggests that the ICER does not change significantly when tolvaptan is given to younger
patients, even after considering their higher likelihood of less advanced kidney disease.
Supplement Figure 7: Cost-Effectiveness of Tolvaptan at Different Starting GFRs
Supplement Figure 8: Sensitivity Analysis – Cost-Effectiveness at Different Rates of
Reduction in Kidney Disease Progression From Tolvaptan
Note: The base case reduction in kidney disease progression was 26.5 percent.
Supplement Figure 9: Sensitivity Analysis – Cost-Effectiveness if Tolvaptan Therapy
Changes Quality of Life
Supplement Figure 10: One-Way Sensitivity Analysis of Remaining Inputs in Women
Supplement Figure 11: One-Way Sensitivity Analysis of Remaining Inputs in Men
Supplement Figure 12: Probabilistic Sensitivity Analysis Results in Women (Base-Case
Tolvaptan Price)
Note: The incremental cost-effectiveness ratio is < $100,000 per quality-adjusted life year
gained in less than 0.1% of simulations.
Supplement Figure 13: Probabilistic Sensitivity Analysis Results in Men (Base-Case
Tolvaptan Price)
Note: The incremental cost-effectiveness ratio is < $100,000 per quality-adjusted life year
gained in less than 0.1% of simulations.
Supplement Figure 14: Probabilistic Sensitivity Analysis Results in Women (Tolvaptan
Price at 10% of Base Case)
Note: Incremental cost-effectiveness ratio (ICER) is less than $100,000 per quality-adjusted life
year (QALY) gained in 97% of 10,000 simulations, and less than $50,000 per QALY gained in
79% of simulations.
Supplement Figure 15: Probabilistic Sensitivity Analysis Results in Men (Tolvaptan Price
at 10% of Base Case)
Note: Incremental cost-effectiveness ratio (ICER) is less than $100,000 per quality-adjusted life
year (QALY) gained in 96% of 10,000 simulations, and less than $50,000 per QALY gained in
78% of simulations.
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