PharmacoEconomics (2014) 32:1129–1139 DOI 10.1007/s40273-014-0203-5

ORIGINAL RESEARCH ARTICLE

Evaluating Disease-Modifying Agents: A Simulation Framework for Alzheimer’s Disease Shien Guo • Denis Getsios • Nikhil Revankar • Peng Xu • Gwilym Thompson • Joel Bobula • Loretto Lacey • Maren Gaudig

Published online: 15 August 2014 Ó Springer International Publishing Switzerland 2014

Abstract Background Considerable advances have been made in modeling Alzheimer’s disease (AD), with a move towards individual-level rather than cohort models and simulations that consider multiple dimensions when evaluating disease severity. However, the possibility that disease-modifying agents (DMAs) may emerge requires an update of existing modeling frameworks. Objectives The aim of this study was to develop a simulation allowing for economic evaluation of DMAs in AD. Methods The model was developed based on a previously published, well-validated, discrete event simulation which measures disease severity on the basis of cognition, behaviour, and function, and captures the interrelated changes in these measures for individuals. The updated model adds one more domain, patient dependence, in addition to cognition, behaviour, and function to better characterize disease severity. Furthermore, the model was modified to have greater flexibility in assessing the impact

L. Lacey was formerly affiliated with Janssen Alzheimer Immunotherapy, Dublin, Ireland and M. Gaudig was formerly affiliated with Alzheimer Immunotherapy, Dublin, Ireland. S. Guo (&)  D. Getsios  P. Xu Evidera, 430 Bedford Street, Suite 300, Lexington Office Park, Lexington, MA 02420, USA e-mail: [email protected] N. Revankar Evidera, London, UK

of various important assumptions, such as the long-term effectiveness of DMAs and their impact on survival, on model outcomes. A validation analysis was performed to examine how well the model predicted change in disease severity among patients not receiving DMA treatment by comparing model results to those observed in two recent phase III clinical trials of bapineuzumab. In addition, various hypothetical scenarios were tested to demonstrate the improved features of the model. Results Validation results show that the model closely predicts the mean changes in disease severity over 18 months. Results from different hypothetical scenarios show that the model allows for credible assessment of those major uncertainties surrounding the long-term effectiveness of DMAs, including the potential impact of improved survival with DMA treatment. They also indicate that varying these assumptions could have a major impact on the value of DMAs. Conclusions The updated economic model has good predictive power, but validation against longer-term outcomes is still needed. Our analyses also demonstrate the importance of designing a model with sufficient flexibility such that the model allows for assessment of the impact of key sources of uncertainty on the value of DMAs. Present Address: L. Lacey Lacey Solutions, Dublin, Ireland L. Lacey Janssen Alzheimer Immunotherapy, Dublin, Ireland

G. Thompson Janssen-Cilag Ltd, High Wycombe, UK

Present Address: M. Gaudig Janssen EMEA, Neuss, Germany

J. Bobula Pfizer Inc., New York, USA

M. Gaudig Alzheimer Immunotherapy, Dublin, Ireland

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Key Points for Decision Makers Disease-modifying agents (DMAs) are drugs that will alter the course of Alzheimer’s disease (AD) through a direct effect on the underlying AD pathophysiology. Long-term effectiveness and the potential impact on survival are two of the major uncertainties that must be considered when evaluating the health and economic implications of DMAs in AD. The discrete event simulation described in this study allows the long-term treatment effect of DMAs to be assessed under different scenarios—effect sustained indefinitely, loss of effect over time, and maintenance of initial effect. The model also allows the user to explore the influence of treatment on mortality. The new model also adds one more domain, patient’s dependence using the Dependence Scale, to better characterize disease severity. Our analyses indicate that the model has good predictive power when it comes to changes in disease severity and is sufficiently flexible, allowing for the assessment of the impact of key sources of uncertainty on the value of DMAs.

S. Guo et al.

existing frameworks. For example, unlike current therapies, DMAs could potentially improve survival in a treated population, which may actually lead to an increase in overall health care costs as the population lives longer [9]. Furthermore, the long-term effectiveness of DMAs will initially be unknown, as would any residual effectiveness following treatment discontinuation. The objective of this research was to expand upon a previously developed discrete event simulation (DES) [3, 10, 11] to accommodate the requirements of evaluating DMAs. These primarily included (i) using more appropriate disease severity measures to better characterize disease severity progression, estimate costs and utilities associated with different levels of severity, and capture the potential benefits of DMAs; and (ii) improving the existing model structure to permit assessment of the impact of major sources of uncertainty on model outcomes with respect to the long-term effectiveness of DMAs, the residual effectiveness following treatment discontinuation, and the impact on survival. This paper introduces the updated model design and its data sources, and presents the results of a model validation exercise along with results based on several hypothetical scenarios to demonstrate the potential impact key uncertainties have on model outcomes.

2 Methods 2.1 Model Overview

1 Introduction Given aging populations and increases in life expectancy, the health consequences and costs of Alzheimer’s disease (AD) will pose a growing burden on health care systems. In the UK alone, there are expected to be in excess of 1 million cases of dementia by 2025, almost two-thirds of whom will have AD [1], imposing a significant economic burden on the health care system. Although some recent clinical trials for therapeutic agents in AD have been disappointing, considerable investments are still being made with the goal of expanding treatment options for this population [2]. Assessing the economic value of emerging agents will require the use of modeling, with the cost of newer therapies weighed against their health benefits and any consequent reductions in the costs of managing dementia. Considerable advances have been made in modeling AD [3–8], with a move towards individual-level rather than cohort models and simulations that consider multiple dimensions when evaluating disease severity. However, the possibility that diseasemodifying agents (DMAs) may emerge requires revisiting

An individual patient simulation using DES was developed to estimate the clinical and economic outcomes associated with the introduction of a hypothetical DMA in the UK. The model was developed based on a previously published, well-validated model which measures disease severity on the basis of cognition (using the Mini-Mental State Examination [MMSE]), behaviour (using the Neuropsychiatric Inventory [NPI]), and function (using both activities of daily living [ADLs] and instrumental ADLs [IADLs]), and captures interrelated changes in these measures for simulated individuals [3, 10, 11]. Unlike the previous model, the current model characterizes disease severity by replacing the ADLs and IADLs with the Disability Assessment for Dementia Scale (DAD) [12] as it is more commonly used in clinical trials of AD to measure the effects of DMAs on functional status [13]. In addition, the Dependence Scale (DS) [14], which measures the amount of assistance patients with AD require, was added to the model as it provides additional information on patients’ severity level of illness [15], and has strong correlations with costs of care and patient health utilities [16– 18]. As data related to the long-term effectiveness of DMAs would be unavailable at their market launch, the

Modeling Disease-Modifying Agents in AD

current model was further improved by allowing greater flexibility in order to test various assumptions on (i) effect size of DMAs beyond the trial follow-up duration (e.g. no change or any percentage reduction over a prespecified time period); (ii) change in disease severity over time following treatment discontinuation (e.g. loss of treatment benefit at discontinuation or over a period of time); and (iii) treatment impact on survival (e.g. no treatment impact or some impact due to reduced rate of progression). Figure 1 presents an overview of the model flow. Simulated patients are first created and assigned demographic and disease characteristics. An identical copy of each patient is then created, with the original patient assigned treatment with the DMA plus standard of care (SOC) and the copied patient assigned to SOC alone. The model then assigns events that will occur to the individual and processes these events as they occur, updating individual characteristics and future event times as required. Disease severity is measured over four domains—cognition (using the MMSE), behaviour (using the NPI), function (using the DAD), and overall dependence (using the DS). During the simulation, the patient’s change in MMSE score since the time of the last update is first estimated. Changes in the NPI, DAD, and DS are then computed sequentially to preserve the interdependencies between the various disease severity measures, while also taking into account the previously estimated scores. Costs, health

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utilities, caregiver time, and time spent in each location of care are calculated and accrued over the appropriate time period based on the patient’s disease severity and treatment status. Patients can discontinue treatment as a result of predefined stopping rules, serious adverse events or due to other reasons. In the analyses presented here, patients only discontinue treatment if their MMSE scores fall below 10. The effect of treatment discontinuation on disease progression is only modelled for patients being treated with a DMA. Upon discontinuation, these patients can lose all treatment benefit at either the time of discontinuation or over a specified duration of time—in the analyses presented here, the latter rule is applied with benefits lost over a 6-month time period. Survival can be either treatment independent, in which case the time to death is only assigned at baseline, or treatment dependent, in which case the time to death is updated based on the rate of disease progression. The simulation continues on this repetitive process until the model time horizon is reached or all patients die, whichever comes first. 2.2 Model Parameters and Data Sources 2.2.1 Patient and Disease Characteristics at Baseline The model contains patient profiles from three donepezil clinical trials (n = 463) [3]. These patients had a mean age

Fig. 1 Simplified representation of model flow. DAD Disability Assessment for Dementia Scale, DMA disease-modifying agent, DS Dependence Scale, MMSE Mini-Mental State Examination, NPI Neuropsychiatric Inventory, QALYs quality-adjusted life-years, SOC standard of care

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of 75.2 (range 55–91) years and 66 % were female. The mean disease severity scores at baseline were 19.5 (range 16–26) for the MMSE, 15.3 (range 0–72) for the NPI, 61.7 (range 40–94) for the DAD, and 6.4 (range 3–9) for the DS.

Information on baseline DAD and DS scores were not available from these trials and thus were imputed using linear equations that utilize other patient and disease characteristics (Table 1).

Table 1 Inputs and equations Equations

Coefficient and predictor

Disease progression Annual rate of change in MMSEa NPI change from baseline

b

5.47 - 0.43PM1 - 0.0042PM2 ? 0.14PM3 - 0.079PrevRate ? 0.075Age ? Int

Total DAD scorec

(5.74 ? 0.03 Weeks - 0.59NPIbase-0.0012 Weeks 9 NPIbase ? 0.24NPIPre 1.74White - 3.82Black ? 2.34PsyMed ? 0.12MMSEbase 0.22MMSErecent ? Int) 9 1.44 50.51 ? 2.55MMSErecent-0.21NPIrecent - 0.53Age ? 7.28Female

Total DS scorec

9.26 - 0.076MMSErecent - 0.073DADrecent ? 0.035Age ? 0.72Female

Mortalityd Treatment effect considerede

Weibull shape: 1.72 Weibull scale: 7.91 Cox model: 0.056Age - 0.59Female - 0.047MMSEbase ? 0.020DeclineRate

Treatment effect not considered

Weibull Shape: 1.85 Weibull scale: 4.60 ? 0.11 Age - 0.0009 Age^2 ? 0.33 Female ? 0.023 MMSEbase

Direct medical ? community costsf Log (3-month costs) Utilities

3.19 ? 0.49Female ? 0.028AgeCurrent ? 0.13DSrecent

g

Patients

0.99 - 0.041DS

Caregivers

0.84 - 0.0015NPI

Other model inputs

Value

Caregiver daily hours by MMSE Mild (25 and above)

0.0

Mild–moderate (20–24)

2.7

Moderate (15–19)

9.8

Moderate–severe (10–14)

11.9

Severe (9 and below)

13.9

In institutional care by MMSE (%)f Mild (25 and above)

0.0

Mild–moderate (20–24)

0.0

Moderate (15–19)

3.2

Moderate–severe (10–14)

17.1

Severe (9 and below)

39.3

AIC Akaike information criterion, BIC Bayesian information criterion, DAD Disability Assessment for Dementia Scale, DS Dependence Scale, MMSE Mini-Mental State Examination, NPI Neuropsychiatric Inventory a

PMs represent patients’ previous MMSE measurements, partitioned over the scale of MMSE; PrevRate is the patient’s last known rate of decline per year; Age represents patients’ age at baseline; Int represents a random intercept parameter

b

Weeks represents weeks of follow-up in the simulation; NPIbase is the patient’s baseline NPI; NPIrecent is the patient’s last NPI. White and Black are dummy variables for race; PsyMed is a dummy variable for patients on psychiatric medications at baseline; MMSEbase represents the patient’s MMSE at baseline; and MMSErecent represents the patient’s current MMSE

c

Female is the dummy variable for sex (1 = female; 0 = male); R2 for DAD and DS equation is 0.49 and 0.72, respectively

d

The AIC and BIC are 1,658 and 1,668 based on Weibull fit, which are lower (i.e. better) than those for exponential fit (AIC = 1,797; BIC = 1,802), loglogistic fit (AIC = 1,681; BIC = 1,691), and lognormal fit (AIC = 1,720; BIC = 1,729) e

DeclineRate represents the annual rate of decline in MMSE

f

R2 for the cost equation is 0.18

g

R2 for patient and caregiver utility equation is 0.18 and 0.06, respectively

Modeling Disease-Modifying Agents in AD

2.2.2 Disease Progression Disease progression was modelled based on the interrelated changes in MMSE, NPI, DAD, and DS scores over time. For patients assigned to the SOC group, MMSE scores were estimated using a piecewise linear equation obtained from published literature (Table 1) [3]. This equation predicts the annual rate of MMSE change based on patient’s age, current disease severity, and previous rate of change, and was derived using pooled data from the Consortium to Establish a Registry for Alzheimer’s disease (CERAD) registry [19] and seven donepezil clinical trials [20–26]. Changes in NPI score were predicted using an equation from the same data source as the equation for MMSE [3]. As shown in Table 1, NPI scores are influenced by patients’ baseline and most recent MMSE and NPI scores, as well as other patient characteristics. As equations to model changes in DAD and DS scores were not available from the published literature, two multivariable linear equations were derived from the Dependence in Alzheimer’s Disease in England (DADE) study, which included 249 patients in the community and institutionalized settings with possible/probable AD [27– 30]. The DADE study collected information on patients’ disease severity, as measured by MMSE, NPI, DAD, and DS, and other data, including patient demographics, resource utilization, and patient utilities. The predictors of DAD and DS scores and their corresponding coefficients are shown in Table 1. DAD scores are influenced by MMSE and NPI scores, and DS scores are influenced by MMSE and DAD, in addition to patients’ age and sex. 2.2.3 Mortality Survival data were obtained from analysis of data from the CERAD study [19]. Two predictive equations were derived from the analysis. One equation predicts survival using patients’ age, sex, and MMSE score at baseline (Table 1). If this equation is applied in the simulation, treatment has no influence on survival. However, given that a DMA may have the potential to extend life expectancy, the model also allows survival to be influenced by treatment through its effect on the rate of MMSE change. For this analysis, a Cox proportional hazards model was derived from the CERAD data [19]. In addition to patients’ age, sex, and baseline MMSE score, the annual rate of MMSE decline, a time-dependent covariate, was also included in the equation (Table 1). 2.2.4 Direct Medical Costs Direct medical costs included costs of cholinesterase inhibitors/memantine, medical care for AD, community

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services, and accommodation (Table 1). Cost of cholinesterase inhibitors and memantine was estimated based on a weighted daily cost (£4.26) of donepezil (28 %), rivastigmine (7 %), galantamine (5 %), memantine alone (5 %), and a combination of cholinesterase inhibitors and memantine (55 %) using an assumed treatment distribution. In all the analyses presented here, the model assumes that 90 % of the patients received one of these treatments at baseline. The unit costs for these drugs were obtained from the British National Formulary [31]. Costs of medical care and community services, in 2010 values, were taken from the DADE study [27–30]. For patients living in the community, costs over 3 months were estimated using a linear regression, with age, sex, and DS score as predictors (Table 1). For institutionalized patients, a mean 3-month cost (£701), estimated from the 36 institutionalized patients in the DADE study, was applied regardless of disease severity. Cost of accommodation, which is only applied to patients who are institutionalized, was also estimated based on these 36 patients from the DADE study (£10,377 every 3 months). As the intention of this analysis is not to estimate the incremental cost-effectiveness ratio for a DMA, costs related to the DMA, such as drug, routine monitoring, and adverse event management, were not considered. Costs and time by location of care are accumulated based on the severity of disease that patients experience over the course of the simulation. The proportion of patients institutionalized by disease severity level was derived from the DADE study (Table 1). The severity levels were categorized according to MMSE scores: mild (MMSE C25), mild–moderate (MMSE C20 and \25), moderate (MMSE C15 and \20), moderate–severe (MMSE C10 and \15), and severe (MMSE \10). The cost of medical care and community services for a patient is weighted based on the distribution of location of care with respect to the patient’s current disease severity level. For example, 60.7 and 39.3 % of patients who have severe AD (as defined by the MMSE) are in the community and institutional care, respectively. Thus, when assigning the medical care and community costs for a patient with severe AD, 60.7 % of the value from the predictive equation for community patients and 39.3 % of the value for institutionalized patients is utilized. Similarly, institutionalization is not explicitly modelled as an event and thus a specific percentage of the time the patient is alive (i.e. the time between each update of the patient’s status) is allotted as time spent in institutional care. This percentage is based on the proportion of patients institutionalized at the current severity level of the patient. While modeling time to institutionalization directly would have been preferable, appropriate data from which to model this were not identified.

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2.2.5 Caregiver Time and Health Utilities Indirect cost included only caregiver time which was predicted based on patients’ level of disease severity, categorized by MMSE scores and converted into costs by applying the minimum wage rate (£6.19 per hour) for fulltime employees in the UK [32]. The mean caregiver hours per day by level of severity were obtained from published literature (Table 1) [33]. Data used to assign health utilities for patient and caregivers were obtained from the DADE study, which measured patient and caregiver utilities using the EQ-5D [27–30]. Health utilities for patients and caregivers were estimated based on patients’ DS and NPI scores, respectively (Table 1) as these were the only severity measures found to be significantly associated with these outcomes. 2.3 Analyses To assess how well the model predicts changes in MMSE, NPI, DAD, and DS, changes in these outcome measures at 18 months from the SOC arm of the model were compared with those from two placebo arms of the two recent phase III clinical trials of bapineuzumab in mild-to-moderate AD with the same follow-up time [13]. One of the trials (Study 302) included 1,121 carriers of apolipoprotein E4 (APOE4), and the other (Study 301) had 1,331 non-carriers. These two trials were chosen for external validation mainly because they included patients similar to the model population and captured changes in scores on MMSE, NPI, DAD, and DS over time. Table 2 shows the baseline values of these measures for subjects in both trials. To demonstrate how the model assesses the impact of various assumptions on the long-term effectiveness of a DMA and its effect on survival, several hypothetical scenarios were performed. The model population in these analyses were focused on patients with mild AD (MMSE 20–26) as they are more likely to be the potential target for future DMAs than patients with more advanced AD. In the analyses of assessing impact of different assumptions on

the long-term effectiveness of a DMA during the trial follow-up period, treatment was assumed to have direct treatment effects of 3 points on the MMSE, -3 points on the NPI, 3 points on the DAD, and -0.5 points on the DS over an assumed 18-month follow-up period. Due to the interrelated changes in these measures, as shown in the equations in Table 1, these assumed effect sizes would be expected to result in an improvement in MMSE, NPI, DAD, and DS scores from baseline by an average of 3.00, -3.96, 11.48, and -1.57 points, respectively, over 18 months. For example, a 3-point treatment effect on the MMSE would, on average, lead to a 0.96 reduction in an individual’s NPI score, thus resulting in a difference in NPI scores between treated and untreated individuals of -3.96 on the NPI. The resulting change scores for DAD and DS can be estimated from the same method. After 18 months, three scenarios on the observed direct treatment effect were assumed: (i) the observed treatment effect size is sustained for the rest of the model time horizon; (ii) the observed effect size after 18 months drops by 50 % over subsequent years (i.e. the reference case); and (iii) the observed effect size drops by 100 % at the end of 18 months, meaning that continued treatment only maintains the initial 18-month treatment benefit. In these scenarios, treatment with a DMA is assumed to have no impact on the survival, but a sensitivity analysis was also performed to assess the potential impact of this assumption under different mortality assumptions. All of the analyses were performed based on 50,000 simulated patients in each treatment arm, using a lifetime model horizon and discount rates of 3.5 % per annum for costs and health outcomes.

3 Results 3.1 Model Validation Table 2 shows the model validation results, comparing mean changes in MMSE, NPI, DAD, and DS scores over

Table 2 Model validation results Mean score at baseline

Model

Mean change score at 18 months

MMSE (0–30)a

NPI (0–144)a

DAD (0–100)a

DS (0–15)a

MMSE

NPI

DAD

DS

19.5

15.3

61.8b

6.4b

-5.2

4.2

-14.7c

1.5c

Trial 301 (non-carriers)

21.2

11.2

80.5

4.6

-3.9

1.6

-15.5

1.4

Trial 302 (carriers)

20.7

10.1

79.4

4.8

-4.5

3.6

-16.2

1.4

DAD Disability Assessment for Dementia Scale, DS Dependence Scale, MMSE Mini-Mental State Examination, NPI Neuropsychiatric Inventory a

Values in the parentheses indicate the ranges of the scales

b

Baseline scores were predicted using regression equations derived from different data sources

c

Change scores were not affected by baseline scores in the model as they were estimated using MMSE and NPI scores at the same time point

Modeling Disease-Modifying Agents in AD

18 months. At baseline, the severity of disease among the modelled population is, on average, slightly worse compared with that of the trial population. It should be noted again that DAD and DS scores for the model population were imputed with two regression equations derived from the DADE study, which have R2 scores of 0.49 and 0.72, respectively. This may explain why the mean DAD score among the modelled population is much lower than the mean scores in both trials as half of the variance is still not accounted for by the predictors included in the equation. At 18 months, mean change scores from baseline in MMSE, NPI, and DS among the modelled population are slightly greater than those among the trial population. This is very consistent with our expectation as the rate of progression is usually greater as baseline disease severity increases. The mean change in DAD among the model population is, however, slightly smaller than those observed from both trials. Again, this is likely due to the equation used to convert DAD score from MMSE and NPI score, which seems to underestimate mean change. Despite that, the mean change score in DAD at 18 months for the modelled population still falls in a very reasonable range relative to observed results. Overall, these results suggest that the model has fairly good predictive power for disease progression, as measured by the interrelated changes in MMSA, NPI, DAD, and DS, among patients receiving SOC. 3.2 Results Under Different Long-Term Effectiveness Scenarios Figure 2a illustrates that the pattern of change in MMSE varies considerably under the three sets of assumptions. While the mean MMSE score for the SOC arm falls to 10, which is considered to be the severe state of the disease, after 42 months, when assuming an indefinite treatment effect in Scenario 1 mean MMSE does not reach 10 until 81 months—a 39-month difference. Under the more conservative assumption laid out in Scenario 3, the differentiation from SOC drops to less than 9 months. Given the hierarchical assignment of disease severity across measures, the pattern observed with MMSE is similar for the NPI, DAD, and DS (Fig. 2b–d). The model predicts a difference of 3.01, -3.97, 11.44, and -1.57 in MMSE, NPI, DAD, and DS scores, respectively, at 18 months between DMA plus SOC relative to SOC alone, which is consistent with the expected results based on the assumed direct effect size of treatment. 3.3 Results Under Different Survival Scenarios Another major factor to consider is the impact of DMA on mortality. In the results presented, survival was assumed to

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be independent of treatment. The CERAD-based analyses indicated the possibility of a small (and not significant) association between the rate of decline in MMSE and mortality (hazard ratio [HR] 1.02 for every 1-point difference in the annual rate of MMSE change). In comparison to the reference case, applying this ratio in the model leads to a negligible change in QALYs gained versus SOC, and a modest reduction in cost savings of under £4,000 per patient (Table 3). Applying a much larger effect (HR 1.50) does lead to a 56 % increase in patient QALYs gained versus SOC, and a reduction in savings of just over 15 and 22 % from the payer and societal perspectives, respectively. Using a willingness-to-pay threshold of £30,000/ patient QALY yields a change in net monetary benefit from just under £55,000 and £32,000 with no mortality effect to just under £50,000 and £30,000 from the payer and societal perspectives, respectively. This suggests that concerns of improved survival with effective treatment options in AD may lead to an explosion in costs of dementia care (excluding cost of DMA) are unwarranted.

4 Discussion The simulation presented here provides a framework for evaluating the potential benefits of treatment with future DMAs. While the results for a hypothetical agent presented here suggest that DMAs could result in substantial savings, it should be stressed that the cost of DMA therapy is not considered and the effectiveness parameters in the simulation are purely illustrative. The simulation nevertheless demonstrates good predictive power on disease progression based on the interrelated changes in cognition, behavioural symptoms, function, and dependence among patients receiving SOC. In addition, the flexible model structure allows for the assessment of the impact of major sources of uncertainty surrounding the effectiveness of DMAs which are likely to be present at the time of market launch. These include a lack of data on the long-term effectiveness of DMAs and their impact on survival, which have a varying degree impact on the value of DMAs, as illustrated in the analyses. Although results are not presented in this paper, the simulation also has the capability to assess the impact of different scenarios on outcomes with respect to the residual effectiveness of DMAs following discontinuation, and their effect on specific severity domains (e.g. DMAs have direct benefit on cognition only versus on all four different domains). Information regarding the impact of these uncertainties on the value of DMAs will be valuable to decision makers when making reimbursement decisions for these treatments. In addition to the improved flexibility, this version of the model replaces both ADL and IADL measures used in the

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(a)

(b)

(c)

(d)

Fig. 2 Mean MMSE, NPI, DAD, and DS scores over time (under different treatment effect scenarios post 18 months). (a) MMSE; (b) NPI; (c) DAD; (d) DS. DAD Disability Assessment for Dementia

Scale, DMA disease-modifying agent, DS Dependence Scale, MMSE Mini-Mental State Examination, NPI Neuropsychiatric Inventory, SOC standard of care

previous model with DAD as this functional measure is more commonly used in clinical trials and observational studies to access patient’s ability to perform various ADLs, thus improving the applicability of the model. Furthermore, patient dependence (DS) was added to the model to better characterize disease severity as this measure provides additional valuable information on disease severity, distinctive from cognition, behaviour, and function. This allows the model to capture the benefits of DMAs in another meaningful way. In addition, DS has been shown to be correlated better with health care resource utilization and patient utilities [16–18], which should improve predictions on resource use by patients and patient quality of life. Despite all of the scenarios illustrated in this paper being hypothetical, the finding regarding the impact of improved survival with DMA is of interest. One recent analysis indicated that improved survival with DMA would result in an unfavourable impact on the cost per QALY outcomes due to substantial increases in costs of care [9]. This finding

differs from ours, which show only a modest impact on cost per QALY. The main difference in this particular finding between analyses is very likely due to the inclusion of cost of DMA in the analysis conducted by Skoldunger and colleagues, but not in ours. If the cost of DMA is considered, the impact of improved survival with DMA on the cost per QALY would probably be considerable, depending on the price assumed in the analysis. One particular difference in the way that survival is modelled between both models is worth highlighting as it could be a potential source of bias for estimating the benefit of DMA treatment in survival. The model developed by Skoldunger and colleagues used mortality estimates based on an association of mortality with baseline disease severity, but applied these associations based on a simulated cohort’s disease severity over time. The CERAD-based mortality functions used in this simulation do indeed show a strong association between baseline disease severity and mortality, but a much weaker relationship between subsequent changes in severity and survival. This is consequential

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Table 3 Health and cost outcomes when treatment effect on mortality is considered Discounted outcomes (per patient)

Reference case

Mortality – HR 1.02 for annual rate of MMSE decline

Mortality – HR 1.50 for annual rate of MMSE decline

DMA ? SOC

SOC

Net

DMA ? SOC

SOC

Net

DMA ? SOC

SOC

Net

8.07

8.07

0.00

7.45

7.43

0.02

8.43

8.11

0.31

6.70

6.70

0.00

6.21

6.19

0.02

6.96

6.70

0.26

Patient

4.40

4.08

0.32

4.12

3.80

0.32

4.57

4.07

0.50

Caregiver

5.36

5.33

0.03

4.97

4.93

0.04

5.57

5.33

0.24

Cholinesterase inhibitors/memantine

6,026

4,205

1,821

5,753

4,089

1,664

6,259

4,165

2,094

Medical ? community services

26,193

28,859

-2,666

23,539

25,964

-2,424

27,204

28,835

-1,630

Accommodation Informal care

50,054 157,332

70,577 180,668

-20,522 -23,335

44,419 143,602

63,191 165,238

-18,772 -21,635

52,281 164,049

70,908 180,8690

-18,626 -16,820

Direct

82,274

103,641

-21,366

73,713

93,245

-19,532

85,746

103,909

-18,162

Direct ? indirect

239,607

284,309

-44,702

217,315

258,483

-41,168

249,795

284,778

-34,982

Survival Undiscounted Discounted QALYs

Costs (£)

Total (£)

DMA disease-modifying agent, HR hazard ratio, MMSE Mini-Mental State Examination; QALYs quality-adjusted life-years, SOC standard of care

since the assumption that the relationship between baseline disease severity and mortality acts as a good proxy for current disease severity and mortality is fraught with potential bias. While the current model has a number of advancements from the previous version [3], the simulation still has a number of important limitations. First, due to lack of patient-level data from the trials of bapineuzumab (i.e. Study 301 and 302), we were unable to conduct model validation by setting the baseline severity of the model population equal to the observed baseline severity of subjects from these two trials. This is because the model creates the simulated population by sampling actual patient profiles from a different study. In addition, all of the baseline severity variables are correlated for individuals. Thus, it is not possible to create identical cohorts of both trials without actual patient baseline profiles from these two studies. It would be easier to interpret the results of model validation if such data are accessible. Moreover, as there are no long-term data, we are still unable to validate whether the model is capable of predicting change in disease severity beyond 18 months for patients receiving SOC. Further validation efforts with longer-term data are still necessary. Second, DAD and DS scores are assigned based on cross-sectional relationship with other measures of disease severity. Ideally, data would be available to allow for a more robust exploration of disease progression on these scales, and a more refined understanding of the relationship between progression on cognition and

behaviour on patient dependence and disability. This is particularly important for the prediction of DAD scores as the current equation tends to underestimate DAD scores. Although this issue can potentially be remedied by including additional variables in the equations (e.g. comorbidities), this would increase the complexity of the model and model data requirements. Third, cross-sectional data were also used to estimate time spent in institutional care and assign institutional care costs based on the percentage of patients who are institutionalized at the current severity level of the patient. A better approach for the assignment of this outcome, which has major cost implications, would be to develop a time to institutional care function which considers multiple patient attributes and, importantly, both initial disease severity and rate of disease progression. Finally, there is increased recognition that treatment with DMAs may have to be initiated at much earlier, presymptomatic stages of dementia in order to be effective [34–36]. While modifying the simulation to allow for earlier treatment and a potential delay to symptomatic disease is relatively straightforward, data with which to populate the model to account for such delays, although now emerging, are currently quite weak. As a recent review of AD models pointed out [8], many previous economic models in this area have been limited by restricting their analytic framework to a small number of health states, and have often projected severity using a single measure of disease severity [e.g. MMSE or clinical

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dementia rating (CDR)]. The current simulation, which builds on previous work whose objective was to overcome some of these modeling challenges, provides one framework for allowing more reliable quantification of the potential long-term consequences of interventions in AD. Acknowledgments The authors would like to thank Drs. Peter Neumann and Linus Jo¨nsson for their helpful feedback during the development of the model and the manuscript. Funding This study was sponsored by Pfizer Inc. and Janssen Alzheimer Immunotherapy Research & Development, LLC. Role of the funding source Gwilym Thompson and Maren Gaudig are employees of Janssen Alzheimer Immunotherapy Research & Development, LLC. Loretto Lacey was an employee of Janssen Alzheimer Immunotherapy Research & Development, LLC, at the time this research was conducted. Joel Bobula is an employee of Pfizer Inc. All were involved in the design and conduct of the study. Conflicts of interest Denis Getsios and Shien Guo are employees of Evidera, who were paid consultants to Pfizer Inc. and Janssen Alzheimer Immunotherapy Research and Development, LLC, in connection with the development of this manuscript. Nikhil Revankar and Peng Xu were employees of Evidera at the time this research was conducted. Author contributions Shien Guo, Dennis Getsios, Nikhil Revankar, Peng Xu, Gwilym Thompson, Loretto Lacey, Joel Bobula and Maren Gaudig participated in the design of the model, identification of data sources, conduct of data analyses, and implementing the design. Each author also contributed to the interpretation of data and results, drafting the manuscript, and approved the final version. Shien Guo will serve as guarantor for the overall content of the manuscript.

References 1. Personal Social Services Research Unit (PSSRU); Institute of Psychiatry. Dementia UK: a report into the prevalence and cost of dementia. London: Alzheimer’s Society; 2007. 2. Corbett A, Ballard C. New and emerging treatments for Alzheimer’s disease. Expert Opin Emerg Drugs. 2012;17(2):147–56. 3. Getsios D, Blume S, Ishak KJ, et al. Cost effectiveness of donepezil in the treatment of mild to moderate Alzheimer’s disease: a UK evaluation using discrete-event simulation. Pharmacoeconomics. 2010;28(5):411–27. 4. Guo S, Getsios D, Hernandez L, et al. Florbetaben PET in the early diagnosis of Alzheimer’s disease: a discrete event simulation to explore its potential values and key data gaps. Int J Alzheimers Dis. 2012;2012:548157. 5. Cohen JT, Neumann PJ. Decision analytic models for Alzheimer’s disease: state of the art and future directions. Alzheimers Dement. 2008;4(3):212–22. 6. Gustavsson A, Van Der Putt R, Jonsson L, et al. Economic evaluation of cholinesterase inhibitor therapy for dementia: comparison of Alzheimer’s disease and Dementia with Lewy bodies. Int J Geriatr Psychiatry. 2009;24(10):1072–8. 7. Stallard E, Kinosian B, Zbrozek AS, et al. Estimation and validation of a multiattribute model of Alzheimer disease progression. Med Decis Mak. 2010;30(6):625–38.

S. Guo et al. 8. Green C, Shearer J, Ritchie CW, et al. Model-based economic evaluation in Alzheimer’s disease: a review of the methods available to model Alzheimer’s disease progression. Value Health. 2011;14(5):621–30. 9. Skoldunger A, Johnell K, Winblad B, et al. Mortality and treatment costs have a great impact on the cost-effectiveness of disease modifying drugs in Alzheimer’s disease. Curr Alzheimer Res. 2012;10(2):207–16. 10. Getsios D, Blume S, Ishak KJ, et al. An economic evaluation of early assessment for Alzheimer’s disease in the United Kingdom. Alzheimers Dement. 2012;8(1):22–30. 11. Hartz S, Getsios D, Tao S, et al. Evaluating the cost effectiveness of donepezil in the treatment of Alzheimer’s disease in Germany using discrete event simulation. BMC Neurol. 2012;12:2. 12. Gelinas I, Gauthier L, McIntyre M, et al. Development of a functional measure for persons with Alzheimer’s disease: the disability assessment for dementia. Am J Occup Ther. 1999;53(5):471–81. 13. Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med. 2014;370(4):322–33. 14. Stern Y, Albert SM, Sano M, et al. Assessing patient dependence in Alzheimer’s disease. J Gerontol. 1994;49(5):M216–22. 15. Lenderking WR, Wyrwich KW, Stolar M, et al. Reliability, validity, and interpretation of the Dependence Scale in mild to moderately severe Alzheimer’s disease. Am J Alzheimers Dis Other Demen. 2013;28(8):738–49. 16. McLaughlin T, Buxton M, Mittendorf T, et al. Assessment of potential measures in models of progression in Alzheimer disease. Neurology. 2010;75(14):1256–62. 17. Zhu CW, Leibman C, McLaughlin T, et al. The effects of patient function and dependence on costs of care in Alzheimer’s disease. J Am Geriatr Soc. 2008;56(8):1497–503. 18. Zhu CW, Leibman C, McLaughlin T, et al. Patient dependence and longitudinal changes in costs of care in Alzheimer’s disease. Dement Geriatr Cogn Disord. 2008;26(5):416–23. 19. CERAD: Consortium to Establish a Registry for Alzheimer’s Disease. Available from: http://cerad.mc.duke.edu/Default.htm. Accessed 9 Jan 2013 20. Mohs RC, Doody RS, Morris JC, et al. A 1-year, placebo-controlled preservation of function survival study of donepezil in AD patients. Neurology. 2001;57(3):481–8. 21. Winblad B, Engedal K, Soininen H, et al. A 1-year, randomized, placebo-controlled study of donepezil in patients with mild to moderate AD. Neurology. 2001;57(3):489–95. 22. Feldman H, Gauthier S, Hecker J, et al. A 24-week, randomized, double-blind study of donepezil in moderate to severe Alzheimer’s disease. Neurology. 2001;57(4):613–20. 23. Rogers SL, Farlow MR, Doody RS, et al. A 24-week, doubleblind, placebo-controlled trial of donepezil in patients with Alzheimer’s disease. Donepezil Study Group. Neurology. 1998;50(1):136–45. 24. Rogers SL, Doody RS, Mohs RC, et al. Donepezil improves cognition and global function in Alzheimer disease: a 15-week, double-blind, placebo-controlled study. Donepezil Study Group. Arch Intern Med. 1998;158(9):1021–31. 25. Black SE, Doody R, Li H, et al. Donepezil preserves cognition and global function in patients with severe Alzheimer disease. Neurology. 2007;69(5):459–69. 26. Winblad B, Kilander L, Eriksson S, et al. Donepezil in patients with severe Alzheimer’s disease: double-blind, parallel-group, placebo-controlled study. Lancet. 2006;367(9516):1057–65. 27. Lacey LA, Jones RW, Trigg R, et al. Caregiver burden as illness progresses in Alzheimer’s disease (AD): association with patient dependence on others and other factors: results from the

Modeling Disease-Modifying Agents in AD Dependence in AD in England (DADE) study. Vancouver: AAIC; 2012. 28. King D, Knapp M, Romeo R, et al. Relationship between healthcare and social care costs and patient dependence on others as illness progresses in Alzheimer’s disease (AD): results from the Dependence in AD in England (DADE) study. Vancouver: AAIC; 2012. 29. Trigg R, Jones RW, Lacey LA, et al. Relationship between patient self- assessed and proxy-assessed quality of life (QoL) and patient dependence on others as illness progresses in Alzheimer’s disease (AD): results from the Dependence in AD in England (DADE) study. Vancouver: AAIC; 2012. 30. Jones RW, Lacey LA, Knapp M, et al. Relationship between patient dependence on others and clinical measures of cognitive impairment, functional disability and behavioural problems in Alzheimer’s disease (AD): results from the Dependence in AD in England (DADE) study. Vancouver: AAIC; 2012.

1139 31. British National Formulary (BNF) September 2012. Available from: http://www.bnf.org/bnf/index.htm. Accessed 9 Jan 2013; 32. National minimum wage rates. 2013. Available from: https:// www.gov.uk/national-minimum-wage-rates. Accessed 10 Mar 2013 33. Teipel SJ, Ewers M, Reisig V, et al. Long-term cost-effectiveness of donepezil for the treatment of Alzheimer’s disease. Eur Arch Psychiatry Clin Neurosci. 2007;257(6):330–6. 34. Di Carlo M, Giacomazza D, San Biagio PL. Alzheimer’s disease: biological aspects, therapeutic perspectives and diagnostic tools. J Phys Condens Matter. 2012;24(24):244102. 35. de la Torre JC. A turning point for Alzheimer’s disease? Biofactors. 2012;38(2):78–83. 36. Holland D, McEvoy LK, Desikan RS, et al. Enrichment and stratification for predementia Alzheimer disease clinical trials. PLoS One. 2012;7(10):e47739.

Evaluating disease-modifying agents: a simulation framework for Alzheimer's disease.

Considerable advances have been made in modeling Alzheimer's disease (AD), with a move towards individual-level rather than cohort models and simulati...
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