1 McMaster University, Hamilton Ontario, Canada, 2 University of Sheffield, The Jessop Hospital for Women, Sheffield, UK, 3 National Center of Scientific Research (CNRS), Villeurbanne, France, 4 Texas Fertility Center, Austin, Texas, USA, 5 Carlanderska Hospital, Gothenburg, Sweden, 6 IWK Grace Health Center, Halifax University, Canada and 7 Serono International SA, Geneva, Switzerland
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Abstract |
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Key words: assisted reproduction/cost-effectiveness/mathematical modelling/recombinant FSH/urinary FSH
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Introduction |
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The increased numbers of couples requiring advanced fertility treatment and its attendant financial implications on healthcare systems, emphasizes the importance of demonstrating that clinical interventions are cost-effective. In the UK, for example, the provision of services using ART is complex, with only some regions providing health care through the National Health Service (NHS). Increasingly, the cost of ART is being assumed in large measure by the infertile couple. Therefore, it is important to ensure that treatments are both effective with respect to attaining pregnancy and cost-effective. This objective is particularly relevant when multiple treatment cycles are needed to achieve a successful pregnancy.
Ideally, efficacy analyses of ART treatment regimens should be based on prospective controlled clinical trials. The usefulness of clinical trials in determining the efficacy of a single drug or a procedure is widely recognized. However, their value is limited for determining the cost-effectiveness of complex situations such as a typical multi-step, multi-cycle ART intervention. To date, very few studies of clinical effectiveness have attempted to evaluate cost outcomes in such complex treatment situations. While retrospective surveys are informative, they are fraught with bias and are inadequate for pharmacoeconomic analyses because complete data for all subjects are usually unavailable. Furthermore, a randomized trial to evaluate cost-effectiveness in such a complex treatment as ART would require considerable resources, an enormous number of patients and a lengthy follow-up to provide any meaningful information.
An effective method of overcoming these limitations to undertaking a pharmacoeconomic assessment of ART treatment, is to employ modelling and computer-simulation. Modelling is a powerful method for repetitively simulating and testing the conditions and outcomes of a complex treatment programme. It allows the use of data from several sources such as randomized controlled trials, national IVF registries and expert opinions. Computer-based models are constructed with the use of established mathematical simulation techniques. Markov modelling is also increasingly used in economic evaluations of other health related issues (Briggs and Sculpher, 1998). In a Markov model, patients are at any time in a specific `health state' that indicates their position in their treatment cycle, e.g. ovarian stimulation, oocyte retrieval, fertilization and so on. Patients progress along an ART treatment cycle through a series of health states, each with an associated probability (a `transition' probability) for that particular outcome, during a complete ART cycle, e.g. failed fertilization. Transition probabilities for all stages of a treatment cycle (e.g. cancelled ovum retrieval, successful recovery of oocytes, fertilization of oocytes, etc.) can be obtained from available clinical trials. This model therefore provides a `natural' framework for constructing simulations of temporally changing situations, such as one encounters in ART, while also assessing the economic and clinical effects of the intervention being studied.
Markov modelling has been used to determine the cost-effectiveness of medical treatments in a variety of clinical conditions (Col et al., 1997; Nuitjten et al., 1998; Palmer et al., 1998
; Mantovani et al., 1999
; van Loon et al., 2000
). The cost-effectiveness of recombinant (r)FSH has been compared with urinary (u)FSH in the treatment of infertile women in Greece (van Loon et al., 2000
) and in Italy (Mantovani et al., 1999
), and in both of these studies it was demonstrated that the use of rFSH was most cost-beneficial. However, the results of these studies have limited utility because they were based on data from a small number of clinical trials and the models contained relatively few health states. Additionally, although the mean transition probabilities of the models were agreed upon by a panel of experts, critical information on the distribution of these probabilities was not provided. Such omissions result in uncertainty about the interpretation of such modelling outcomes and, despite the use of sensitivity analyses, the lack of confidence limits around the estimates of the outcomes makes it impossible to determine whether differences between treatments are statistically significant. The purpose of the present study, therefore, was to develop a detailed model of ART that could incorporate a large number of health care states, thereby providing outcome estimates (and their ranges) in order to effectively compare the cost of infertility therapy with rFSH (Gonal F®, Serono) and uFSH (Metrodin HP®, Serono) in the UK.
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Materials and methods |
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The general architecture of this Markov model for ART is shown in Figure 1. The first cycle is a complete treatment cycle (CTC) in which fresh embryos are transferred. Should the first cycle not result in pregnancy, a second cycle could involve either another CTC with fresh embryo transfer or a non-stimulated cycle with frozen embryo transfer. Similarly, if a third attempt is needed, the third cycle could also involve either a CTC with fresh embryos or a frozen embryo transfer. Figure 1
represents, albeit in a simplified fashion, the possible CTC and frozen embryo transfer combinations that can occur with up to three cycles of embryo transfer in ART after rFSH stimulation. An identical representation exists for ovarian stimulation with uFSH.
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There are six health states for each of rFSH and uFSH for frozen embryo transfer after the first health state, which is frozen embryo thaw. The subsequent health states are: embryo survival (yes or no); pregnancy (yes or no); and ongoing pregnancy or miscarriage. Thus, the total number of health states in this study of three cycles is:
CTC + (CTC + frozen embryo transfer) + (CTC + frozen embryo transfer + CTC + frozen embryo transfer) = 61 + (61 + 13) + (61 + 13 + 61 + 13) = 283. The total number of health states considered in this model reflects its comprehensiveness.
The insert in Figure 1 illustrates the decision pathway that includes the 61 health states involved in a CTC. Similar decision pathways (decision trees), which for reasons of brevity are not illustrated, exist for cycles two and three, further demonstrating the complex nature of this model.
Transition probabilities for each health state were estimated for both rFSH and uFSH protocols. The means of these probabilities were calculated from data derived from several sources, including a meta-analysis of randomized controlled trials comparing the two gonadotrophins (Daya and Gunby, 1999), national IVF/ICSI registries (FIVNAT, 1999
; HFEA, 1999
; SART `97, 1999
) and clinical expert consensus opinion when published data were unavailable. These probabilities are shown in Table I
.
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Figure 2 illustrates the distribution profile of the transition probabilities for the health state `cancelled ovum retrieval' for both rFSH and uFSH. The shape of both distributions is Gaussian (normal curve) and is centred about the mean value, with a dispersion that reflects the calculated SD. The expert clinical panel validated the normality of the distributions. Furthermore, mathematical estimations of proportions are always normal, thereby allowing the construction of confidence intervals. The mean ± SD for all the transition probabilities are shown in Table I
.
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As with any other model, a number of assumptions were made in the construction of this model. These assumptions were: (i) complete therapy was defined as participation in up to three cycles with embryo transfer, each cycle constituting a treatment cycle; (ii) the transition probabilities for each treatment cycle remained unchanged; (iii) the distribution of all transition probabilities was normal; (iv) there were no spontaneous, treatment-independent pregnancies; (v) the main outcome of the study was clinical pregnancy (intrauterine pregnancy at 12 weeks gestation confirmed by ultrasound); (vi) the gonadotrophin (i.e. rFSH or uFSH) used for ovarian stimulation was randomly assigned and remained the same in subsequent treatment cycles.
A comprehensive cost analysis accounting for all health states in the ART model was undertaken and included costs incurred for ovarian stimulation, monitoring, oocyte retrieval, laboratory procedures, luteal phase support, pregnancy determination and cryopreservation. The costs considered in this study were for the year 2000 and were obtained from the NHS clinic tariff from one clinic located in Sheffield, UK, which is a mediumlow price clinic in the provision of ART services. The price of drugs are the list prices for the year 2000 (Table II).
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Results |
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Cost effectiveness
The cost per ongoing pregnancy was £5906 (± 455) with rFSH (Gonal-F®) and £6060 (± 547) with uFSH (Table III). This difference of £154 between treatments was statistically significant in favour of rFSH (P < 0.0001) and represented a reduction in overall treatment cost of 2.5%. The cost-effectiveness distribution for each FSH preparation can be seen in Figure 4
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Mean number of cycles per success
This model also allows one to calculate the total number of treatment cycles needed to obtain a successful outcome (i.e. ongoing pregnancy). Taking into consideration the numbers of cycles terminated before oocyte retrieval, and taking fresh and frozen embryo transfer cycles together, the mean number of cycles required to achieve one ongoing pregnancy was found to be 4.49 with rFSH and 4.80 with uFSH (Table III).
When these data were further subdivided into fresh and frozen embryo transfer cycles, it was found that with rFSH the mean number of fresh embryo transfer cycles for one successful outcome was 3.83 compared with 4.29 with uFSH, and for frozen embryo transfer cycles the mean numbers were similar at 0.66 and 0.51 respectively.
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Discussion |
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Ongoing pregnancy was chosen as the outcome of interest because the meta-analysis used to derive clinical probabilities for the model contained no information on live births or premature delivery, but used clinical pregnancy (at 12 weeks) gestation as the primary endpoint. Furthermore, the expert panel validated the fact that there was no evidence to suggest that the choice of recombinant or urinary gonadotrophin would have an effect on obstetrical outcomes.
One reason for the superiority of rFSH is its higher level of clinical response. The retrieval of a higher number of mature oocytes when rFSH was used for ovarian stimulation compared with uFSH has been reported in many studies (Fisch et al., 1995; Out et al., 1995
, 1997
; recombinant human FSH Study Group, 1995
; Bergh et al., 1997
; Manassiev et al., 1997
; Khalaf et al., 1998
; Franco et al., 1999
; Frydman et al., 2000
; Lenton et al., 2000
). Overall, these comparative studies demonstrated that rFSH has statistically significant advantages in terms of efficacy, resulting in higher numbers of follicles aspirated and oocytes retrieved, even though the daily dose of gonadotrophins administered was lower and the treatment period shorter (Bergh et al., 1997
; Manassiev et al., 1997
; Khalaf et al., 1998
; Franco et al., 1999
; Lenton et al., 2000
). Consequently, the total consumption of gonadotrophins was much lower with rFSH than with uFSH. As a result, the likelihood of having more embryos when rFSH is used is much greater, thereby providing more opportunities for cryopreservation of surplus embryos, which can be used for subsequent, less costly frozen embryo transfer cycles. Additionally, a meta-analysis evaluating the clinical outcomes when the two gonadotrophins were compared demonstrated a significantly higher pregnancy rate per cycle started with the use of rFSH (Daya and Gunby, 1999
).
From an economic evaluation point of view, rFSH was clearly more cost-effective than uFSH, but did not dominate this alternative, because even though its effectiveness was higher, its cost was also higher. The incremental cost-effectiveness analysis demonstrated that for each additional clinical pregnancy, the overall cost increment was £4148. However, there would be savings generated from using rFSH because fewer numbers of cycles would be required to attain one pregnancy compared with uFSH. Furthermore, since social costs (non-medical costs, such as patients' time off work, travel, parking and so on) were not incorporated into the model, the cost differences might, in reality, be greater. The shorter duration of treatment with rFSH and the requirement of fewer cycles to achieve pregnancy might have had a greater effect in reducing total costs further, suggesting that the cost-effectiveness of rFSH may be of an even greater magnitude.
The use of clinical tariff from the mediumlow price clinic in Sheffield, UK is a conservative approach since a more expensive clinic with higher tariffs would result in a increased difference between rFSH and uFSH due to the fact that use of urinary gonadotrophins requires more cycles to achieve success as a consequence of lower pregnancy rates.
The robustness of the pharmacoeconomic model that generated these findings deserves some discussion. The general robustness of a model depends on both the robustness of the structure (the model) and the robustness of the data. The structure of the model is robust because all assumptions were validated and considered as acceptable by the clinical expert panel. The data used are robust because they are based on methodologically sound studies (randomized controlled trials) and because we performed a high number of Monte Carlo simulations that reduced the magnitude of the standard deviations. Current commercial software packages are not as powerful when it is necessary to manage a very large number of health states and the quality and characteristics of the random number generator is not disclosed/described. Furthermore, our choice of the random number generator, which is necessary for the Monte Carlo method, guarantees that there will be no selection bias from repetition because the sequences of random numbers used are unique.
The potential limitation of the model is based on the assumption that patients proceeding through second and/or third cycles would be treated with the same drug combinations as in the first cycle (a situation that may vary in practice) and the agreement by the expert panel that there were no reasons to change the value of transition probabilities for the second and third cycles because there is no evidence to suggest that this assumption is invalid.
In other studies of the relative cost-effectiveness of rFSH and uFSH in ART, classical sensitivity analyses have been performed to attempt to account for the variance of inputted data and test the robustness of the results (Van Loon et al., 2000). Such sensitivity analyses consist of sequentially altering the value of an important parameter, e.g. a mean transition probability, to observe the effect of this change on the overall outcomes. Such analyses are usually restricted to the effects produced by altering the values of only a few key parameters. Consequently, the choice of the examined parameter and the degree to which the parameter is altered are subject to selection bias. A further limitation is that parameters are altered one at a time to test the effect on the results, whereas in reality, there is variance around all these variables simultaneously. These limitations of classical sensitivity analyses consequently led to the development of probability sensitivity analysis using the Monte Carlo technique previously described. In the present study, probability distributions were used at every decision point to account simultaneously for variation in all the variables. The Monte Carlo method uses these distributions to produce confidence intervals around the estimates of the outcomes and, therefore, provides much more precise and useful information than do classical sensitivity analyses.
It is important to note that the findings of this cost-effectiveness study are valid only when considering rFSH and uFSH within the scenario of treatment of infertility with ART. Simple substitution of the cost of gonadotrophins other than those used in this study would fail to derive an accurate estimate of the cost-effectiveness of the substituted drug. This is because the present model utilized product specific data, i.e. transition probabilities, and in order to test other drugs reliably the model would have to be `reprogrammed' using the correct distribution probabilities and costs associated with the substituted drugs. Furthermore, although the mean figures used in the model come from a variety of sources, the use of the panel of experts who were able to review the range of costs in the UK implies that the distribution probabilities are finely honed to the situation in the UK. Similar exercises using this model can be undertaken to evaluate the cost-effectiveness of the gonadotrophins in ART in other countries.
In conclusion, a robust, statistically powerful, pharmacoeconomic model of the procedures used in the conduct of a typical infertility treatment programme using ART in the UK has been constructed. Running Monte Carlo simulations on a Markov cohort of patients has clearly demonstrated that use of rFSH for ovarian stimulation in ART and embryo transfer is significantly more cost-effective than uFSH. This model is currently being developed to assess the cost-effectiveness of other drugs used in ART.
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Acknowledgements |
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Notes |
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* Colin Howles is Vice President, Reproductive Endocrinology, Serono, International SA and Ariel Beresniak is Corporate Director Pharmacoeconomics, Serono International, SA.
Submitted on December 29, 2000; July 10, 2001
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References |
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accepted on September 22, 2001.