ABSTRACT
Introduction: The ongoing development of genomic medicine and the use of molecular and imaging markers in personalized medicine (PM) has arguably challenged the field of health economic modeling (HEM). This study aims to provide detailed insights into the current status of HEM in PM, in order to identify if and how modeling methods are used to address the challenges described in literature.
Areas covered: A review was performed on studies that simulate health economic outcomes for personalized clinical pathways. Decision tree modeling and Markov modeling were the most observed methods. Not all identified challenges were frequently found, challenges regarding companion diagnostics, diagnostic performance, and evidence gaps were most often found. However, the extent to which challenges were addressed varied considerably between studies.
Expert commentary: Challenges for HEM in PM are not yet routinely addressed which may indicate that either (1) their impact is less severe than expected, (2) they are hard to address and therefore not managed appropriately, or (3) HEM in PM is still in an early stage. As evidence on the impact of these challenges is still lacking, we believe that more concrete examples are needed to illustrate the identified challenges and to demonstrate methods to handle them.
1. Introduction
With the advent of personalized medicine (PM), the delivery of health care is shifting towards selecting and monitoring the best available treatment for each individual patient based on patient characteristics and diagnostic information. More specifically, ‘Personalized medicine seeks to improve stratification and timing of health care by utilizing biological information and biomarkers on the level of molecular disease pathways, genetics, proteomics as well as metabolomics’ [Citation1]. Personalized clinical processes typically involve multiple diagnostic tests and treatments over time, and the sequences of tests and treatments may differ between individual patients. Furthermore, treatment decisions are becoming increasingly preference sensitive [Citation2], due to the fact that there is no longer a ‘one-size-fits-all’ approach and the need to appraise the combined information from multiple sources, such as numerous test results, patients’ characteristics, and medical histories. Examples of personalized clinical processes include treatment targeting based on risk-stratification by patient characteristics and response monitoring using biomarkers [Citation3,Citation4]. The shift towards more interactive and dynamic, and therefore more complex, clinical treatment processes is associated with challenges not only regarding the delivery of health care, but also regarding the health economic evaluation of medical technologies [Citation5].
For instance, the use of randomized controlled trials (RCTs) for collecting evidence, and to inform health economic evaluations, is increasingly being questioned in a PM context [Citation5–Citation9]. RCTs are designed to draw conclusions on a population-level, while PM focusses on patient-level outcomes. The resulting data gaps characterize the increasingly common work setting in which evaluations of health care interventions need to be performed, initiating the need for accumulating other types of data, e.g. observational (big) data, or expert elicitation techniques [Citation10,Citation11]. Furthermore, new challenges occur with respect to analyzing trial data at the individual level and identifying and handling multiple subgroups [Citation11]. Therefore, health economic evaluation in the context of PM increasingly relies on modeling approaches and new methods, such as dynamic simulation modeling and the use of machine learning or other statistical approaches [Citation12].
However, PM also challenges the field of health economic modeling (HEM), as there is a need for models to accurately capture the interactions and dynamics present in personalized clinical processes [Citation13,Citation14]. Annemans et al. [Citation13] report on 10 methodological challenges that need to be considered when ‘designing and conducting robust model-based economic evaluations in the context of personalized medicine’. In general, these challenges raised by Annemans et al. focus towards the need to appropriately represent the dynamics of personalized treatment decisions with a wealth of diagnostics and surrounding uncertainty. More specifically, these challenges can mostly be translated into appropriate handling of the diagnostic performance of tests, combinations of tests, greater uncertainty due to more complex analysis, and data gaps. Phillips et al. [Citation15] complement these challenges by taking into account patients’ and physicians’ preferences, patients’ characteristics (such as age, gender, comorbidities, and medical history), and considering the impact of drug therapies and companion diagnostics simultaneously. Other challenges relate to the absence of implemented guidelines, criteria, and standards for the evaluation of new technologies in PM [Citation16,Citation17].
Given these methodological challenges, the appropriateness of the commonly used modeling method for HEM, i.e. Markov modeling, is being questioned [Citation14,Citation18], as this modeling approach may not be able to fully capture the complex treatment processes associated with PM [Citation19–Citation22]. Consequently, the use of more advanced modeling methods, such as discrete event simulation, agent based modeling, and system dynamics, might be more appropriate in this personalized context [Citation11,Citation23–Citation25].
It is unknown how current models address these challenges reported by experienced modelers and which modeling methods are being used to do so. Therefore, we assess the level of support for the methodological challenges described in literature by (1) identifying if and how the methodological challenges regarding modeling in the context of PM are being addressed, (2) exploring the different modeling approaches in PM in use to date, and (3) determining which alternative modeling methods may be appropriate to handle the specific issues in PM. Although several reviews have been published on modeling in PM [Citation26–Citation29], these focus on patient stratification using pharmacogenetics. We contribute to this literature by researching the challenges for HEM in PM in general, including patient stratification by other means than pharmacogenetics, e.g. the use of imaging technologies or risk stratification by patient characteristics.
2. Literature review
We performed a search in PubMed, employing primary search terms on Personalized Medicine and Precision Medicine combined with additional search terms on modeling and simulation, both in the title or the abstract of the publication. The search strategy was further specified by adding well-known key words used in health economics. The exact search algorithm can be found in Supplementary materials 1. The narrow primary search terms were required to include all sorts of patient stratification, e.g. pharmacogenetics, imaging technologies, and stratification by patient characteristics. No specific start date was applied and the search was updated until the 27 November 2015. The final sample was enriched by cross-referencing to include as many relevant publications as possible [Citation30].
To maintain the broad perspective of this review, and therefore prevent erroneous excluding of publications, only duplicates and animal studies were removed from the initial search results. The unique sample was first assessed based on title and abstract by one reviewer (KD). Next, screening the full text of the remaining publications resulted in inclusion into, or exclusion from, the final sample. Only publications relating to HEM studies in PM were included for full text screening. Publications were considered to meet the PM criterion when tests or prediction models were used to stratify patients into subgroups, for screening or targeting purposes, or when tests were used to monitor treatment effectiveness and thereby support patient-level treatment decisions. The refined sample was enriched by cross-referencing. Cross-references were included based on full text screening and until theoretical saturation was reached when inclusion of additional publications did not result in additional insights in a specific disease area, for example the screening for breast cancer. A second reviewer was consulted if it was unclear whether a publication should be included or excluded. The final decision to include or exclude a publication was made based on consensus between authors KD and HK. Reasons for exclusion were categorized into not mutually exclusive categories, as specified in Supplementary materials 2.
3. Scoring checklist representing the main challenges
In order to extract the data from the final sample, the general study characteristics and information on the used modeling method were summarized first. This includes the target disease, the description on how the treatment process was personalized, the used modeling method, the model structure, and the performed analyses. The treatment processes were characterized by the purpose of the stratification (screening, targeting, or monitoring) and whether the stratification was prognostic or predictive [Citation31]. Screening is referred to as the process of diagnosing a patient with a specific disease, whereas treatment targeting relates to selecting a treatment, from a set of treatment options that is expected to be most beneficial to a specific patient, based on patient-specific characteristics or diagnostic information. Treatment monitoring concerns the process of repeatedly assessing a patient’s response to the current treatment, in order to stop this treatment or switch to another treatment, if the patient is not benefiting from the current treatment.
Next, a checklist presenting the main methodological challenges for HEM in the context of PM was developed using the literature, where the publications by Annemans et al. [Citation13] and Phillips et al. [Citation15] served as the reference. After reviewing and classifying relevant papers, 10 different items in the checklist were used for the analysis of the final sample, based on the challenges derived from literature [Citation13,Citation14,Citation17,Citation20] (). The checklist was used to highlight the challenges in the final sample. For the first seven items in the checklist a positive result indicates that the authors addressed the challenge in the model, whereas for the remaining items eight to ten a positive result indicates that the authors identified that specific challenge and reported this in the publication. When a challenge was addressed or identified by the authors, this was scored as ‘+’, when challenges were not addressed or identified this was scored as ‘–’. If applicable, additional information on the extent to which challenges were addressed or mentioned was recorded.
Table 1. Checklist presenting the main challenges described in literature and used for analysis of the final sample. A positive score on challenges 1–7 indicates that the corresponding challenge is addressed in the model presented in the publication. A positive score in challenges 8–10 indicates that the authors identified the corresponding challenge and reported on the challenge in the publication. Negative scores indicate that the corresponding challenge is not addressed or identified and reported by the authors.
4. Results
The search strategy yielded 2245 publications on PubMed (). From this initial sample, five duplicates and 775 animal studies were excluded. The abstracts of all remaining 1465 publications were read and finally resulted in the exclusion of 1442 publications. These excluded publications were predominantly publications of a qualitative nature (n = 534, 37%) and experimental nature (n = 421, 29%). Several other excluded publications were publications on mathematical or statistical models developed to support medical decision-making (n = 168, 12%). A total of 128 publications were excluded from the final sample because they covered adjacent topics, such as personalized speaking tools or personalized computer systems (9%). The detailed reasons for exclusion are presented in Supplementary materials 2. After exclusion based on title and abstract, 23 publications remained. From these publications the full text was screened, resulting in five more exclusions from the sample for different reasons (i.e. papers presenting a biomedical model [Citation32], a prediction model [Citation33], or that did not include diagnostics or personalized risk estimations [Citation34–Citation36]). Analyzing the full text of the included articles resulted in an enrichment of the sample with 13 additional publications by cross-referencing. Many candidate cross-reference articles were not included due to the absence of diagnostics or personalized risk estimations [Citation37–Citation46]. Finally, a total of 31 publications were available for analysis [Citation47–Citation77].
Figure 1. Graphical representation of the search- and selection process.
The final sample concerned studies in various disease areas, including oncology (n = 17), cardiovascular disease (n = 5), human immunodeficiency virus (n = 2), hepatitis C virus (n = 2), Alzheimer’s disease (n = 1), neonatal disease (n = 1), rheumatoid arthritis (n = 1), depressive disorder (n = 1), and type 2 diabetes (n = 1) (). Most studies in the sample stratify patients for targeted therapy (n = 19, 61%), of which four studies combine treatment targeting with response monitoring and one article combines treatment targeting with screening. In total 13 studies stratify patients for screening purposes (42%). No articles used tests for response monitoring only. This indicates that patient stratification is used only for predictive purposes in 45% of the included publications (n = 14), for prognostic purposes in 39% of the included publications (n = 12), and for both predictive and prognostic purposes in 16% of the included publications (n = 5).
Table 2. Summary of the study characteristics of the final sample of publications.
The most frequently used modeling methods are decision tree modeling (n = 15, 48%) and Markov modeling (n = 12, 39%), which are often combined (n = 6, 19%). Other frequently used modeling methods are microsimulation modeling (n = 4, 13%), and mathematical modeling by equations (n = 3, 10%). Other observed modeling methods include partially observable Markov decision process (POMDP) modeling, deterministic dynamic compartment modeling (DDCM), and discrete event simulation (DES). Whereas decision trees, Markov models, and DDCM [Citation78] simulate cohorts of patients, POMDP modeling [Citation79], microsimulation modeling [Citation80], and DES [Citation81] are used for patient-level simulations. As presents, there is not only an increase in publications over time, but also an increase in the use of modeling methods other than decision tree analysis and Markov modeling. Regarding the motivation of the used modeling methods, one article mentions the straightforward interpretation of a decision tree as reason for its use [Citation49], whereas another article, using a Markov model, suggests that it may not be appropriate to model a screening process as a homogenous process [Citation59].
Table 3. Summary of the distribution of the publications and the use of cohort modeling3 or alternative modeling methods over time.
shows that not all methodological challenges as included in the checklist were systematically addressed or identified and reported in the final sample. The challenges regarding physicians’ preferences, disease-specific outcome measures, greater uncertainty, and absence of guidelines are addressed or identified and reported in at most five of the included publications. Yet, the challenges regarding the patients’ preferences, diagnostic performance, multiple tests, companion diagnostics, and the lack of evidence are more frequently addressed or identified and reported (in at least 10 publications). However, the latter result needs to be perceived in relative terms, as the extent to which challenges are addressed varies considerably between studies. For example, several studies modeled multiple tests, but assumed a fixed sequence of these tests. Although in practice, test sequences might be dynamic and thereby influence the diagnostic performance of the process as a whole. Furthermore, of the 19 publications in which a lack of evidence is identified, these data gaps are not purely caused by stratification in 37% of the cases (n = 7).
Table 4. Results of the analysis on whether the challenges for HEM in PM are addressed or identified and reported in the final sample of publications.
5. Conclusion
From the articles that were included, it can be concluded that patient stratification, using diagnostics or risk models, is mostly performed for treatment targeting or screening purposes. Only in few of the included publications diagnostics are used for treatment monitoring. Overall, the most frequently observed modeling methods are decision tree analysis and Markov modeling, which is in line with literature [Citation14,Citation18]. However, an increase in the use of more advanced modeling methods is observed. Finally, the results show that the methodological challenges for HEM in the context of PM described in literature are not (yet) frequently addressed or identified and reported.
6. Expert commentary
The findings from the literature study can have different implications. For instance, they may indicate that the impact of the challenges for HEM in PM is less severe than expected, that the challenges are hard to address and there is a lack of methods to overcome the challenges, or that we are still in an early stage of personalization and that the complexity of Personalized Medicine, therefore, is not yet a major issue. The observation that the most frequently used modeling methods are still cohort models may also indicate that we are still in a premature stage. Currently, it seems to be sufficient to stratify patients into relatively large subgroups, as there is also no regulatory incentive to further personalize these models.
However, treatment decisions are becoming increasingly complex, involving multiple biomarkers or panels of markers from next-generation sequencing to feed a sequence of clinical decisions. In this context, patient-level models are likely to become standard, as cohort models can no longer reflect the dynamic treatment processes in heterogeneous subgroups of individuals and may lead to biased estimates of the impact of new technologies. Representing the variation in patients’ clinical pathways is particularly problematic for cohort models, as this would either require numerous separate cohort models, representing all plausible sequences, or one very large model including all these sequences. In both cases, however, models will become substantially complex to manage and models’ cognitive ease will decrease dramatically. Conversely, more advanced modeling methods can represent the dynamics of individual pathways in a straightforward and more natural manner.
Policymakers will need to incentivize the use of appropriate modeling approaches to accurately represent clinical practice and accept that this might result in more complex health economic models, possibly at the expense of these models’ cognitive ease. It is necessary to accept this increase in complexity, as health economic models are likely to become biased and may lose their value in supporting decision-making when they are not matched with the dynamics and complexity of current and future clinical processes.
That the challenges are present, but may be hard to address using current approaches, is illustrated by the fact that many authors do recognize the challenges described in literature, but do not actually address them in the corresponding models. For example, the relevance of shared decision-making is highlighted in several publications, as authors argue the need for physicians to provide patients with personalized information on expected treatment outcomes and to involve these patients in decision-making [Citation36,Citation52,Citation70,Citation72,Citation75]. The observation that this interactive and complex decision-making process is not yet integrated into the corresponding models, however, illustrates the challenge to further personalize these models. Another example can be found in breast cancer screening, as individualization of the screening process is considered very important [Citation37,Citation82], whereas all included models are still cohort-based.
Although patient-level simulation seems appropriate, the present review insufficiently can conclude the usefulness of modeling techniques beyond cohort models to adequately represent personalized clinical processes. Yet, it seems obvious that modeling patients on an individual level is desirable and advanced methods, such as microsimulation modeling and discrete event simulation, may be essential. However, to utilize the full potential of these advanced modeling methods, more evidence, for example on subgroup-specific event rates, costs, and quality of life, may be required compared to less advanced methods. Since the results show that evidence gaps are a frequently experienced challenge, the use of advanced methods may therefore not always be feasible to address the challenges associated with PM.
The results of this study provide insights into the level of support in modeling practice for the methodological challenges described in literature. These findings can be used to focus development of a framework for HEM in this context, as they indicate which challenges require additional guidance the most. Additional further research is recommended to investigate the impact of the challenges for HEM in PM when truly personalized clinical processes are being modeled and whether this will affect decisions made by policy makers. Furthermore, whether the use of advanced modeling methods indeed can solve at least some of these challenges, needs further investigation while being weighed against the increased complexity of these models.
Our study has certain limitations. First of all, it is interesting to note that the number of papers that were finally selected from the initial search results is relatively low, whereas PM is a growing field of research and the health economic issues related to various targeted drugs receive global recognition. This might be partly caused by the strict inclusion criteria required for the research objectives and by the fact that we only consulted one database. However, we minimalized the effect of this limitation by cross-referencing and found that the results for the publications obtained by cross-referencing to be in line with the publications in the initial sample. Furthermore, underreporting may have resulted in the absence of mentioned challenges for HEM in PM and the lack of motivation for the used modeling methods. This also relates to the limited space that is available in peer-reviewed journals for reporting methods and results.
7. Five-year view
The continuing personalization of clinical pathways highlights the need for using more advanced modeling methods to accurately represent the complex context of clinical practice and, therefore, to be meaningful for supporting decision-making. If not already, regulatory agencies will need to critically review the modeling methods that are being used to translate clinical practice into health economic models, initiating the need for using appropriate modeling methods. Furthermore, efforts to illustrate and guide the use of more advanced modeling methods, such as discrete event simulation and system dynamics modeling, will strengthen and spread the knowledge base of these methods, increasing their use for the evaluation of health care interventions, also by clinical experts. Finally, the pharmacoeconomic and clinical communities will realize that using more advanced modeling methods does not necessarily result in more complex models. On the contrary, using more advanced modeling methods will enable models to continue being valuable for what they are needed for, which is supporting decisions in an increasingly complex environment.
Key issues
Health economic models in personalized medicine are to a large extend based on cohorts of patients instead of individual patients.
Some of the specifics of Health Economic Modeling in Personalized Medicine, such as patient-level models and the representation of the dynamics and sequence of preference-sensitive clinical decisions, are still not addressed appropriately.
This may indicate that the impact of these challenges is less severe than expected, that the challenges are hard to address and there is a lack of methods to overcome the challenges, or that we are still in an early stage of personalization and that the complexity of Personalized Medicine, therefore, is not yet a major issue.
Further research should focus on identifying the extent of these challenges when truly personalized processes are being modeled and the added value of advanced modeling methods in this context.
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Contribution statement
The research design, including the search strategy and inclusion criteria, was developed as a combined effort of all authors. The search and primary analysis was performed by KD in close cooperation with HK and under the supervision of MIJ. The initial manuscript was drafted by KD and revised by HK and MIJ. The overall guarantor of this study is MIJ.
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