A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

<p><b>Introduction:</b> 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.</p> <p><b>Areas covered:</b> 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.</p> <p><b>Expert commentary:</b> 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.</p

Procedure and test tariffs (2014).

<p>Procedure and test tariffs (2014).</p

Population characteristics.

<p>Population characteristics.</p

Usage of diagnostic procedures in diagnostic pathway.

<p>Usage of diagnostic procedures in diagnostic pathway.</p

Treatment of the primary tumour against the treatment of SP tumours.

<p>* indicates a significant difference. Abbreviations: SP = second primary.</p><p>Treatment of the primary tumour against the treatment of SP tumours.</p

Multivariable Cox regression analysis after multiple imputation.

<p>Abbreviations: HR = hazard rate, CI = Confidence Interval, DFI = disease-free interval, LR = local recurrence, RR = regional recurrence, LRR = locoregional recurrence, SP = second primary, ER = oestrogen receptor, PR = progesterone receptor, na = not applicable.</p><p>* indicates most influencing factors.</p><p>Multivariable Cox regression analysis after multiple imputation.</p

Patient and primary tumour characteristics, for LRRs stratified by length of the DFI and tested for differences between the DFI groups.

<p>Abbreviations: LRR = locoregional recurrence, SP = second primary, DFI = disease-free interval, ER = oestrogen receptor, PR = progesterone receptor, Her2-Neu = human epidermal growth factor receptor 2</p><p>Patient and primary tumour characteristics, for LRRs stratified by length of the DFI and tested for differences between the DFI groups.</p

Kaplan-Meier curves divided in short, medium and long DFIs for (A) overall survival in patients with a LRR, (B) overall survival in patients with a SP, (C) survival after recurrence in patients with a LRR, and (D) survival after recurrence for patients with a SP tumour.

<p>Abbreviations: DFI = disease-free interval, LRR = locoregional recurrence, SP = second primary.</p

Primary treatment and recurrence characteristics, for LRRs stratified by length of the DFI and tested for differences between the DFI groups.

<p>Abbreviations: LRR = locoregional recurrence, SP = second primary, LR = local recurrence, RR = regional recurrence, na = not applicable.</p><p>Primary treatment and recurrence characteristics, for LRRs stratified by length of the DFI and tested for differences between the DFI groups.</p

Treatment of the primary tumour against the treatment of LRRs.

<p>* indicates a significant difference Abbreviations: LRR = locoregional recurrence.</p><p>Treatment of the primary tumour against the treatment of LRRs.</p