Aim Decreased cancer incidence and reported changes to clinical management indicate that the COVID‐19 pandemic has delayed cancer diagnosis and treatment. This study aimed to develop and apply a flexible model to estimate the impact of delayed diagnosis and treatment on survival outcomes and healthcare costs based on a shift in the disease stage at treatment initiation. Methods A model was developed and made publicly available to estimate population‐level health economic outcomes by extrapolating and weighing stage‐specific outcomes by the distribution of stages at treatment initiation. It was applied to estimate the impact of 3‐ and 6‐month delays based on Australian data for stage I breast cancer, colorectal cancer, and lung cancer patients, and for T1 melanoma. Two approaches were explored to estimate stage shifts following a delay: (a) based on the relation between time to treatment initiation and overall survival (breast, colorectal, and lung cancer), and (b) based on the tumor growth rate (melanoma). Results Using a conservative once‐off 3‐month delay and considering only shifts from stage I/T1 to stage II/T2, 88 excess deaths and $12 million excess healthcare costs were predicted in Australia over 5 years for all patients diagnosed in 2020. For a 6‐month delay, excess mortality and healthcare costs were 349 deaths and $46 million over 5 years. Conclusions The health and economic impacts of delays in treatment initiation cause an imminent policy concern. More accurate individual patient data on shifts in stage of disease during and after the COVID‐19 pandemic are critical for further analyses.

No 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. ARTICLE HISTORY

Blood-based liquid biopsies are considered a new and promising diagnostic and monitoring tool for cancer. As liquid biopsies only require a blood draw, they are non-invasive, potentially more rapid and assumed to be a less costly alternative to genomic analysis of tissue biopsies. A multi-disciplinary workshop (n = 98 registrations) was organized to discuss routine implementation of liquid biopsies in cancer management. Real-time polls were used to engage with experts’ about the current evidence of clinical utility and the barriers to implementation of liquid biopsies. Clinical, laboratory and health economics presentations were given to illustrate the opportunities and current levels of evidence, followed by three moderated break-out sessions to discuss applications. The workshop concluded that tumor-informed assays using next-generation sequencing (NGS) or PCR-based genotyping assays will most likely provide better clinical utility than tumor-agnostic assays, yet at a higher cost. For routine application, it will be essential to determine clinical utility, to define the minimum quality standards and performance of testing platforms and to ensure their use is integrated into current clinical workflows including how they complement tissue biopsies and imaging. Early health economic models may help identifying the most viable application of liquid biopsies. Alternative funding models for the translation of complex molecular diagnostics, such as liquid biopsies, may also be explored if clinical utility has been demonstrated and when their use is recommended in multi-disciplinary consensus guidelines.

No abstract

Background: Decreased cancer incidence and reported changes to clinical management indicate that the COVID-19 pandemic will result in diagnostic and treatment delays for cancer patients. We aimed to develop a flexible model to estimate the impact of delayed diagnosis and treatment initiation on survival outcomes and healthcare costs based on a shift in the disease stage at treatment initiation. Methods: The stage-shift model estimates population-level health economic outcomes by weighting disease stage-specific outcomes by the distribution of stages at treatment initiation, assuming delays lead to stage-progression. It allows for extrapolation of population-level survival data using parametric distributions to calculate the expected survival in life years. The model was demonstrated based on an analysis of the impact of 3 and 6-month delays for stage I breast cancer, colorectal cancer and lung cancer patients, and for T1 melanoma, based on Australian data. In the absence of patient-level data about time to stage progression, two approaches were explored to estimate the proportion of patients that would experience a stage shift following the delay: 1) based on the relation between time to treatment initiation and overall survival (breast, colorectal and lung cancer), and 2) based on the tumour growth rate (melanoma). The model is available on http://stage-shift.personex.nl/. Results: A shift from stage I to stage II due to a 6-month delay is least likely for colorectal cancer patients, with an estimated proportion of 3% of the stage I patients diagnosed in 2020 progressing to stage II, resulting in 11 excess deaths after 5 years and a total of 96 life years lost over a 10-year time horizon. For breast and lung cancer, progression from stage I to stage II due to a 6-month delay were slightly higher at 5% (breast cancer) and 8% (lung cancer), resulting in 25 and 43 excess deaths after 5 years, and 239 and 373 life years lost over a 10-year time horizon, respectively. For melanoma, with 32% of T1 patients progressing to T2 disease following a 6-month delay, the model estimated 270 excess death after 5 years and 2584 life years lost over a 10-year time horizon. Conclusions: Using a conservative 3-month delay in diagnosis and treatment initiation due to the COVID-19 pandemic, this study predicts nearly 90 excess deaths and $12 million excess healthcare costs in Australia over 5 years for the in 2020 diagnosed patients for 4 cancers. If the delays increase to 6 months, excess mortality and cost approach nearly 350 deaths and $46 million in Australia. More accurate data on stage of disease during and after the COVID-19 pandemic are critical to obtain more reliable estimates.

BackgroundParametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes.MethodsTwo approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study.ResultsAccounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes.ConclusionsModelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.Electronic supplementary materialThe online version of this article (doi: 10.1186/s12874-017-0437-y) contains supplementary material, which is available to authorized users.

Background. Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches. Methods. Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling approach was applied to uncensored individual patient data in a simulation study and a case study in colorectal cancer. Their performance was assessed in terms of relative event incidence difference, relative absolute event incidence difference, and relative entropy of time-to-event distributions. Differences in health economic outcomes were also illustrated for the case study. Results. In the simulation study, the ESPD and MDR approaches outperformed the ESD and UDR approaches, in terms of both event incidence differences and relative entropy. Disease pathway and data characteristics, such as the number of competing risks and overlap between competing time-to-event distributions, substantially affected the approaches’ performance. Although no considerable differences in health economic outcomes were observed, the case study showed that the ESPD approach was most sensitive to low event rates, which negatively affected performance. Conclusions. Based on overall performance, the recommended modeling approach for implementing competing risks in DES models is the MDR approach, which is defined according to the general strategy of selecting the time-to-event first and the corresponding event second. The ESPD approach is a less complex and equally performing alternative if sufficient observations are available for each competing event (i.e., the internal validity shows appropriate data representation).