Predicting the Population Health Economic Impact of Current and New Cancer Treatments for Colorectal Cancer: A Data-Driven Whole Disease Simulation Model for Predicting the Number of Patients with Colorectal Cancer by Stage and Treatment Line in Australia.

OBJECTIVES: Effective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The Predicting the Population Health Economic Impact of Current and New Cancer Treatments (PRIMCAT) for Colorectal Cancer (CRC) simulation model (PRIMCAT-CRC) was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. METHODS: Real-world data were used to estimate treatment utilization and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilize future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model's applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair-deficient stage IV. RESULTS: Clinical registry data from 7163 patients informed the model. The model forecasts 15 738 incident and 2821 prevalent cases requiring treatment in 2022, rising to 15 921 and 2871, respectively, by 2026. Projections show that over 2022 to 2026, there will be a total of 116 752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totaling 3530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilization of subsequent treatments. CONCLUSIONS: PRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments.

Health economic evidence for adjuvant chemotherapy in stage II and III colon cancer: a systematic review.

OBJECTIVE: The aims of this study was to appraise the health economic evidence for adjuvant chemotherapy (AC) strategies in stage II and III colon cancer (CC) and identify gaps in the available evidence that might inform further research. METHOD: A systematic review of published economic evaluations was undertaken. Four databases were searched and full-text publications in English were screened for inclusion. A narrative synthesis was performed to summarise the evidence. RESULTS: Thirty-eight studies were identified and stratified by cancer stage and AC strategy. The majority (89%) were full economic evaluations considering both health outcomes, usually measured as quality-adjusted life years (QALYs), and costs. AC was found to be cost-effective compared to no AC for both stage II and III CC. Oral and oxaliplatin-based AC was cost-effective for stage III. Three months of CAPOX was cost-effective compared to 6-month in high-risk stage II and stage III CC. Preliminary evidence suggests that biomarker approaches to AC selection in stage II can reduce costs and improve health outcomes. Notably, assessment of QALYs were predominantly reliant on a small number of non-contemporary health-utility studies. Only 32% of studies considered societal costs such as travel and time off work. CONCLUSIONS: Published economic evaluations consistently supported the use of AC in stage II and III colon cancer. Biomarker-driven approaches to patient selection have great potential to be cost-effective, but more robust clinical and economic evidence is warranted. Patient surveys embedded into clinical trials may address critical knowledge gaps regarding accurate assessment of QALYs and societal costs in the modern era.

Health economic evidence for the use of molecular biomarker tests in hematological malignancies: A systematic review.

OBJECTIVES: Molecular biomarker tests can inform the clinical management of genomic heterogeneous hematological malignancies, yet their availability in routine care largely depends on the supporting health economic evidence. This study aims to systematically review the economic evidence for recent molecular biomarker tests in hematological malignancies. METHODS: We conducted a systematic search in five electronic databases for studies published between January 2010 and October 2020. Publications were independently screened by two reviewers. Clinical study characteristics, economic methodology, and results were extracted, and reporting quality was assessed. RESULTS: Fourteen studies were identified, of which half (n = 7; 50%) were full economic evaluations examining both health and economic outcomes. Studies were predominantly conducted in a first-line treatment setting (n = 7; 50%) and adopted a non-lifetime time horizon to measure health outcomes and costs (n = 7; 50%). Five studies reported that companion diagnostics for associated therapies were likely cost-effective for acute myeloid leukemia, chronic myeloid leukemia, diffuse large B-cell lymphoma, and multiple myeloma. Four studies suggested molecular biomarker tests for treatment monitoring in chronic myeloid leukemia were likely cost-saving. CONCLUSIONS: Although there is initial confirmation of the promising health economic results, the present research for molecular biomarker tests in hematological malignancies is sparse with many applications of technological advances yet to be evaluated.

Can we increase efficiency of CT lung cancer screening by combining with CVD and COPD screening? Results of an early economic evaluation.

OBJECTIVES: Estimating the maximum acceptable cost (MAC) per screened individual for low-dose computed tomography (LDCT) lung cancer (LC) screening, and determining the effect of additionally screening for chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), or both on the MAC. METHODS: A model-based early health technology assessment (HTA) was conducted to estimate whether a new intervention could be cost-effective by calculating the MAC at a willingness-to-pay (WTP) of €20k/quality-adjusted life-year (QALY) and €80k/QALY, for a population of current and former smokers, aged 50-75 years in The Netherlands. The MAC was estimated based on incremental QALYs gained from a stage shift assuming screened individuals are detected in earlier disease stages. Data were obtained from literature and publicly available statistics and validated with experts. RESULTS: The MAC per individual for implementing LC screening at a WTP of €20k/QALY was €113. If COPD, CVD, or both were included in screening, the MAC increased to €230, €895, or €971 respectively. Scenario analyses assessed whether screening-specific disease high-risk populations would improve cost-effectiveness, showing that high-risk CVD populations were more likely to improve economic viability compared to COPD. CONCLUSIONS: The economic viability of combined screening is substantially larger than for LC screening alone, primarily due to benefits from CVD screening, and is dependent on the target screening population, which is key to optimise the screening program. The total cost of breast and cervical cancer screening is lower (€420) than the MAC of Big-3, indicating that Big-3 screening may be acceptable from a health economic perspective. KEY POINTS: • Once-off combined low-dose CT screening for lung cancer, COPD, and CVD in individuals aged 50-75 years is potentially cost-effective if screening would cost less than €971 per screened individual. • Multi-disease screening requires detailed insight into the co-occurrence of these diseases to identify the optimal target screening population. • With the same target screening population and WTP, lung cancer-only screening should cost less than €113 per screened individual to be cost-effective.

Comparing Modeling Approaches for Discrete Event Simulations With Competing Risks Based on Censored Individual Patient Data: A Simulation Study and Illustration in Colorectal Cancer.

OBJECTIVES: This study aimed to provide detailed guidance on modeling approaches for implementing competing events in discrete event simulations based on censored individual patient data (IPD). METHODS: The event-specific distributions (ESDs) approach sampled times from event-specific time-to-event distributions and simulated the first event to occur. The unimodal distribution and regression approach sampled a time from a combined unimodal time-to-event distribution, representing all events, and used a (multinomial) logistic regression model to select the event to be simulated. A simulation study assessed performance in terms of relative absolute event incidence difference and relative entropy of time-to-event distributions for different types and levels of right censoring, numbers of events, distribution overlap, and sample sizes. Differences in cost-effectiveness estimates were illustrated in a colorectal cancer case study. RESULTS: Increased levels of censoring negatively affected the modeling approaches' performance. A lower number of competing events and higher overlap of distributions improved performance. When IPD were censored at random times, ESD performed best. When censoring occurred owing to a maximum follow-up time for 2 events, ESD performed better for a low level of censoring (ie, 10%). For 3 or 4 competing events, ESD better represented the probabilities of events, whereas unimodal distribution and regression better represented the time to events. Differences in cost-effectiveness estimates, both compared with no censoring and between approaches, increased with increasing censoring levels. CONCLUSIONS: Modelers should be aware of the different modeling approaches available and that selection between approaches may be informed by data characteristics. Performing and reporting extensive validation efforts remains essential to ensure IPD are appropriately represented.

Evaluating the resource implications of different service delivery models for offering additional genomic findings.

PURPOSE: To evaluate the resource implications of different delivery models for the provision of additional findings (AF) in genomics from a health-care purchaser perspective. METHODS: Data from the Additional Findings study were used to develop and validate a discrete event simulation model that represented the pathway of delivering AF. Resource implications were estimated by microcosting the consultations, sample verifications, bioinformatics, curation, and multidisciplinary case review meetings. A proof-of-concept model was used to generate costing, and then the simulation model was varied to assess the impact of an automated analysis pipeline, use of telehealth consultation, full automation with electronic decision support, and prioritizing case review for cases with pathogenic variants. RESULTS: For the proof-of-concept delivery model, the average total cost to report AF was US$430 per patient irrespective of result pathogenicity (95% confidence interval [CI] US$375-US$489). However, the cost of per AF diagnosis was US$4349 (95% CI US$3794-US$4953). Alternative approaches to genetic counseling (telehealth, decision support materials) and to multidisciplinary case review (pathogenic AF cases only) lowered the total per patient cost of AF analysis and reporting by 41-51%. CONCLUSION: Resources required to provide AF can be reduced substantially by implementing alternative approaches to counseling and multidisciplinary case review.

Lifetime Health and Economic Outcomes of Active Surveillance, Radical Prostatectomy, and Radiotherapy for Favorable-Risk Localized Prostate Cancer.

OBJECTIVES: To estimate the lifetime health and economic outcomes of selecting active surveillance (AS), radical prostatectomy (RP), or radiation therapy (RT) as initial management for low- or favorable-risk localized prostate cancer. METHODS: A discrete-event simulation model was developed using evidence from published randomized trials. Health outcomes were measured in life-years and quality-adjusted life-years (QALYs). Costs were included from a public payer perspective in Australian dollars. Outcomes were discounted at 5% over a lifetime horizon. Probabilistic and scenario analyses quantified parameter and structural uncertainty. RESULTS: A total of 60% of patients in the AS arm eventually received radical treatment (surgery or radiotherapy) compared with 90% for RP and 91% for RT. Although AS resulted in fewer treatment-related complications, it led to increased clinical progression (AS 40.7%, RP 17.6%, RT 19.9%) and metastatic disease (AS 13.4%, RP 6.1%, RT 7.0%). QALYs were 10.88 for AS, 11.10 for RP, and 11.13 for RT. Total costs were A$17 912 for AS, A$15 609 for RP, and A$15 118 for RT. At a willingness to pay of A$20 000/QALY, RT had a 61.4% chance of being cost-effective compared to 38.5% for RP and 0.1% for AS. CONCLUSIONS: Although AS resulted in fewer and delayed treatment-related complications, it was not found to be a cost-effective strategy for favorable-risk localized prostate cancer over a lifetime horizon because of an increase in the number of patients developing metastatic disease. RT was the dominant strategy yielding higher QALYs at lower cost although differences compared with RP were small.

Using Metamodeling to Identify the Optimal Strategy for Colorectal Cancer Screening.

OBJECTIVES: Metamodeling can address computational challenges within decision-analytic modeling studies evaluating many strategies. This article illustrates the value of metamodeling for evaluating colorectal cancer screening strategies while accounting for colonoscopy capacity constraints. METHODS: In a traditional approach, the best screening strategy was identified from a limited subset of strategies evaluated with the validated Adenoma and Serrated pathway to Colorectal CAncer model. In a metamodeling approach, metamodels were fitted to this limited subset to evaluate all potentially plausible strategies and determine the best overall screening strategy. Approaches were compared based on the best screening strategy in life-years gained compared with no screening. Metamodel runtime and accuracy was assessed. RESULTS: The metamodeling approach evaluated >40 000 strategies in <1 minute with high accuracy after 1 adaptive sampling step (mean absolute error: 0.0002 life-years) using 300 samples in total (generation time: 8 days). Findings indicated that health outcomes could be improved without requiring additional colonoscopy capacity. Obtaining similar insights using the traditional approach could require at least 1000 samples (generation time: 28 days). Suggested benefits from screening at ages <40 years require adequate validation of the underlying Adenoma and Serrated pathway to Colorectal CAncer model before making policy recommendations. CONCLUSIONS: Metamodeling allows rapid assessment of a vast set of strategies, which may lead to identification of more favorable strategies compared to a traditional approach. Nevertheless, metamodel validation and identifying extrapolation beyond the support of the original decision-analytic model are critical to the interpretation of results. The screening strategies identified with metamodeling support ongoing discussions on decreasing the starting age of colorectal cancer screening.

Evaluation of the performance of algorithms mapping EORTC QLQ-C30 onto the EQ-5D index in a metastatic colorectal cancer cost-effectiveness model.

BACKGROUND: Cost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D. We evaluated the performance of available algorithms for QLQ-C30 conversion into EQ-5D-3L based utilities in a metastatic colorectal cancer (mCRC) patient population and subsequently developed a mCRC specific algorithm. Influence of mapping on cost-effectiveness was evaluated. METHODS: Three available algorithms were compared with observed utilities from the CAIRO3 study. Six models were developed using 5-fold cross-validation: predicting EQ-5D-3L tariffs from QLQ-C30 functional scale scores, continuous QLQ-C30 scores or dummy levels with a random effects model (RE), a most likely probability method on EQ-5D-3L functional scale scores, a beta regression model on QLQ-C30 functional scale scores and a separate equations subgroup approach on QLQ-C30 functional scale scores. Performance was assessed, and algorithms were tested on incomplete QLQ-C30 questionnaires. Influence of utility mapping on incremental cost/QALY gained (ICER) was evaluated in an existing Dutch mCRC cost-effectiveness model. RESULTS: The available algorithms yielded mean utilities of 1: 0.87 ± sd:0.14,2: 0.81 ± 0.15 (both Dutch tariff) and 3: 0.81 ± sd:0.19. Algorithm 1 and 3 were significantly different from the mean observed utility (0.83 ± 0.17 with Dutch tariff, 0.80 ± 0.20 with U.K. tariff). All new models yielded predicted utilities drawing close to observed utilities; differences were not statistically significant. The existing algorithms resulted in an ICER difference of €10,140 less and €1765 more compared to the observed EQ-5D-3L based ICER (€168,048). The preferred newly developed algorithm was €5094 higher than the observed EQ-5D-3L based ICER. Disparity was explained by minimal diffences in incremental QALYs between models. CONCLUSION: Available mapping algorithms sufficiently accurately predict utilities. With the commonly used statistical methods, we did not succeed in developping an improved mapping algorithm. Importantly, cost-effectiveness outcomes in this study were comparable to the original model outcomes between different mapping algorithms. Therefore, mapping can be an adequate solution for cost-effectiveness studies using either a previously designed and validated algorithm or an algorithm developed in this study.

Comparison of Timed Automata with Discrete Event Simulation for Modeling of Biomarker-Based Treatment Decisions: An Illustration for Metastatic Castration-Resistant Prostate Cancer.

BACKGROUND: With the advent of personalized medicine, the field of health economic modeling is being challenged and the use of patient-level dynamic modeling techniques might be required. OBJECTIVES: To illustrate the usability of two such techniques, timed automata (TA) and discrete event simulation (DES), for modeling personalized treatment decisions. METHODS: An early health technology assessment on the use of circulating tumor cells, compared with prostate-specific antigen and bone scintigraphy, to inform treatment decisions in metastatic castration-resistant prostate cancer was performed. Both modeling techniques were assessed quantitatively, in terms of intermediate outcomes (e.g., overtreatment) and health economic outcomes (e.g., net monetary benefit). Qualitatively, among others, model structure, agent interactions, data management (i.e., importing and exporting data), and model transparency were assessed. RESULTS: Both models yielded realistic and similar intermediate and health economic outcomes. Overtreatment was reduced by 6.99 and 7.02 weeks by applying circulating tumor cell as a response marker at a net monetary benefit of -€1033 and -€1104 for the TA model and the DES model, respectively. Software-specific differences were observed regarding data management features and the support for statistical distributions, which were considered better for the DES software. Regarding method-specific differences, interactions were modeled more straightforward using TA, benefiting from its compositional model structure. CONCLUSIONS: Both techniques prove suitable for modeling personalized treatment decisions, although DES would be preferred given the current software-specific limitations of TA. When these limitations are resolved, TA would be an interesting modeling alternative if interactions are key or its compositional structure is useful to manage multi-agent complex problems.

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models.

BACKGROUND: Parametric 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. METHODS: Two 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. RESULTS: Accounting 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. CONCLUSIONS: Modelers 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.

Scenario analysis and multi-criteria decision analysis to explore alternative reimbursement pathways for whole genome sequencing for blood cancer patients.

BACKGROUND: Whole genome sequencing (WGS) has transformative potential for blood cancer management, but reimbursement is hindered by uncertain benefits relative to added costs. This study employed scenario planning and multi-criteria decision analysis (MCDA) to evaluate stakeholders' preferences for alternative reimbursement pathways, informing future health technology assessment (HTA) submission of WGS in blood cancer. METHODS: Key factors influencing WGS reimbursement in blood cancers were identified through a literature search. Hypothetical scenarios describing various evidential characteristics of WGS for HTA were developed using the morphological approach. An online survey, incorporating MCDA weights, was designed to gather stakeholder preferences (consumers/patients, clinicians/health professionals, industry representatives, health economists, and HTA committee members) for these scenarios. The survey assessed participants' approval of WGS reimbursement for each scenario, and scenario preferences were determined using the geometric mean method, applying an algorithm to improve reliability and precision by addressing inconsistent responses. RESULTS: Nineteen participants provided complete survey responses, primarily clinicians or health professionals (n = 6; 32 %), consumers/patients and industry representatives (both at n = 5; 26 %). "Clinical impact of WGS results on patient care" was the most critical criterion (criteria weight of 0.25), followed by "diagnostic accuracy of WGS" (0.21), "cost-effectiveness of WGS" (0.19), "availability of reimbursed treatment after WGS" (0.16), and "eligibility criteria for reimbursed treatment based on actionable WGS results" and "cost comparison of WGS" (both at 0.09). Participants preferred a scenario with substantial clinical evidence, high access to reimbursed targeted treatment, cost-effectiveness below $50,000 per quality-adjusted life year (QALY) gained, and affordability relative to standard molecular tests. Reimbursement was initially opposed until criteria such as equal cost to standard tests and better treatment accessibility were met. CONCLUSION: Payers commonly emphasize acceptable cost-effectiveness, but strong clinical evidence for many variants and comparable costs to standard tests are likely to drive positive reimbursement decisions for WGS.

Cost Effectiveness of Molecular Diagnostic Testing Algorithms for the Treatment Selection of Frontline Ibrutinib for Patients with Chronic Lymphocytic Leukemia in Australia.

BACKGROUND: Clinical indications for ibrutinib reimbursement in Australia should consider the inclusion of patients with chronic lymphocytic leukemia (CLL) harboring prognostically unfavorable TP53/IGHV genomic aberrations. This study assessed the cost effectiveness of five first-line treatment strategies in CLL for young (aged ≤ 65 years), fit patients without significant comorbidities: (1) no testing (fludarabine, cyclophosphamide and rituximab [FCR] for all), (2) test for del(17p) only, (3) test for TP53 gene mutation status, (4) test for TP53 and IGHV gene mutation status and (5) no testing (ibrutinib for all). METHOD: A decision analytic model (decision tree and partitioned survival model) was developed from the Australian healthcare system perspective with a lifetime horizon. Comparative treatment effects were estimated from indirect treatment comparisons and survival analysis using several studies. Costs, utility and adverse events were derived from public literature sources. Deterministic and probabilistic sensitivity analyses explored the impact of modeling uncertainties on outcomes. RESULTS: Strategy 1 was associated with 5.69 quality-adjusted life-years (QALYs) and cost 458,836 Australian dollars (AUD). All other strategies had greater effectiveness but were more expensive than Strategy 1. At the willingness-to-pay (WTP) threshold of 100,000 AUD per QALY gained, Strategy 1 was most cost effective with an estimated probability of 68.8%. Strategy 4 was cost effective between thresholds 155,000-432,300 AUD per QALY gained, and Strategy 5 >432,300 AUD per QALY gained. CONCLUSION: Population targeting using mutation testing for TP53 and IGHV when performed with del(17p) testing specifically in the context of frontline ibrutinib choice does not make a cost-ineffective treatment into a cost-effective treatment.

Economic Impact of Whole Genome Sequencing and Whole Transcriptome Sequencing Versus Routine Diagnostic Molecular Testing to Stratify Patients with B-Cell Acute Lymphoblastic Leukemia.

Whole genome and whole transcriptome sequencing (WGTS) can accurately distinguish B-cell acute lymphoblastic leukemia (B-ALL) genomic subtypes. However, whether this is economically viable remains unclear. This study compared the direct costs and molecular subtype classification yield using different testing strategies for WGTS in adolescent and young adult/adult patients with B-ALL. These approaches were: (1) combined BCR::ABL1 by fluorescence in situ hybridization (FISH) + WGTS for all patients; and (2) sequential BCR::ABL1 FISH + WGTS contingent on initial BCR::ABL1 FISH test outcome. The cost of routine diagnostic testing was estimated using Medicare or hospital fees, and the additional cost of WGTS was evaluated from the health care provider perspective using time-driven activity-based costing with resource identification elicited from experts. Molecular subtype classification yield data were derived from literature sources. Parameter uncertainty was assessed through deterministic sensitivity analysis; additional scenario analyses were performed. The total per patient cost of WGTS was $4319 (all costs reported in US dollars); consumables accounted for 74% of the overall cost, primarily driven by sequencing-related consumables. The incremental cost per additional patient categorized into molecular subtype was $8498 for combined BCR::ABL1 FISH + WGTS for all patients and $5656 for initial BCR::ABL1 FISH + WGTS for select patients compared with routine diagnostic testing. A reduction in the consumable costs of WGTS or an increase in the yield of molecular subtype classification is favorable.

Predicting Benefit From FOLFOXIRI Plus Bevacizumab in Patients With Metastatic Colorectal Cancer.

PURPOSE: Patient outcomes may differ from randomized trial averages. We aimed to predict benefit from FOLFOXIRI versus infusional fluorouracil, leucovorin, and oxaliplatin/fluorouracil, leucovorin, and irinotecan (FOLFOX/FOLFIRI), both plus bevacizumab, in patients with metastatic colorectal cancer (mCRC). METHODS: A Cox model with prespecified clinical, molecular, and laboratory variables was developed in 639 patients from the TRIBE2 trial for predicting 2-year mortality. Data from the CHARTA (n = 232), TRIBE1 (n = 504), and CAIRO5 (liver-only mCRC, n = 287) trials were used for external validation and heterogeneity of treatment effects (HTE) analysis. This involves categorizing patients into risk groups and assessing treatment effects across these groups. Performance was assessed by the C-index and calibration plots. The C-for-benefit was calculated to assess evidence for HTE. The c-for-benefit is specifically designed for HTE analysis. Like the commonly known c-statistic, it summarizes the discrimination of a model. Values over 0.5 indicate evidence for HTE. RESULTS: In TRIBE2, the overoptimism-corrected C-index was 0.66 (95% CI, 0.63 to 0.69). At external validation, the C-index was 0.69 (95% CI, 0.64 to 0.75), 0.68 (95% CI, 0.64 to 0.72), and 0.65 (95% CI, 0.65 to 0.66), in CHARTA, TRIBE1, and CAIRO5, respectively. Calibration plots indicated slight underestimation of mortality. The c-for-benefit indicated evidence for HTE in CHARTA (0.56, 95% CI, 0.48 to 0.65), but not in TRIBE1 (0.49, 95% CI, 0.44 to 0.55) and CAIRO5 (0.40, 95% CI, 0.32 to 0.48). CONCLUSION: Although 2-year mortality could be reasonably estimated, the HTE analysis showed that clinically available variables did not reliably identify which patients with mCRC benefit from FOLFOXIRI versus FOLFOX/FOLFIRI, both plus bevacizumab, across the three studies.

Implementing competing risks in discrete event simulation: the event-specific probabilities and distributions approach.

Background: Although several strategies for modelling competing events in discrete event simulation (DES) exist, a methodological gap for the event-specific probabilities and distributions (ESPD) approach when dealing with censored data remains. This study defines and illustrates the ESPD strategy for censored data. Methods: The ESPD approach assumes that events are generated through a two-step process. First, the type of event is selected according to some (unknown) mixture proportions. Next, the times of occurrence of the events are sampled from a corresponding survival distribution. Both of these steps can be modelled based on covariates. Performance was evaluated through a simulation study, considering sample size and levels of censoring. Additionally, an oncology-related case study was conducted to assess the ability to produce realistic results, and to demonstrate its implementation using both frequentist and Bayesian frameworks in R. Results: The simulation study showed good performance of the ESPD approach, with accuracy decreasing as sample sizes decreased and censoring levels increased. The average relative absolute error of the event probability (95%-confidence interval) ranged from 0.04 (0.00; 0.10) to 0.23 (0.01; 0.66) for 60% censoring and sample size 50, showing that increased censoring and decreased sample size resulted in lower accuracy. The approach yielded realistic results in the case study. Discussion: The ESPD approach can be used to model competing events in DES based on censored data. Further research is warranted to compare the approach to other modelling approaches for DES, and to evaluate its usefulness in estimating cumulative event incidences in a broader context.

Quantifying the impact of novel metastatic cancer therapies on health inequalities in survival outcomes.

Background: Novel therapies in metastatic cancers have contributed to improvements in survival outcomes, yet real-world data suggest that improvements may be mainly driven by those patient groups who already had the highest survival outcomes. This study aimed to develop and apply a framework for quantifying the impact of novel metastatic cancer therapies on health inequalities in survival outcomes based on published aggregate data. Methods: Nine (N = 9) novel therapies for metastatic breast cancer (mBC), metastatic colorectal cancer (mCRC), and metastatic non-small cell lung cancer (mNSCLC) were identified, 3 for each cancer type. Individual patient data (IPD) for overall survival (OS) and progression-free survival (PFS) were replicated from published Kaplan-Meier (KM) curves. For each cancer type, data were pooled for the novel therapies and comparators separately and weighted based on sample size to ensure equal contribution of each therapy in the analyses. Parametric (mixture) distributions were fitted to the weighted data to model and extrapolate survival. The inequality in survival was defined by the absolute difference between groups with the highest and lowest survival for 2 stratifications: one for which survival was stratified into 2 groups and one using 5 groups. Additionally, a linear regression model was fitted to survival estimates for the 5 groups, with the regression coefficient or slope considered as the inequality gradient (IG). The impact of the pooled novel therapies was subsequently defined as the change in survival inequality relative to the pooled comparator therapies. A probabilistic analysis was performed to quantify parameter uncertainty. Results: The analyses found that novel therapies were associated with significant increases in inequalities in survival outcomes relative to their comparators, except in terms of OS for mNSCLC. For mBC, the inequalities in OS increased by 13.9 (95% CI: 1.4; 26.6) months, or 25.0%, if OS was stratified in 5 groups. The IG for mBC increased by 3.2 (0.3; 6.1) months, or 24.7%. For mCRC, inequalities increased by 6.7 (3.0; 10.5) months, or 40.4%, for stratification based on 5 groups; the IG increased by 1.6 (0.7; 2.4) months, or 40.2%. For mNSCLC, inequalities decreased by 14.9 (-84.5; 19.0) months, or 12.2%, for the 5-group stratification; the IG decreased by 2.0 (-16.1; 5.1) months, or 5.5%. Results for the stratification based on 2 groups demonstrated significant increases in OS inequality for all cancer types. In terms of PFS, the increases in survival inequalities were larger in a relative sense compared with OS. For mBC, PFS inequalities increased by 8.7 (5.9; 11.6) months, or 71.7%, for stratification based on 5 groups; the IG increased by 2.0 (1.3; 2.6) months, or 67.6%. For mCRC, PFS inequalities increased by 5.4 (4.2; 6.6) months, or 147.6%, for the same stratification. The IG increased by 1.3 (1.1; 1.6) months, or 172.7%. For mNSCLC, inequalities increased by 18.2 (12.5; 24.4) months, or 93.8%, for the 5-group stratification; the IG increased by 4.0 (2.8; 5.4) months, or 88.1%. Results from the stratification based on 2 groups were similar. Conclusion: Novel therapies for mBC, mCRC, and mNSCLC are generally associated with significant increases in survival inequalities relative to their comparators in randomized controlled trials, though inequalities in OS for mNSCLC decreased nonsignificantly when stratified based on 5 groups. Although further research using real-world IPD is warranted to assess how, for example, social determinants of health affect the impact of therapies on health inequalities among patient groups, the proposed framework can provide important insights in the absence of such data.

Circulating Tumour DNA Guided Adjuvant Chemotherapy Decision Making in Stage II Colon Cancer-A Clinical Vignette Study.

Circulating tumour DNA (ctDNA) is a promising biomarker that may better identify stage II colon cancer (CC) patients who will benefit from adjuvant chemotherapy (AC) compared to standard clinicopathological parameters. The DYNAMIC study demonstrated that ctDNA-informed treatment decreased AC utilisation without compromising recurrence free survival, but medical oncologists' willingness to utilise ctDNA results to inform AC decision is unknown. Medical oncologists from Australia, Canada and New Zealand were presented with clinical vignettes for stage II CC comprised of two variables with three levels each (age: ≤50, 52-69, ≥70 years; and clinicopathological risk of recurrence: low, intermediate, high) and were queried about ctDNA testing and treatment recommendations based on results. Sixty-four colorectal oncologists completed at least one vignette (all vignettes, n = 59). The majority of oncologist were Australian (70%; Canada: n = 13; New Zealand: n = 6) and had over 10 years of clinical experience (n = 41; 64%). The proportion of oncologists requesting ctDNA testing exceeded 80% for all vignettes, except for age ≥ 70 and low-risk disease (63%). Following a positive ctDNA result, the proportion of oncologists recommending AC (p < 0.01) and recommending oxaliplatin-based doublet (p < 0.01) increased in all vignettes. Following a negative result, the proportion recommending AC decreased in all intermediate and high-risk vignettes (p < 0.01).

Real-world adjuvant chemotherapy treatment patterns and outcomes over time for resected stage II and III colorectal cancer.

BACKGROUND: The administration of adjuvant chemotherapy (AC) to colorectal cancer (CRC) patients in Australia and impact of recent trial data has not been well reported. We aim to evaluate temporal trends in AC treatment and outcomes in real-world Australian patients. METHODS: CRC patients were analyzed from 13 hospitals, stratified by stage (II or III) and three 5-year time periods (A: 2005-2009, B: 2010-2014, C: 2015-2019). Stage III was further stratified as pre- and post publication of the International Duration Evaluation of Adjuvant Therapy (IDEA) collaboration (March 2018). AC prescription, time-to-recurrence (TTR), and overall survival (OS) was compared across the time periods. RESULTS: Of 3977 identified patients, 1148 (stage II: 640, stage III: 508), 1525 (856 vs. 669), and 1304 (669 vs. 635) were diagnosed in Period A, B, and C, respectively. Fewer patients in Period C received AC compared to Period B in stage II (10% vs. 15%, p <.01) and III (70% vs. 79%, p <.01). Post-IDEA, the proportion of patients receiving ≤3 months of oxaliplatin-based AC increased (45% vs. 13%, p <.01). The proportion of patients who remained recurrence free at 3 years was similar between time periods in stage II (A: 89% vs. B: 88% vs. C: 90%, p = .53) and stage III (72% vs. 76% vs. 72%, p = .08). OS significantly improved for stage II (80%-85%, p = .04) and stage III (69%-77%, <.01) from period A to B. CONCLUSION: AC use has moderately decreased over time with no impact on recurrence rates. Improved survival in more recent years despite similar recurrence rates may be related to improved baseline staging, better postrecurrence treatment, and reduced noncancer-related mortality.

Comparing Survival Outcomes for Advanced Cancer Patients Who Received Complex Genomic Profiling Using a Synthetic Control Arm.

BACKGROUND: Complex genomic profiling (CGP) has transformed cancer treatment decision making, yet there is a lack of robust and quantifiable evidence for how utilisation of CGP improves patient outcomes. OBJECTIVE: This study evaluated cohort level clinical effectiveness of CGP to improve overall survival (OS) in real-world advanced cancer patients using a registry-based matched control population. PATIENTS AND METHODS: Two cohorts of advanced and refractory cancer patients were seen in consecutive series for early phase trial enrolment consideration. The first cohort (CGP group) accessed tumour profiling via a research study; while the second cohort that followed was not profiled. Overall survival between cohorts was compared using Kaplan-Meier curves and Cox proportional hazard models. Potential confounding was analysed and adjusted for using stabilised weights based on propensity scores. RESULTS: Within the CGP group, 25 (17.6%) patients received treatment informed by CGP results and this subgroup had significantly improved survival compared with CGP patients in whom results did not impact their treatment (unadjusted HR = 0.44, (0.22-0.88), p = 0.02). However, when comparing the entire CGP cohort with the No CGP cohort, no significant survival benefit was evident with adjusted median OS for CGP of 13.5 months (9.2-17.0) compared with 11.0 (9.2-17.4) for No CGP (adjusted HR = 0.92, (0.65-1.30), p = 0.63). CONCLUSIONS: This study utilised real-world data to simulate a control arm and quantify the clinical effectiveness of genomic testing. The magnitude of survival benefit for patients who had CGP result-led treatments was insufficient to drive an overall survival gain for the entire tested population. Translation of CGP into clinics requires strategies to ensure higher rates of tested patients obtain clinical benefit to deliver on the value proposition of CGP in an advanced cancer population.