Multistate Statistical Modeling: A Tool to Build a Lung Cancer Microsimulation Model That Includes Parameter Uncertainty and Patient Heterogeneity

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios

We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

How microsimulation translates outcome estimates to patient lifetime event occurrence in the setting of heart valve disease

Treatment decisions in healthcare often carry lifelong consequences that can be challenging to foresee. As such, tools that visualize and estimate outcome after different lifetime treatment strategies are lacking and urgently needed to support clinical decision-making in the setting of rapidly evolving healthcare systems, with increasingly numerous potential treatments. In this regard, microsimulation models may prove to be valuable additions to current risk-prediction models. Notable advantages of microsimulation encompass input from multiple data sources, the ability to move beyond time-to-first-event analysis, accounting for multiple types of events and generating projections of lifelong outcomes. This review aims to clarify the concept of microsimulation, also known as individualized state-transition models, and help clinicians better understand its potential in clinical decision-making. A practical example of a patient with heart valve disease is used to illustrate key components of microsimulation models, such as health states, transition probabilities, input parameters (e.g. evidence-based risks of events) and various aspects of mortality. Finally, this review focuses on future efforts needed in microsimulation to allow for increasing patient-tailoring of the models by extending the general structure with patient-specific prediction models and translating them to meaningful, user-friendly tools that may be used by both clinician and patient to support clinical decision-making.

Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

OBJECTIVES

Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs).

METHODS

In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice.

RESULTS

PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states.

CONCLUSIONS

The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.

Modeling Personalized Adjuvant TreaTment in EaRly stage coloN cancer (PATTERN)

AIM

To develop a decision model for the population-level evaluation of strategies to improve the selection of stage II colon cancer (CC) patients who benefit from adjuvant chemotherapy.

METHODS

A Markov cohort model with a one-month cycle length and a lifelong time horizon was developed. Five health states were included; diagnosis, 90-day mortality, death other causes, recurrence and CC death. Data from the Netherlands Cancer Registry were used to parameterize the model. Transition probabilities were estimated using parametric survival models including relevant clinical and pathological covariates. Subsequently, biomarker status was implemented using external data. Treatment effect was incorporated using pooled trial data. Model development, data sources used, parameter estimation, and internal and external validation are described in detail. To illustrate the use of the model, three example strategies were evaluated in which allocation of treatment was based on (A) 100% adherence to the Dutch guidelines, (B) observed adherence to guideline recommendations and (C) a biomarker-driven strategy.

RESULTS

Overall, the model showed good internal and external validity. Age, tumor growth, tumor sidedness, evaluated lymph nodes, and biomarker status were included as covariates. For the example strategies, the model predicted 83, 87 and 77 CC deaths after 5 years in a cohort of 1000 patients for strategies A, B and C, respectively.

CONCLUSION

This model can be used to evaluate strategies for the allocation of adjuvant chemotherapy in stage II CC patients. In future studies, the model will be used to estimate population-level long-term health gain and cost-effectiveness of biomarker-based selection strategies.

Analysis of Trends and Factors in Breast Multiple Primary Malignant Neoplasms

Background

The study aims to evaluate the current state and tendencies in multiple primary breast cancer incidence, behavior, and treatment in Ukraine.

Methods

A total of 2032 patients who received special treatment at the Department of Breast Tumors and Reconstructive Surgery of the National Cancer Institute from 2008 to 2015 were included in the study. Among them, there were 195 patients with multiple primary malignant neoplasms: 54.9% patients with synchronous cancer and 45.1% patients with metachronous cancer. The average age of patients was 46.6 years, and the percentage of postmenopausal women was 63.1%. Among patients with synchronous cancer, there were 56.1% patients with only breast localizations and 43.9% with combination of breast and other localizations, and among patients with metachronous cancer, there were 46.6% patients with only breast localizations and 53.4% with combination of breast and other localizations. All the patients were evaluated in terms of aggressiveness of the disease, survival rates, as well as risk factors and treatment options.

Results

A more aggressive course of breast cancer is observed in patients exposed to radiation from the Chernobyl accident under the age of 30 years (P < .01). The clinical course of disease in patients with synchronous cancer is worse and prognostically unfavorable compared with metachronous cancer (P < .01). The course of the disease in patients who underwent mastectomy is worse compared with patients who underwent breast-conserving surgery (P < .01). Plastic and reconstructive surgery in patients with synchronous cancer was proven to be reasonable in terms of increase in survival (P < .01).

Conclusions

The patients with multiple primary breast cancer should have attentive management and treatment. Multidisciplinary team should concern all the risk factors and provide the most sufficient option of management. This is crucial to continue research in this oncological area.

MODEL-BASED COST-EFFECTIVENESS OF CONVENTIONAL AND INNOVATIVE CHEMO-RADIATION IN LUNG CANCER

INTRODUCTION

Optimizing radiotherapy with or without chemotherapy through advanced imaging and accelerated radiation schemes shows promising results in locally advanced non-small-cell lung cancer (NSCLC). This study compared the cost-effectiveness of positron emission tomography-computed tomography based isotoxic accelerated sequential chemo-radiation (SRT2) and concurrent chemo-radiation with daily low-dose cisplatin (CRT2) with standard sequential (SRT1) and concurrent chemo-radiation (CRT1).

METHODS

We used an externally validated mathematical model to simulate the four treatment strategies. The model was built using data from 200 NSCLC patients treated with curative sequential chemo-radiation. For concurrent strategies, data from a meta-analysis and a single study were included in the model. Costs, utilities, and resource use estimates were obtained from literature. Primary outcomes were the incremental cost-effectiveness and cost-utility ratio (ICUR) of each strategy. Scenario analyses were carried out to investigate the impact of uncertainty.

RESULTS

Total undiscounted costs and quality-adjusted life-years (QALYs) for SRT1, CRT1, SRT2, and CRT2 were EUR 17,288, EUR 18,756, EUR 19,072, EUR 17,360 and QALYs 1.10, 1.15, 1.40, and 1.40, respectively. Compared with SRT1, the ICURs were EUR 38,024/QALY for CRT1, EUR 6,249/QALY for SRT2, and EUR 346/QALY for CRT2. CRT2 was highly cost-effective compared with SRT1. Moreover, CRT2 was more effective and less costly than CRT1 and SRT2. Therefore, these strategies were dominated by CRT2.

CONCLUSION

Optimized sequential and concurrent chemo-radiation strategies are more effective and cost-effective than the current conventional sequential and concurrent strategies. Concurrent chemo-radiation with a daily low dose cisplatin regimen is the most cost-effective treatment option for locally advanced inoperable NSCLC patients.

Quantifying the Transition from Active Surveillance to Watchful Waiting Among Men with Very Low-risk Prostate Cancer

BACKGROUND

Active surveillance (AS) is commonly used for men with low-risk prostate cancer (PCa). When life expectancy becomes too short for curative treatment to be beneficial, a change from AS to watchful waiting (WW) follows. Little is known about this change since it is rarely documented in medical records.

OBJECTIVE

To model transition from AS to WW and how this is affected by age and comorbidity among men with very low-risk PCa.

DESIGN, SETTING, AND PARTICIPANTS

National population-based healthcare registers were used for analysis.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Using data on PCa characteristics, age, and comorbidity, a state transition model was created to estimate the probability of changes between predefined treatments to estimate transition from AS to WW.

RESULTS AND LIMITATIONS

Our estimates indicate that 48% of men with very low-risk PCa starting AS eventually changed to WW over a life course. This proportion increased with age at time of AS initiation. Within 10 yr from start of AS, 10% of men aged 55 yr and 50% of men aged 70 yr with no comorbidity at initiation changed to WW. Our prevalence simulation suggests that the number of men on WW who were previously on AS will eventually stabilise after 30 yr. A limitation is the limited information from clinical follow-up visits (eg, repeat biopsies).

CONCLUSIONS

We estimated that changes from AS to WW become common among men with very low-risk PCa who are elderly. This potential change to WW should be discussed with men starting on AS. Moreover, our estimates may help in planning health care resources allocated to men on AS, as the transition to WW is associated with lower demands on outpatient resources.

PATIENT SUMMARY

Changes from active surveillance to watchful waiting will become more common among men with very low-risk prostate cancer. These observations suggest that patients need to be informed about this potential change before they start on active surveillance.

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.