Evaluating disease-modifying agents: a simulation framework for Alzheimer's disease.

BACKGROUND: Considerable advances have been made in modeling Alzheimer's disease (AD), with a move towards individual-level rather than cohort models and simulations that consider multiple dimensions when evaluating disease severity. However, the possibility that disease-modifying agents (DMAs) may emerge requires an update of existing modeling frameworks. OBJECTIVES: The aim of this study was to develop a simulation allowing for economic evaluation of DMAs in AD. METHODS: The model was developed based on a previously published, well-validated, discrete event simulation which measures disease severity on the basis of cognition, behaviour, and function, and captures the interrelated changes in these measures for individuals. The updated model adds one more domain, patient dependence, in addition to cognition, behaviour, and function to better characterize disease severity. Furthermore, the model was modified to have greater flexibility in assessing the impact of various important assumptions, such as the long-term effectiveness of DMAs and their impact on survival, on model outcomes. A validation analysis was performed to examine how well the model predicted change in disease severity among patients not receiving DMA treatment by comparing model results to those observed in two recent phase III clinical trials of bapineuzumab. In addition, various hypothetical scenarios were tested to demonstrate the improved features of the model. RESULTS: Validation results show that the model closely predicts the mean changes in disease severity over 18 months. Results from different hypothetical scenarios show that the model allows for credible assessment of those major uncertainties surrounding the long-term effectiveness of DMAs, including the potential impact of improved survival with DMA treatment. They also indicate that varying these assumptions could have a major impact on the value of DMAs. CONCLUSIONS: The updated economic model has good predictive power, but validation against longer-term outcomes is still needed. Our analyses also demonstrate the importance of designing a model with sufficient flexibility such that the model allows for assessment of the impact of key sources of uncertainty on the value of DMAs.

Modeling economic implications of alternative treatment strategies for acute bacterial skin and skin structure infections.

OBJECTIVE: The economic implications from the US Medicare perspective of adopting alternative treatment strategies for acute bacterial skin and skin structure infections (ABSSSIs) are substantial. The objective of this study is to describe a modeling framework that explores the impact of decisions related to both the location of care and switching to different antibiotics at discharge. METHODS: A discrete event simulation (DES) was developed to model the treatment pathway of each patient through various locations (emergency department [ED], inpatient, and outpatient) and the treatments prescribed (empiric antibiotic, switching to a different antibiotic at discharge, or a second antibiotic). Costs are reported in 2012 USD. RESULTS: The mean number of days on antibiotic in a cohort assigned to a full course of vancomycin was 11.2 days, with 64% of the treatment course being administered in the outpatient setting. Mean total costs per patient were $8671, with inpatient care accounting for 58% of the costs accrued. The majority of outpatient costs were associated with parenteral administration rather than drug acquisition or monitoring. Scenarios modifying the treatment pathway to increase the proportion of patients receiving the first dose in the ED, and then managing them in the outpatient setting or prescribing an oral antibiotic at discharge to avoid the cost associated with administering parenteral therapy, therefore have a major impact and lower the typical cost per patient by 11-20%. Since vancomycin is commonly used as empiric therapy in clinical practice, based on these analyses, a shift in treatment practice could result in substantial savings from the Medicare perspective. CONCLUSIONS: The choice of antibiotic and location of care influence the costs and resource use associated with the management of ABSSSIs. The DES framework presented here can provide insight into the potential economic implications of decisions that modify the treatment pathway.

A Canadian study of the cost-effectiveness of apixaban compared with enoxaparin for post-surgical venous thromboembolism prevention.

BACKGROUND: Occurrence of a venous thromboembolism (VTE) in patients undergoing major orthopedic surgery who are not given thromboprophylactic therapy presents considerable danger to patient medical outcomes and a significant economic burden to the health care system at large. Apixaban is a direct factor Xa inhibitor that has been shown in clinical trial use to safely reduce the composite of VTE and mortality rates in patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA); however, the cost-effectiveness of apixaban treatment in Canadian settings has not been studied. Our study evaluated the cost-effectiveness of apixaban compared with enoxaparin as VTE preventive therapy in patients undergoing elective THA or TKA in Canada. METHODS: An economic model, including both a decision-tree component and a Markov model, was created. The decision tree considered VTE, bleeding, and mortality incidence that occurred in patients within 90 days post-surgery using data from the Apixaban Versus Enoxaparin for Thromboprophylaxis After Knee or Hip Replacement (ADVANCE) trials, which compared apixaban therapy with 30-mg twice daily and 40-mg daily enoxaparin treatment. The Markov model provided the option to simulate events that may occur over the long term, such as recurrent VTE and post-thrombotic syndrome. Outcomes during the short-term phase directly impact the risk of events occurring during the long-term phase (5 years post-surgery). RESULTS: The results of our analysis indicated that apixaban is dominant (ie, more effective and less expensive) than enoxaparin in treating patients undergoing THA and TKA. There were fewer occurrences of VTEs, bleeding events, recurrent VTEs, and post-thrombotic syndrome events in the TKA population with apixaban therapy. Similar results were seen in patients undergoing THA, with the exception of bleeding events, which were more common with apixaban treatment. Savings of $180 to $270 per patient are expected with apixaban treatment compared with enoxaparin treatment, and health outcomes in general are better with apixaban use. Sensitivity analyses yielded consistent results across the THA and TKA populations. CONCLUSION: : This is the first economic evaluation of apixaban use for VTE thromboprophylaxis in the Canadian setting, and our study results show apixaban to be a cost-effective treatment alternative to preventive treatment with enoxaparin.

Smoking cessation treatment and outcomes patterns simulation: a new framework for evaluating the potential health and economic impact of smoking cessation interventions.

BACKGROUND: Most existing models of smoking cessation treatments have considered a single quit attempt when modelling long-term outcomes. OBJECTIVE: To develop a model to simulate smokers over their lifetimes accounting for multiple quit attempts and relapses which will allow for prediction of the long-term health and economic impact of smoking cessation strategies. METHODS: A discrete event simulation (DES) that models individuals' life course of smoking behaviours, attempts to quit, and the cumulative impact on health and economic outcomes was developed. Each individual is assigned one of the available strategies used to support each quit attempt; the outcome of each attempt, time to relapses if abstinence is achieved, and time between quit attempts is tracked. Based on each individual's smoking or abstinence patterns, the risk of developing diseases associated with smoking (chronic obstructive pulmonary disease, lung cancer, myocardial infarction and stroke) is determined and the corresponding costs, changes to mortality, and quality of life assigned. Direct costs are assessed from the perspective of a comprehensive US healthcare payer ($US, 2012 values). Quit attempt strategies that can be evaluated in the current simulation include unassisted quit attempts, brief counselling, behavioural modification therapy, nicotine replacement therapy, bupropion, and varenicline, with the selection of strategies and time between quit attempts based on equations derived from survey data. Equations predicting the success of quit attempts as well as the short-term probability of relapse were derived from five varenicline clinical trials. RESULTS: Concordance between the five trials and predictions from the simulation on abstinence at 12 months was high, indicating that the equations predicting success and relapse in the first year following a quit attempt were reliable. Predictions allowing for only a single quit attempt versus unrestricted attempts demonstrate important differences, with the single quit attempt simulation predicting 19 % more smoking-related diseases and 10 % higher costs associated with smoking-related diseases. Differences are most prominent in predictions of the time that individuals abstain from smoking: 13.2 years on average over a lifetime allowing for multiple quit attempts, versus only 1.2 years with single quit attempts. Differences in abstinence time estimates become substantial only 5 years into the simulation. In the multiple quit attempt simulations, younger individuals survived longer, yet had lower lifetime smoking-related disease and total costs, while the opposite was true for those with high levels of nicotine dependence. CONCLUSION: By allowing for multiple quit attempts over the course of individuals' lives, the simulation can provide more reliable estimates on the health and economic impact of interventions designed to increase abstinence from smoking. Furthermore, the individual nature of the simulation allows for evaluation of outcomes in populations with different baseline profiles. DES provides a framework for comprehensive and appropriate predictions when applied to smoking cessation over smoker lifetimes.