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An Overview of R in Health Decision Sciences.

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R’s popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.

A Gaussian Approximation Approach for Value of Information Analysis

Most decisions are associated with uncertainty. Value of information (VOI) analysis quantifies the opportunity loss associated with choosing a suboptimal intervention based on current imperfect information. VOI can inform the value of collecting additional information, resource allocation, research prioritization, and future research designs. However, in practice, VOI remains underused due to many conceptual and computational challenges associated with its application. Expected value of sample information (EVSI) is rooted in Bayesian statistical decision theory and measures the value of information from a finite sample. The past few years have witnessed a dramatic growth in computationally efficient methods to calculate EVSI, including metamodeling. However, little research has been done to simplify the experimental data collection step inherent to all EVSI computations, especially for correlated model parameters. This article proposes a general Gaussian approximation (GA) of the traditional Bayesian updating approach based on the original work by Raiffa and Schlaifer to compute EVSI. The proposed approach uses a single probabilistic sensitivity analysis (PSA) data set and involves 2 steps: 1) a linear metamodel step to compute the EVSI on the preposterior distributions and 2) a GA step to compute the preposterior distribution of the parameters of interest. The proposed approach is efficient and can be applied for a wide range of data collection designs involving multiple non-Gaussian parameters and unbalanced study designs. Our approach is particularly useful when the parameters of an economic evaluation are correlated or interact.

Trade-offs Between Efficacy and Cardiac Toxicity of Adjuvant Chemotherapy in Early-Stage Breast Cancer Patients: Do Competing Risks Matter?

Evidence about treatment efficacy and long‐term toxicities for adjuvant chemotherapy in patients with early‐stage breast cancer is often presented in different formats and studies. This leads to challenges for patients and their physicians to adequately weigh the trade‐offs between effectiveness and long‐term cardiac toxicity when making decisions about adjuvant chemotherapy. We used a decision‐analytic framework to quantify these trade‐offs by combining the available evidence into a single, comparable metric. We developed a Markov model to simulate a hypothetical cohort of newly diagnosed breast cancer patients under three scenarios: no treatment, anthracycline (AC)‐based adjuvant chemotherapy (more effective but also more cardiotoxic), and non‐AC‐based adjuvant chemotherapy. We derived the model parameters from medical literature (e.g., clinical trials). Our primary outcome is 10‐year mortality, and other metrics such as cause of death; life years (LYs) and quality‐adjusted LYs over 10 years were evaluated in sensitivity analysis. For 55‐year‐old women with a 10‐year risk of metastatic recurrence <12.5% no chemotherapy resulted in the preferred strategy. In general, non‐AC‐based adjuvant chemotherapy resulted in lower 10‐year mortality than AC‐based chemotherapy. Patients with low risk of metastatic recurrence are better off without adjuvant chemotherapy regardless of the outcome considered (i.e., the risks of cardiac toxicity from chemotherapy outweighed the benefits). Trade‐offs between effectiveness and induced cardiac toxicity impact health outcomes. The choice of adjuvant treatment must consider the patients risk of distant recurrence and the quality of life associated with different health outcomes.

Revisiting assumptions about age-based mixing representations in mathematical models of sexually transmitted infections

BACKGROUND Sexual mixing between heterogeneous population subgroups is an integral component of mathematical models of sexually transmitted infections (STIs). This study compares the fit of different mixing representations to survey data and the impact of different mixing assumptions on the predicted benefits of hypothetical human papillomavirus (HPV) vaccine strategies. METHODS We compared novel empirical (data-driven) age mixing structures with the more commonly-used assortative-proportionate (A-P) mixing structure. The A-P mixing structure assumes that a proportion of sexual contacts - known as the assortativity constant, typically estimated from survey data or calibrated - occur exclusively within ones own age group and the remainder mixes proportionately among all age groups. The empirical age mixing structure was estimated from the National Survey on Sexual Attitudes and Lifestyles 3 (Natsal-3) using regression methods, and the assortativity constant was estimated from Natsal-3 as well. Using a simplified HPV transmission model under each mixing assumption, we calibrated the model to British HPV16 prevalence data, then estimated the reduction in steady-state prevalence and the number of infections averted due to expanding HPV vaccination from 12- through 26-year-old females alone to 12-year-old males or 27- to 39-year-old females. RESULTS Empirical mixing provided a better fit to the Natsal-3 data than the best-fitting A-P structure. Using the model with empirical mixing as a reference, the model using the A-P structure often under- or over-estimated the benefits of vaccination, in one case overestimating by 2-fold the number of infections prevented due to extended female catch-up in a high vaccine uptake setting. CONCLUSIONS An empirical mixing structure more accurately represents sexual mixing survey data, and using the less accurate, yet commonly-used A-P structure has a notable effect on estimates of HPV vaccination benefits. This underscores the need for mixing structures that are less dependent on unverified assumptions and are directly informed by sexual behavior data.

Incorporating biomarkers into the primary prostate biopsy setting: a cost-effectiveness analysis.

Purpose: We performed a cost‐effectiveness analysis using the PHI (Prostate Health Index), 4Kscore®, SelectMDx™ and the EPI (ExoDx™ Prostate [IntelliScore]) in men with elevated prostate specific antigen to determine the need for biopsy. Materials and Methods: We developed a decision analytical model in men with elevated prostate specific antigen (3 ng/ml or greater) in which 1 biomarker test was used to determine which hypothetical individuals required biopsy. In the current standard of care strategy all individuals underwent biopsy. Model parameters were derived from a comprehensive review of the literature. Costs were calculated from a health sector perspective and converted into 2017 United States dollars. Results: The cost and QALYs (quality adjusted life‐years) of the current standard of care, which was transrectal ultrasound guided biopsy, was

A Kinked Health Insurance Market: Employer-Sponsored Insurance under the Cadillac Tax

3,863 and 18.085, respectively. Applying any of the 3 biomarkers improved quality adjusted survival compared to the current standard of care. The cost of SelectMDx, the PHI and the EPI was lower than performing prostate biopsy in all patients. However, the PHI was more costly and less effective than the SelectMDx strategy. The EPI provided the highest QALY with an incremental cost‐effectiveness ratio of

Modeling the Cost‐Effectiveness of Doula Care Associated with Reductions in Preterm Birth and Cesarean Delivery

58,404 per QALY. The use of biomarkers could reduce the number of unnecessary biopsies by 24% to 34% compared to the current standard of care. Conclusions: Applying biomarkers in men with elevated prostate specific antigen to determine the need for biopsy improved quality adjusted survival by decreasing the number of biopsies performed and the treatment of indolent disease. Using SelectMDx or the EPI following elevated prostate specific antigen but before proceeding to biopsy is a cost‐effective strategy in this setting.

Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis

The Affordable Care Act imposes a 40 percent excise tax on high-cost “Cadillac” health insurance plans in excess of defined thresholds beginning in 2020. Using economic theory and a microsimulation model, we predict how employers will respond to the Cadillac tax by adjusting wages and health insurance benefits. In its first year, 13.34 percent of individual and 16.73 percent of family employer-sponsored health insurance plan holders will be affected by the Cadillac tax; these percentages will increase to 35.33 and 42.01 percent, respectively, by 2025. Over 99 percent of those affected will reduce their health insurance benefits to the thresholds. Effectively, the Cadillac tax will impose a hard cap on health insurance benefits, causing a clustering of benefits at the thresholds and a sharp reduction in the variance of benefits. Revenue from the Cadillac tax through 2025 will total

Análisis de costo-beneficio: prevención del VIH/sida en migrantes en Centroamérica

204 billion, all but