Microsimulation Model to Compare Enteral and Parenteral Iron Supplementation in Children With Intestinal Failure.

BACKGROUND: Children with chronic intestinal failure have a high prevalence of anemia, commonly from iron deficiency, leading to frequent blood transfusions. No current guideline exists for iron supplementation in these children. In this analysis, we evaluate the effectiveness and the cost-effectiveness of using parenteral, enteral, and no iron supplementation to reduce blood transfusions. METHODS: We created a microsimulation model of pediatric intestinal failure over a 1-year time horizon. Model outcomes included cost (US dollars), blood transfusions received, and hemoglobin trend. Strategies tested included no supplementation, daily enteral supplementation, and monthly parenteral supplementation. We estimated parameters for the model using an institutional cohort of 55 patients. Model parameters updated each 1-month cycle using 2 regressions. A multivariate mixed-effects linear regression estimated hemoglobin values at the next month based on data from the prior month. A mixed-effects logistic regression on hemoglobin predicted the probability of receiving a blood transfusion in a given month. RESULTS: Compared with no supplementation, both enteral and parenteral iron supplementation reduced blood transfusions required per patient by 0.3 and 0.5 transfusions per year, respectively. Enteral iron cost $34 per avoided blood transfusion. Parenteral iron cost an additional $6600 per avoided blood transfusion compared with enteral iron. CONCLUSIONS: We found both parenteral and enteral iron to be effective at reducing blood transfusions. The cost of enteral iron makes it the desired choice in patients who can tolerate it. Future work should aim to identify which subpopulations of patients may benefit most from one strategy over the other.

A Multidimensional Array Representation of State-Transition Model Dynamics.

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.

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.

Some Health States Are Better Than Others: Using Health State Rank Order to Improve Probabilistic Analyses.

BACKGROUND: Probabilistic sensitivity analyses (PSA) may lead policy makers to take nonoptimal actions due to misestimates of decision uncertainty caused by ignoring correlations. We developed a method to establish joint uncertainty distributions of quality-of-life (QoL) weights exploiting ordinal preferences over health states. METHODS: Our method takes as inputs independent, univariate marginal distributions for each QoL weight and a preference ordering. It establishes a correlation matrix between QoL weights intended to preserve the ordering. It samples QoL weight values from their distributions, ordering them with the correlation matrix. It calculates the proportion of samples violating the ordering, iteratively adjusting the correlation matrix until this proportion is below an arbitrarily small threshold. We compare our method with the uncorrelated method and other methods for preserving rank ordering in terms of violation proportions and fidelity to the specified marginal distributions along with PSA and expected value of partial perfect information (EVPPI) estimates, using 2 models: 1) a decision tree with 2 decision alternatives and 2) a chronic hepatitis C virus (HCV) Markov model with 3 alternatives. RESULTS: All methods make tradeoffs between violating preference orderings and altering marginal distributions. For both models, our method simultaneously performed best, with largest performance advantages when distributions reflected wider uncertainty. For PSA, larger changes to the marginal distributions induced by existing methods resulted in differing conclusions about which strategy was most likely optimal. For EVPPI, both preference order violations and altered marginal distributions caused existing methods to misestimate the maximum value of seeking additional information, sometimes concluding that there was no value. CONCLUSIONS: Analysts can characterize the joint uncertainty in QoL weights to improve PSA and value-of-information estimates using Open Source implementations of our method.