Pro 2014

Manufactured by TreeAge

Sourced in United States

TreeAge Pro 2014 is a software application designed for decision analysis and modeling. It provides tools for creating and analyzing decision trees, Markov models, and other types of models used in healthcare, business, and other domains. The software offers features for data input, model construction, sensitivity analysis, and results presentation.

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Lab products found in correlation

Stata version 12, by StataCorp (1 mentions)

21 protocols using pro 2014

Sensitivity Analysis of Decision Model

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Deterministic and probabilistic sensitivity analyses were carried out to assess uncertainty and robustness of this model by evaluating the effects of differing model parameters. Probabilistic sensitivity analysis was conducted by 10 000 iterations of an automatic multiple random numbering method using Monte Carlo simulation. In the sensitivity analysis, the ranges of parameters varied were 95% confidence intervals for probability and ±30% for utility weights and drug costs.
All analyses in this study were conducted using TreeAge PRO 2014 (TreeAge Software, Inc., Williamstown, MA, USA).

Tsukiyama I., Hasegawa S., Ikeda Y., Takeuchi M., Tsukiyama S., Kurose Y., Ejiri M., Sakuma M., Saito H., Arakawa I., Inoue T., Yamaguchi E, & Kubo A. (2018). Cost‐effectiveness of aprepitant in Japanese patients treated with cisplatin‐containing highly emetogenic chemotherapy. Cancer Science, 109(9), 2881-2888.

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Cost-Effectiveness Analysis of Operative vs. Non-Operative Treatment

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In the base case analysis for each of the three cohorts, the incremental cost effectiveness ratio (ICER) of operative vs. non-operative treatment was expressed as cost per additional QALY gained. All analyses were performed utilizing TreeAge Pro 2014 software (TreeAge Software Inc., Williamstown, MA). All variables were varied in one-way sensitivity analyses in each of the age cohorts. The ranges for sensitivity analyses were determined based on clinical judgment and three systematic reviews published on this topic^{8 (link),10 (link),35} (Table 1). Ranges for the costs of treatment were defined as ± 50% of the index cost and ranges for costs of complications as $0 – three times the index cost (Table 2). In sensitivity analyses we used $100,000 per QALY gained as the benchmark for assessing cost-effectiveness.³⁶

Barlow D.R., Higgins B.T., Ozanne E.M., Tosteson A.N, & Pearson A.M. (2016). Cost Effectiveness of Operative vs. Non-operative Treatment of Geriatric Type-II Odontoid Fracture. Spine, 41(7), 610-617.

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Modeling Therapeutic Strategies for Major Depressive Disorder

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A Markov microsimulation model was constructed and analyzed in TreeAge Pro 2014 software. The model duration was 3 years with 2-monthly cycles. Eight health states were used to account for acute or continuing treatments, combinations of responsiveness to treatment and relapse options, and deaths (see File 2 in Supplemental Materials found at http://dx.doi.org/10.1016/j.jval. 2015.04.004). The MDD health states were based on the 17-item Hamilton Depression Rating Scale (HAM-D17), which is one of the most widely used and accepted measures for rating the severity of depression symptoms. In the absence of long-term clinical data, the duration of 3 years was chosen to track several courses of treatment per patient, which is typical of clinical practice.
Patients entered the model and moved between the various health states according to their treatments, their response to therapies, and their chance of remission or relapse. The probability of gaining remission or regressing varied according to the strategy under analysis (either rTMS or antidepressant). After this point, the model for both strategies was identical in incorporating the probabilities of receiving salvage treatments and their efficacy outcomes (ECT, augmentation, and hospitalization) and the probability of having adverse events during treatment.

Nguyen K.H, & Gordon L.G. (2015). Cost-Effectiveness of Repetitive Transcranial Magnetic Stimulation versus Antidepressant Therapy for Treatment-Resistant Depression. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 18(5).

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Probabilistic Sensitivity Analysis of Cost-Effectiveness

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Because model variables may not accurately represent reality, sensitivity analyses for all transition variables, treatment variables, and utilities were performed across wide ranges of potential values. Using one-way sensitivity analyses, we tested whether the relative cost-effectiveness of the model’s treatment strategies change as each variable’s assigned value is allowed to fluctuate. In order to perform probabilistic sensitivity analyses, each variable’s baseline value was replaced with a probabilistic distribution. These distributions were defined by values 50% above and below the baseline value. By randomly sampling from these distributions and repeatedly processing the model, a confidence interval representing the likelihood of cost-effectiveness could be depicted. Willingness to pay (WTP) was set at $50,000/dQALY. All analyses were conducted using TreeAge Pro 2014 (TreeAge Software, Inc). Retrospective data collection was approved by the University of Virginia Institutional Review Board (IRB protocol #10803).

Hu Y., Shah P., Stukenborg G.J, & Slingluff CL J.r. (2015). Utility of Sentinel Lymph Node Biopsy for Solitary Dermal Melanomas. Journal of surgical oncology, 111(7), 800-807.

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Cost-Effectiveness Analysis Using TreeAge

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We created a decision tree and performed the analysis using TreeAge Pro 2014 (TreeAge Software, Williamstown, MA). The mean costs, also known as the expected value, for CIMRT and HIMRT were determined by adjusting the mean cost of patients who were categorized into each terminal branch of the model with the probabilities in the terminal branch and in each preceding branch. Descriptive analysis was performed using STATA version 12 (StataCorp, College Station, TX).

Voong K.R., Lal L.S., Kuban D.A., Pugh T.J., Swint J.M., Godby J., Choi S., Lee A.K., Schlembach P.J., Frank S.J., McGuire S.E, & Hoffman K.E. (2017). Long-term economic value of hypofractionated prostate radiation: Secondary analysis of a randomized trial. Advances in Radiation Oncology, 2(3), 249-258.

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Modeling Peak Oral Mucositis Risk

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High quality trials were mapped to a decision tree using TreeAge Pro 2014 version R2.2 (TreeAge Pro , 2014 ). The risk of developing peak OM data was imputed by sensitivity, specificity, and pre-test or post-test Bayesian probability (Gelman, 2004 ). In the first node, equal weight (0.5) was assigned to treatment and controls for each mineral derivative arm. Sensitivity and specificity was calculated using MedCalc (Charlie’s Clinical Calculators, 2014 ) in conjunction with statistical program R (R Core Team, 2014 ) for subsequent nodes (2 × 2 contingency tables; participants with peak OM or not), and terminal nodes assigned pre-test (control), or post-test (treatment) Bayesian probability which defined ‘risk’ as the pre- or posttest predictive value in developing peak OM (Petitti, 2000 ). The pre-test (prevalence) of developing OM was set high “0.8” based on high risk groups developing OM during cancer therapy (Sonis, 2009 (link)). The tree was then “rolled back” in estimate of probable risks in developing peak OM or not, and in deciding which treatment was the most effective.

, & Lee S. (2015). Mineral derivatives in alleviating oral mucositis during cancer therapy: a systematic review. PeerJ, 3, e765.

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Cost-Effectiveness Analysis in Healthcare

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The incremental cost-effectiveness ratio (ICER) based on the number of quality-adjusted life-years (QALYs) were calculated. The calculation was conducted using Tree-Age Pro 2014 software (Tree-Age Software, Inc., Boston, Massachusetts).^{[12 (link)]} Each cycle length was 8 weeks. Estimate of the cost was obtained from Dutch payment and Hebei Province Hospital System, China. A threshold limit of €30,000/QALY and ¥82,442/QALY was adopted according to the literature^{[13 (link)]} and the average annual salary of local employees in China.

Wang X., Li Q., Sun S., Liang X., Li H., Huang J., Zhao T., Hu J., Liu J., Hu Z., Duan Y, & He J. (2022). Network meta-analysis and cost-effectiveness analysis of infliximab, cyclosporine and tacrolimus for ulcerative colitis. Medicine, 101(51), e31850.

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Dynamic HPV Transmission and Cervical Cancer Modeling

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A dynamic model of HPV transmission and vaccination (implemented in Microsoft Visual Studio C++), coupled with a deterministic Markov model of the natural history of CIN and cervical screening and invasive cervical cancer survival (implemented using TreeAge Pro 2014, TreeAge Software, Inc., MA, USA), was used to simulate cervical disease and screening in NZ. This model platform, adaptable to different settings, has been previously used to evaluate various cervical screening and follow-up management strategies in Australia, NZ and England [10 ,15 (link),16 (link),17 ,18 ,19 (link),20 ,21 (link)]. Most recently, it has been used to evaluate primary HPV screening in both unvaccinated women and cohorts offered vaccination in England [9 ] and in Australia [10 ]; the Australian findings have underpinned the recent recommendations to transition to primary HPV screening in that country.

Lew J.B., Simms K., Smith M., Lewis H., Neal H, & Canfell K. (2016). Effectiveness Modelling and Economic Evaluation of Primary HPV Screening for Cervical Cancer Prevention in New Zealand. PLoS ONE, 11(5), e0151619.

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Markov Model for Fanconi Anemia Treatment Strategies

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The structure of our model is based on the earlier model we created for patients with biallelic mutations in FANCD1/BRCA2 [8 (link)], allowing application to patients with FA of all ages and other genotypes. Specifically, we developed mathematical (Markov) models of event-free survival (EFS) [9 (link)] to reflect the natural history of FA, a strategy that we call standard care (Figure 1). Patients receiving standard care are at risk of BMF, AML, and ST. BMF in this context means marrow failure severe enough to lead to transplantation or death from aplastic anemia. We then developed a model for the competing strategy of PE-BMT in which the hematologic risks (BMF and AML) were eliminated by transplantation but an independent risk of TRM was introduced. The primary outcome was EFS, defined as freedom from BMF, AML, ST, or TRM. Note that our estimate of TRM means mortality, not morbidity. The models were analyzed using TreeAge Pro 2014 (TreeAge Software, Inc, Williamstown, MA). To obtain mathematically smooth outputs for tabulation and plotting, each model was run for 200 cycles with a cycle length of 3-months, all rates were transformed into 3-month probabilities, and we used a half-cycle correction [10 (link)].

Khan N.E., Rosenberg P.S, & Alter B.P. (2016). Preemptive Bone Marrow Transplantation and Event-Free Survival in Fanconi Anemia. Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation, 22(10), 1888-1892.

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Cost-Effectiveness Analysis of Clinical Strategies

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We calculated and compared incremental cost‐effectiveness ratios (ICERs) for the 5 clinical treatment strategies using the lifetime costs and QALYs projected by the simulation model for each clinical strategy. We assessed cost effectiveness based on a cost‐effectiveness threshold of $100 000 per QALY, representing the willingness of a healthcare system to pay for care.33 We conducted the analysis from a healthcare system perspective throughout a lifetime horizon with all costs in 2019 US dollars and all future healthcare costs and QALYs discounted at 3% annually.34 The model was programmed in Tree‐Age Pro 2014 (TreeAge Software, Williamstown, MA).
Parameters were varied individually in 1‐way sensitivity analyses to evaluate the sensitivity of the results to plausible variations in model inputs. Overall model uncertainty was evaluated in probabilistic sensitivity analysis by simultaneously conducting 10 000 random draws from probability distributions for each variable and recalculating cost effectiveness for each iteration within the model.

Baradaran H., Gupta A., Anzai Y., Mushlin A.I., Kamel H, & Pandya A. (2019). Cost Effectiveness of Assessing Ultrasound Plaque Characteristics to Risk Stratify Asymptomatic Patients With Carotid Stenosis. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 8(21), e012739.

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