Publications by authors named "Daniel M Sheinson"

Background: Clinical practice guidelines recommend broad-panel genomic profiling to identify actionable genomic alterations for patients with advanced non-small cell lung cancer (aNSCLC).

Objective: To assess the cost-effectiveness of large-panel next-generation sequencing (LP-NGS) compared with current empirical single-gene test (SGT) patterns to inform first-line treatment decisions for patients with aNSCLC from a US commercial payer perspective, accounting for the effect of testing turnaround time and time to treatment initiation.

Methods: We developed a discrete-event simulation model to estimate the impact of LP-NGS vs SGT for patients with nonsquamous aNSCLC.

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Importance: In March 2018, Medicare issued a national coverage determination (NCD) for next-generation sequencing (NGS) to facilitate access to NGS testing among Medicare beneficiaries. It is unknown whether the NCD affected health equity issues for Medicare beneficiaries and the overall population.

Objective: To examine the association between the Medicare NCD and NGS use by insurance types and race and ethnicity.

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Purpose: In 2018, Medicare issued a national coverage determination (NCD) providing reimbursement for next-generation sequencing (NGS) tests for beneficiaries with advanced or metastatic cancer and no previous NGS testing. We examined the association between NCD implementation and NGS utilization trends in Medicare beneficiaries versus commercially insured patients.

Methods: This was a retrospective study of patients with advanced non-small-cell lung cancer (aNSCLC), metastatic colorectal cancer (mCRC), metastatic breast cancer (mBC), or advanced melanoma with a de novo or recurrent advanced diagnosis from January 1, 2011, through December 30, 2019, using a nationwide US electronic health record-derived deidentified database.

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Introduction: Coronavirus disease 2019 (COVID-19) has imposed a considerable burden on the United States (US) health system, with particular concern over healthcare capacity constraints.

Methods: We modeled the impact of public and private sector contributions to developing diagnostic testing and treatments on COVID-19-related healthcare resource use.

Results: We estimated that public sector contributions led to at least 30% reductions in COVID-19-related healthcare resource utilization.

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Purpose: The National Comprehensive Cancer Network (NCCN) developed the Evidence Blocks framework to assess the value of oncology regimens. This study characterizes the relationship between real-world costs and NCCN affordability ratings (ARs) for advanced non-small-cell lung cancer (aNSCLC) treatments.

Methods: Using the MarketScan and PharMetrics Plus databases, we identified patients treated between 2012 and 2017 with an aNSCLC regimen evaluated by the NCCN Evidence Blocks.

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Article Synopsis
  • The study introduces a methodology for sequential inference in nonlinear stochastic state-space models, focusing on estimating both dynamic states and fixed parameters simultaneously.
  • It highlights the limitations of basic particle filters due to parameter estimation issues and proposes using kernel density approximation to improve parameter regeneration.
  • Additionally, it discusses how the choice of priors impacts posterior inferences and recommends using more constrained priors, while also advocating for better resampling techniques in particle filters, illustrated through a disease outbreak tracking model.
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