Impact of in-house specialty pharmacy on access to novel androgen axis inhibitors in men with advanced prostate cancer

Introduction: Novel androgen axis inhibitors are standard of care treatments in advanced prostate cancer. The billed amounts for these medications are often very high, which may create significant financial toxicity for patients and lead to delays in treatment. Our institution implemented an in-house specialty pharmacy in 2014, that provides these medications and evaluates copay assistance options for all patients. We evaluated the program’s impact on out of pocket cost (OOP) and turnaround time (TAT). Methods: We reviewed available internal specialty pharmacy records to identify prescriptions for abiraterone or enzalutamide filled between 1/1/17 and 12/31/18. Payments were stratified by primary payment (amount reimbursed by the patient’s prescription plan based on the benefit’s design) and copayment assistance. Turnaround times (TAT) in business days were stratified by prescriptions requiring intervention (prior authorization, copayment assistance, or insufficient inventory) and clean prescriptions (those requiring no intervention). Results: One thousand four hundred seventeen prescriptions for 175 unique patients requiring abiraterone (n=869, 61.3%) or enzalutamide (n=548, 38.7%) were filled through the institution’s specialty pharmacy. The average amount paid by primary payer was $9,492.96 for a 30 day supply (range:$3,382.48-$12,939.84). Average quoted copay was$577.53 (range $3.08-$10,560.39). 64% of patients received copayment assistance. Average OOP cost per prescription after co-pay assistance was $100.83 (range$0-$8556.64). Three patients declined treatment due to cost (1.7% of overall). Average TAT was 2.98 days for clean prescriptions and 3.36 days for prescriptions needing intervention (p=0.055). Discussion: OOP cost varied significantly based on plan design and copayment assistance eligibility. The majority of patients received copayment assistance, which markedly reduced OOP cost. Cost rarely precluded access to treatment. TAT was not significantly prolonged for prescriptions requiring intervention. Further studies to determine impact of pharmacy type on access to specialty medications are indicated

"We Really Help, Taking Care of Each Other": Older Homeless Adults as Caregivers.

Objectives:Many older homeless adults maintain contact with family. We conducted a qualitative study examining the role of family caregiving for older homeless adults. Method:We conducted semi-structured qualitative interviews with a sample of 46 homeless participants who reported spending at least one night with a housed family member in the prior 6 months. Results:A total of 13 of 46 older adult participants provided caregiving. Themes included (a) the death of the care recipient led to the participant's homelessness; (b) feeling a duty to act as caregivers; (c) providing care in exchange for housing; (d) caregivers' ability to stay was tenuous; (e) providing care conflicted with the caregiver's needs; and (f) resentment when family was ungrateful. Discussion:In a sample of older homeless adults in contact with family, many provided caregiving for housed family. For some, caregiving precipitated homelessness; for others, caregiving provided temporary respite from homelessness, and for others, caregiving continued during homelessness

Family Weekend Open House

On Oct. 1, explore the UD Libraries at our Family Weekend open house, including an exhibit of recent additions of books, art and artifacts to the Marian Library collections

Statistical analysis of proteomic mass spectrometry data

This thesis considers the statistical modelling and analysis of proteomic mass spectrometry data. Proteomics is a relatively new field of study and tried and tested methods of analysis do not yet exist. Mass spectrometry output is high-dimensional and so we firstly develop an algorithm to identify peaks in the spectra in order to reduce the dimensionality of the datasets. We use the results along with a variety of classification methods to examine the classification of new spectra based on a training set. Another method to reduce the complexity of the problem is to fit a parametric model to the data. We model the data as a mixture of Gaussian peaks with parameters representing the peak locations, heights and variances, and apply a Bayesian Markov chain Monte Carlo (MCMC) algorithm to obtain their estimates. These resulting estimates are used to identify m/z values where differences are apparent between groups, where the m/z value of an ion is its mass divided by its charge. A multilevel modelling framework is also considered to incorporate the structure in the data and locations exhibiting differences are again obtained. We consider two mass spectrometry datasets in detail. The first consists of mass spectra from breast cancer cells which either have or have not been treated with the chemotherapeutic agent Taxol. The second consists of mass spectra from melanoma cells classified as stage I or stage IV using the TNM system. Using the MCMC and multilevel techniques described above we show that, in both datasets, small subsets of the available m/z values can be identified which exhibit significant differences in protein expression between groups. Also we see that good classification of new data can also be achieved using a small number of m/z values and that the classification rate does not fall greatly when compared with results from the complete spectra. For both datasets we compare our results with those in the literature which use other techniques on the same data. We conclude by discussing potential areas for further research

A Primer on the Human Readiness Level Scale (ANSI/HFES 400-2021)

The Human Readiness Level (HRL) Scale is a simple 9-level scale for evaluating, tracking, and communicating the readiness of a technology for safe and effective human use. It is modeled after the well-established Technology Readiness Level (TRL) framework that is used throughout the government and industry to communicate the maturity of a technology and to support decision making about technology acquisition. Here we (1) introduce the ANSI/HFES 400-2021 Standard that defines the HRL scale and (2) provide concrete examples of evaluation activities to support the application of HRLs in the development of automated driving systems

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Writing in Britain and Ireland, c. 400 to c. 800

No abstract available

Multi-arm multi-stage (MAMS) randomised selection designs:Impact of treatment selection rules on the operating characteristics

Background: Multi-arm multi-stage (MAMS) randomised trial designs have been proposed to evaluate multiple research questions in the confirmatory setting. In designs with several interventions, such as the 8-arm 3-stage ROSSINI-2 trial for preventing surgical wound infection, there are likely to be strict limits on the number of individuals that can be recruited or the funds available to support the protocol. These limitations may mean that not all research treatments can continue to accrue the required sample size for the definitive analysis of the primary outcome measure at the final stage. In these cases, an additional treatment selection rule can be applied at the early stages of the trial to restrict the maximum number of research arms that can progress to the subsequent stage(s).This article provides guidelines on how to implement treatment selection within the MAMS framework. It explores the impact of treatment selection rules, interim lack-of-benefit stopping boundaries and the timing of treatment selection on the operating characteristics of the MAMS selection design.Methods: We outline the steps to design a MAMS selection trial. Extensive simulation studies are used to explore the maximum/expected sample sizes, familywise type I error rate (FWER), and overall power of the design under both binding and non-binding interim stopping boundaries for lack-of-benefit.Results: Pre-specification of a treatment selection rule reduces the maximum sample size by approximately 25% in our simulations. The familywise type I error rate of a MAMS selection design is smaller than that of the standard MAMS design with similar design specifications without the additional treatment selection rule. In designs with strict selection rules - for example, when only one research arm is selected from 7 arms - the final stage significance levels can be relaxed for the primary analyses to ensure that the overall type I error for the trial is not underspent. When conducting treatment selection from several treatment arms, it is important to select a large enough subset of research arms (that is, more than one research arm) at early stages to maintain the overall power at the pre-specified level.Conclusions: Multi-arm multi-stage selection designs gain efficiency over the standard MAMS design by reducing the overall sample size. Diligent pre-specification of the treatment selection rule, final stage significance level and interim stopping boundaries for lack-of-benefit are key to controlling the operating characteristics of a MAMS selection design. We provide guidance on these design features to ensure control of the operating characteristics