Biomedical subjects

Josephine A Mauskopf

Publications and source records attributed to Josephine A Mauskopf.

5 recordsLinked to original sources

PubMed2006

Minimizing resistance consequences after virologic failure on initial combination therapy: a systematic overview.

OBJECTIVE: To identify optimal first-line therapies based on the rate of virologic success (VS) and the preservation of future treatment options in antiretroviral therapy (ART)-naive subjects. DESIGN: Systematic overview of genotypic resistance mutations from clinical trials of combination ART. METHODS: Various sources were searched for studies in ART-naive subjects providing virologic response rates and genotypes from subjects with virologic failure. The International AIDS Society-USA genotypic resistance guidelines were used to calculate regimen resistance cost (RCreg) and number of active drug (AD) scores for each regimen and to rank the regimens. RESULTS: Intra- and interstudy comparisons showed higher VS rates for nonnucleoside reverse transcriptase inhibitor (NNRTI) regimens (range: 51%-76%) and boosted protease inhibitor (boosted PI) regimens (range: 55%-79%). Boosted PI failures had the lowest RCreg (range: 0.12-0.21) and the highest AD (range: 19.80-20.18) scores. NNRTI failures had higher RCreg (range: 0.00-1.22) and lower AD (range: 16.83-21) scores. CONCLUSIONS: NNRTI and boosted PI regimens provide the highest rates of VS in treatment-naive HIV-infected persons. Treatment option scores were higher in subjects who failed boosted PI- containing regimens versus NNRTI-containing regimens, however.

Anti-HIV Agents↗

PubMed2006

Optimal assignment of treatments to health states using a Markov decision model: an introduction to basic concepts.

Assessing the cost effectiveness of a new health intervention often requires modelling to estimate the impact of the intervention on cost, survival and quality of life over the lifetime of a cohort of patients. Markov modelling is a methodology that is commonly employed to estimate these long-term costs and benefits. As commonly used, these models assume that the patients continue to get the treatments assigned regardless of the change in health states. In this paper, we describe an extension to the Markov modelling approach, called Markov decision modelling. Such a model starts with a set of health states and treatments and optimally assigns treatments to each of the health states. A Markov decision model can be used to identify the optimal treatment strategy not just for the initial disease state, but also as the disease state changes over time. We present a dynamic programming approach to identifying the optimal assignment of treatments, and illustrate this methodology using an example. The Markov decision modelling approach provides an efficient way of identifying optimal assignment of treatments to health states, but, like the standard Markov model, may be of limited use when probabilities of future events depend on past history in a complex fashion. Even with its limitations, Markov decision models offer an opportunity for health economists to inform healthcare decision-makers on how to modify current treatment pathways to incorporate new treatments as they become available.

CD4 Lymphocyte Count↗

PubMed2005

Drug persistency patterns for patients treated with rivastigmine or donepezil in usual care settings.

OBJECTIVE: To compare levels of persistency with 2 cholinesterase (ChE) inhibitors--rivastigmine and donepezil--for the treatment of Alzheimer's disease (AD) through the use of administrative claims data. METHODS: This retrospective cohort study identified treatment-naive, community-based AD patients having an initial prescription (index event) for rivastigmine or donepezil between June and December 2000, in the United States, from pharmacy claims in a proprietary administrative claims database. Patients were excluded if they received either drug during the 180 days prior to their index prescription or if they did not have continuous plan enrollment during this period and for at least 90 days following the index date. The probability of treatment discontinuation within the first 60 days of treatment was estimated. Time to treatment discontinuation was analyzed for the cohort of patients that remained on therapy > or =60 days as well as for subgroups of the cohort reaching either approved or maximum recommended doses of donepezil or rivastigmine. Treatment discontinuation was defined as either a stop of therapy (no prescription refill within 60 days of estimated completion of prior prescription) or a switch to an alternative AD drug. Kaplan-Meier survival and proportional hazard model analyses were performed. Proportion of days covered (PDC) by an AD therapy was also evaluated in each quarter during the first year of follow-up. RESULTS: Of the newly treated AD study population, 30.4% (171/563) of rivastigmine patients and 31.2% (583/1,871) of donepezil patients discontinued treatment within 60 days of starting therapy (P = 0.72). For the cohort of patients that remained on therapy > or =60 days, the mean time to treatment discontinuation was 331 days (95% confidence interval [CI], 307-355) for rivastigmine patients (n = 392) versus 337 days (95% CI, 322-352) for donepezil patients (n = 1288). The proportion of patients with a PDC > or =80% after 12 months of follow-up was 23% for the donepezil group and 19% for the rivastigmine group (P = 0.34). For the cohort subgroup that reached an approved dose, the mean time to treatment discontinuation was 346 days (95% CI, 318-374) for rivastigmine patients (n = 282) versus 338 days (95% CI, 323-353) for donepezil patients (n = 1,283). For the cohort subgroup that reached the maximum recommended dose, the mean time to treatment discontinuation was 396 days (95% CI, 343-449) for rivastigmine patients (n = 61) versus 364 days (95% CI, 344-384) for donepezil patients (n = 712). CONCLUSION: Newly treated AD patients in a usual care setting who initiate therapy with either rivastigmine or donepezil have similar levels of persistency with treatment.

Aged↗

PubMed2002

A Monte Carlo simulation for modelling outcomes of AIDS treatment regimens.

Healthcare providers are often faced with the need to make treatment decisions about antiretroviral therapy regimens for patients with HIV infection without definitive information as to the optimal sequencing of drug regimens. In this paper we describe our model developed to simulate the impact of differing drug sequencing strategies used in the treatment of patients with HIV/AIDS. In addition, we present the methods used to estimate the efficacy and duration of antiretroviral therapies to populate the model at baseline. We conducted a hypothetical application of the model using three alternative series of therapies: a series containing no drug therapy, a series starting with a protease inhibitor-containing regimen, and a series starting with a non-nucleoside reverse transcriptase inhibitor-containing regimen. The model described in this article can be used to compare and contrast the relative impact of different treatment guidelines in the absence of necessary clinical data and may serve as a means of evaluation until clinical trials or observational databases provide guidance as to the optimal treatment regimen(s). The model is flexible and permits adaptation to the changing treatment environment in HIV infection.

Acquired Immunodeficiency Syndrome↗

PubMedDate not supplied

Conditions for the near equivalence of cost-effectiveness and cost-benefit analyses.

The equivalence of cost-effectiveness analysis (CEA) and cost-benefit analysis (CBA) has been vigorously debated in the health economic literature. In this paper we review and refine the conditions for the equivalence of CEA and CBA. The previously stated conditions require that 1) each individual's willingness to pay (WTP) per quality-adjusted life year (QALY) is constant and does not vary with the magnitude of QALY gains, and 2) the WTP per QALY is identical for every individual in society. Based on mathematical programming formulations of CEA and CBA, we note that condition 2 can be replaced with two other conditions, which together are less restrictive than the requirement that every individual have the same WTP per QALY. Even with this less restrictive set of conditions, CEA and CBA are unlikely to be equivalent under real world conditions. When CEA and CBA do lead to different resource allocation decisions, the most appropriate framework for health economic analysis depends on the perspective regarding distribution issues. We also examine the equivalence of two different definitions of CEA provided in the literature and discuss the problems that could arise when there are multiple optima.

Attitude to Health↗