APPLYING RISK ADJUSTED COST‐EFFECTIVENESS (RAC‐E) ANALYSIS TO HOSPITALS: ESTIMATING THE COSTS AND CONSEQUENCES OF VARIATION IN CLINICAL PRACTICE

Cost‐effectiveness analysis is well established for pharmaceuticals and medical technologies but not for evaluating variations in clinical practice. This paper describes a novel methodology—risk adjusted cost‐effectiveness (RAC‐E)—that facilitates the comparative evaluation of applied clinical practice processes. In this application, risk adjustment is undertaken with a multivariate matching algorithm that balances the baseline characteristics of patients attending different settings (e.g. hospitals). Linked, routinely collected data are used to analyse patient‐level costs and outcomes over a 2‐year period, as well as to extrapolate costs and survival over patient lifetimes. The study reports the relative cost‐effectiveness of alternative forms of clinical practice, including a full representation of the statistical uncertainty around the mean estimates. The methodology is illustrated by a case study that evaluates the relative cost‐effectiveness of services for patients presenting with acute chest pain across the four main public hospitals in South Australia. The evaluation finds that services provided at two hospitals were dominated, and of the remaining services, the more effective hospital gained life years at a low mean additional cost and had an 80% probability of being the most cost‐effective hospital at realistic cost‐effectiveness thresholds. Potential determinants of the estimated variation in costs and effects were identified, although more detailed analyses to identify specific areas of variation in clinical practice are required to inform improvements at the less cost‐effective institutions. Copyright © 2012 John Wiley & Sons, Ltd.     

Mechanism-based inactivation of human cytochrome P450 enzymes: strategies for diagnosis and drug–drug interaction risk assessment

Among drugs that cause pharmacokinetic drug–drug interactions, mechanism-based inactivators of cytochrome P450 represent several of those agents that cause interactions of the greatest magnitude. In vitro inactivation kinetic data can be used to predict the potential for new drugs to cause drug interactions in the clinic. However, several factors exist, each with its own uncertainty, that must be taken into account in order to predict the magnitude of interactions reliably. These include aspects of in vitro experimental design, an understanding of relevant in vivo concentrations of the inactivator, and the extent to which the inactivated enzyme is involved in the clearance of the affected drug. Additionally, the rate of enzyme degradation in vivo is also an important factor that needs to be considered in the prediction of the drug interaction magnitudes. To address mechanism-based inactivation for new drugs, various in vitro experimental approaches have been employed. The selection of approaches for in vitro kinetic characterization of inactivation as well as in vitro–in vivo extrapolation should be guided by the purpose of the exercise and the stage of drug discovery and development, with an increase in the level of sophistication throughout the research and development process.     

Allometric principles for interspecies extrapolation in toxicological risk assessment--empirical investigations

Four types of data (toxicokinetic data of pharmaceuticals from six species including humans, LD(50) values from eight animal species, long-term NOAEL values of pesticides from mice, rats, and dogs, and toxicity data on anti-neoplastic agents from six species including humans) were used for interspecies comparisons. Species differences with regard to kinetic parameters and toxicity were evaluated and the concordance with predictions by allometric scaling according to caloric demand (allometric exponent 0.75) or to body weight (allometric exponent 1) was checked. For LD(50) values, agreement was poor for both allometric concepts. Recently reported concordance of LD(50) species differences with body weight scaling could be traced back to biased data selection. The other three datasets are clearly in agreement with the allometric scaling according to caloric demand. Caloric demand scaling is thus proposed as a generic interspecies extrapolation method in the absence of substance-specific data. Moreover, the evaluated data make it possible to describe uncertainty associated with the process of interspecies extrapolation by allometric rules.     

Dose-Response Curves in Chemical Carcinogenesis

Extrapolation from studies of chemical carcinogenicity in rodents, at high doses, to humans, at the typically low doses to which we are exposed, has been one of the most controversial issues in toxicology. Many chemical carcinogenesis experiments currently are evaluated on a linear scale for dose. Log dose has been the standard for decades in pharmacology and toxicology for noncancer toxicities and there is no reason to think that it should not apply to chemical carcinogenesis. Furthermore, log dose is consistent with fundamental principles of chemistry. Direct comparisons of linear and logarithmic scales for dose illustrate the deceptive nature of linear plots for dose; low doses, which is where our interest lies in comparing human exposures, are compressed beyond evaluation by a linear scale. Unequivocal thresholds for carcinogenicity are shown when the dose-response curves for animal studies done at high doses are evaluated on a log scale for dose. This observation now raises the issue of the relevance to human exposures of these high-dose experiments in animals. Studies analyzed by this log dose to linear response procedure demonstrate that the thresholds from animal experiments can be used to calculate safety factors for human exposure and that humans are more resistant than animals to carcinogenesis from chemicals.     

Coronavirus Disease 2019: Considerations for Health Technology Assessment From the National Centre for Pharmacoeconomics Review Group

It is expected that the coronavirus disease 2019 (COVID-19) pandemic will leave large deficits in the budgets of many jurisdictions. Funding for other treatments, in particular new treatments, may become more constrained than previously expected. Therefore, a robust health technology assessment (HTA) system is vital. Many clinical trials carried out during the pandemic may have been temporarily halted, while others may have had to change their protocols. Even trials that continue as normal may experience external changes as other aspects of the healthcare service may not be available to the patients in the trial, or the patients themselves may contract COVID-19. Consequently, many limitations are likely to arise in the provision of robust HTAs, which could have profound consequences on the availability of new treatments. Therefore, the National Centre for Pharmacoeconomics Review Group wishes to discuss these issues and make recommendations for applicants submitting to HTA agencies, in ample time for these HTAs to be prepared and assessed. We discuss how the pandemic may affect the estimation of the treatment effect, costs, life-years, utilities, discontinuation rates, and methods of evidence synthesis and extrapolation. In particular, we note that trials conducted during the pandemic will be subject to a higher degree of uncertainty than before. It is vital that applicants clearly identify any parameters that may be affected by the pandemic. These parameters will require considerably more scenario and sensitivity analyses to account for this increase in uncertainty.     

Some aspects relating to the evaluation of the effects of chemicals on male fertility

Reviews and studies on individual compounds were analyzed as to the suitability of different study designs and endpoints for detecting adverse effects of chemicals on male reproduction in animal species. Of the endpoints investigated, the most sensitive proved to be histopathology of the testes. Using refined histopathology, effects could be detected with a high degree of sensitivity as early as 4 weeks after treatment. Other sensitive endpoints were the weights of reproductive organs, including accessory glands, i.e., testis, epididymis, prostate, and of the seminal vesicle, as well as sperm parameters such as sperm count, sperm morphology, and sperm motility. Sperm motility was found to be in some cases more sensitive than histopathology. The above parameters showed a higher sensitivity than fertility parameters. In fact, in most cases, not only one but several endpoints were affected. Continuous breeding studies and 90-day studies with additional measurements of sperm parameters were similarly effective in detecting compounds which affect male fertility. Interspecies extrapolation factors (IEFs) have been derived for the most sensitive endpoints in laboratory animals. If the calculation is based on caloric demand and a sensitive endpoint of reproductive toxicity, many IEFs tend to be about 1, indicating that humans are generally not more susceptible to reproductive toxicants than laboratory animals. With respect to hazard identification, it is possible to detect adverse effects on male reproduction in a standard subacute study with concentrations that produce significant general toxicity. If effects are found, for the risk assessment the NOAEL has to be determined by testing specific sensitive parameters as specified above.     

The efficiency variation method for 4πβ−γ coincidence counting by ink-jet printing

In order to vary the counting efficiencies in the 4πβ−γ coincidence extrapolation technique, a radioactive source was coated directly with varying amounts of an electrical conducting pigment using an ink-jet printer. This method can be used to efficiently prepare the multiple sources needed to generate efficiency extrapolation curves, and was successfully applied to the standardization of a ⁵⁴Mn source.     

新疆兵团企业职工基本养老保险基金运行情况研究

[目的]分析2009-2019年新疆兵团企业职工基本养老保险制度运行情况,并预测2020-2024年的情况.[方法]利用2009-2019年新疆兵团统计年鉴相关数据,分析兵团企业职工基本养老保险制度运行状况;采用简单趋势外推法,估算2020-2024年兵团的基金收支额以及收支结余情况.[结果]2009-2019年兵团企...     

Value of External Data in the Extrapolation of Survival Data: A Study Using the NJR Data Set

Background

Extrapolation of time-to-event data can be a critical component of cost-effectiveness analysis.

Estimation of time‐shift models with application to survival calibration in health technology assessment

The incremental life expectancy, defined as the difference in mean survival times between two treatment groups, is a crucial quantity of interest in cost‐effectiveness analyses. Usually, this quantity is very difficult to estimate from censored survival data with a limited follow‐up period. The paper develops estimation procedures for a time‐shift survival model that, provided model assumptions are met, gives a reliable estimate of incremental life expectancy without extrapolation beyond the study period. Methods for inference are developed both for individual patient data and when only published Kaplan–Meier curves are available. Through simulation, the estimators are shown to be close to unbiased and constructed confidence intervals are shown to have close to nominal coverage for small to moderate sample sizes. Copyright © 2016 John Wiley & Sons, Ltd.