Abstract Background The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. Objective The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for Conjoint Analysis Task Force was established to identify good research practices for conjoint-analysis applications in health. Methods The task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. ISPOR members contributed to this process through an extensive consultation process. A final consensus meeting was held to revise the article using these comments, and those of a number of international reviewers. Results Task force findings are presented as a 10-item checklist covering: 1) research question; 2) attributes and levels; 3) construction of tasks; 4) experimental design; 5) preference elicitation; 6) instrument design; 7) data-collection plan; 8) statistical analyses; 9) results and conclusions; and 10) study presentation. A primary question relating to each of the 10 items is posed, and three sub-questions examine finer issues within items. Conclusions Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies.
Abstract Background Budget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission. Objectives The objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions. Methods The Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research. Results The Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision maker’s population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision maker’s own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis. Conclusions We recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.
Abstract Objective There is growing recognition that a comprehensive economic assessment of a new health-care intervention at the time of launch requires both a cost-effectiveness analysis (CEA) and a budget impact analysis (BIA). National regulatory agencies such as the National Institute for Health and Clinical Excellence in England and Wales and the Pharmaceutical Benefits Advisory Committee in Australia, as well as managed care organizations in the United States, now require that companies submit estimates of both the cost-effectiveness and the likely impact of the new health-care interventions on national, regional, or local health plan budgets. Although standard methods for performing and presenting the results of CEAs are well accepted, the same progress has not been made for BIAs. The objective of this report is to present guidance on methodologies for those undertaking such analyses or for those reviewing the results of such analyses. Methods The Task Force was appointed with the advice and consent of the Board of Directors of ISPOR. Members were experienced developers or users of budget impact models, worked in academia, industry, and as advisors to governments, and came from several countries in North America, Oceana, Asia, and Europe. The Task Force met to develop core assumptions and an outline before preparing a draft report. They solicited comments on the outline and two drafts from a core group of external reviewers and more broadly from the membership of ISPOR at two ISPOR meetings and via the ISPOR web site. Results The Task Force recommends that the budget impact of a new health technology should consider the perspective of the specific health-care decision-maker. As such, the BIA should be performed using data that reflect, for a specific health condition, the size and characteristics of the population, the current and new treatment mix, the efficacy and safety of the new and current treatments, and the resourceuse and costs for the treatments and symptoms as would apply to the population of interest. The Task Force recommends that budget impact analyses be generated as a series of scenario analyses in the same manner that sensitivity analyses would be provided for CEAs. In particular, the input values for the calculation and the specific cost outcomes presented (a scenario) should be specific to a particular decision-maker's population and information needs. Sensitivity analysis should also be in the form of alternative scenarios chosen from the perspective of the decision-maker. The primary data sources for estimating the budget impact should be published clinical trial estimates and comparator studies for efficacy and safety of current and new technologies as well as, where possible, the decision-maker's own population for the other parameter estimates. Suggested default data sources also are recommended. These include the use of published data, well-recognized local or national statistical information and in special circumstances, expert opinion. Finally, the Task Force recommends that the analyst use the simplest design that will generate credible and transparent estimates. If a health condition model is needed for the BIA, it should reflect health outcomes and their related costs in the total affected population for each year after the new intervention is introduced into clinical practice. The model should be consistent with that used for the CEA with regard to clinical and economic assumptions. Conclusion The BIA is important, along with the CEA, as part of a comprehensive economic evaluation of a new health technology. We propose a framework for creating budget impact models, guidance about the acquisition and use of data to make budget projections and a common reporting format that will promote standardization and transparency. Adherence to these proposed good research practice principles would not necessarily supersede jurisdiction-specific budget impact guidelines, but may support and enhance localrecommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.
The objective of this study was to evaluate the efficacy and safety of atomoxetine in children and adolescents.We searched for studies published between 1985 and 2006 through Medline, PubMed, PsychInfo and Cochrane Central Register of Controlled Trials (CENTRAL 2006 Issue 3) using keywords related to atomoxetine and attention-deficit/hyperactivity disorder (ADHD) and scanned though reference lists. We included nine randomized placebo-controlled trials (atomoxetine:placebo = 1,150:678).Atomoxetine was superior (p < 0.01) to placebo in reducing ADHD symptoms across different scales (Attention-Deficit/Hyperactivity Disorder Rating Scale-IV, Conners’ Parent and Teacher Rating Scales-Revised:Short Form, Clinical Global Impression-Severity) rated by different raters (parent, teacher, clinician). The number-needed-to-treat (NNTs) for treatment response and relapse prevention were 3.43 (95% CI, 2.79–4.45) and 10.30 (95% CI, 5.89–40.62), respectively. High baseline ADHD symptoms (p = 0.02) was associated with greater reduction in ADHD symptoms, whereas male gender (p = 0.02), comorbid oppositional defiant disorder (ODD) status (p = 0.01) and ADHD hyperactive/impulsive subtype (p = 0.01) were associated with smaller reductions. The commonest adverse events were gastrointestinal [appetite decrease, number-needed-to-harm (NNH) = 8.81; abdominal pain, NNH = 22.48; vomiting, NNH = 29.96; dyspepsia, NNH = 49.38] and sleep related (somnolence, NNH = 19.41). Young age (p = 0.03) and high baseline hyperactive/impulsive symptoms (p < 0.01) were associated with more adverse events, whereas ADHD inattentive subtype (p = 0.04) was associated with less adverse events. Quality of life using Child Health Questionnaire (CHQ) improved (p < 0.01) with atomoxetine treatment. Both ADHD and ODD symptoms (p < 0.01) were reduced in comorbid ADHD+ODD, and ODD status was not associated with more adverse events. Efficacy and side effects were not altered by comorbid general anxiety disorder or major depression.Atomoxetine is efficacious in reducing ADHD symptoms. It may have a role in treating comorbid ODD or depression, and probably in comorbid anxiety.
Statin therapy may lower plasma coenzyme Q10 (CoQ10) concentrations, but the evidence as to the significance of this effect is unclear. We assessed the impact of statin therapy on plasma CoQ10 concentrations through the meta-analysis of available RCTs. The literature search included selected databases up to April 30, 2015. The meta-analysis was performed using either a fixed-effects or random-effect model according to statistic. Effect sizes were expressed as weighted mean difference (WMD) and 95% confidence interval (CI). The data from 8 placebo-controlled treatment arms suggested a significant reduction in plasma CoQ10 concentrations following treatment with statins (WMD: −0.44 μmol/L, 95%CI: −0.52, −0.37, < 0.001). The pooled effect size was robust and remained significant in the leave-one-out sensitivity analysis. Subgroup analysis suggested that the impact of statins on plasma CoQ10 concentrations is significant for all 4 types of statins studied i.e. atorvastatin (WMD: −0.41 μmol/L, 95%CI: −0.53, −0.29, < 0.001), simvastatin (WMD: −0.47 μmol/L, 95% CI: −0.61, −0.33, < 0.001), rosuvastatin (WMD: −0.49 μmol/L, 95%CI: −0.67, −0.31, < 0.001) and pravastatin (WMD: −0.43 μmol/L, 95%CI: −0.69, −0.16, = 0.001). Likewise, there was no differential effect of lipophilic (WMD: −0.43 μmol/L, 95%CI: −0.53, −0.34, < 0.001) and hydrophilic statins (WMD: −0.47 μmol/L, 95%CI: −0.62, −0.32, < 0.001). With respect to treatment duration, a significant effect was observed in both subsets of trials lasting <12 weeks (WMD: −0.51 μmol/L, 95%CI: −0.64, −0.39, < 0.001) and ≥12 weeks (WMD: −0.40 μmol/L, 95%CI: −0.50, −0.30, < 0.001). The meta-analysis showed a significant reduction in plasma CoQ10 concentrations following treatment with statins. Further well-designed trials are required to confirm our findings and elucidate their clinical relevance.
Henna ( ) is applied to stain keratin, present in hair, skin and fingernails, a red-orange or rust colour. Producers of temporary tattoos mix the aromatic amine compound, -phenylenediamine (PPD) into natural henna to create ‘black henna’ that rapidly stains the skin black. However, PPD may cause severe delayed hypersensitivity reactions following skin contact. This study proposes a rapid direct-analysis method to detect and identify PPD using an atmospheric solids analysis probe (ASAP) coupled to a Q-ToF mass spectrometer (MS). Since laborious, multistep methods of analysis to determine PPD are undesirable, due to the instability of the compound in solution, a screening method involving no sample preparation steps was developed. Experiments were carried out to optimise the corona current, sample cone voltage, source temperature, and desolvation gas temperature to determine ideal ASAP-Q-ToF-MS analysing conditions. Eleven of the 109 henna samples, originating from various countries, tested positive for PPD when henna products were screened using ASAP-MS, without any form of sample preparation other than grinding. Ultra-performance liquid chromatography electrospray ionisation-mass spectrometry (UPLC-Q-ToF-MS) was subsequently used to confirm the results from ASAP and to determine the concentrations of PPD in henna products. The allergen was detected in the same eleven samples, with concentrations ranging from 0.05–4.21% (w/w). It can be concluded that the sensitivity of the ASAP-MS technique is sufficient (limit of detection = 0.025% w/w) to allow screening of henna samples for the presence of PPD. This relatively new technique can be applied to commercial products without extraction, sample treatment or chromatographic separation.
Developing a drug carrier system which could perform targeted and controlled release over a period of time is utmost concern in the pharmaceutical industry. This is more relevant when designing drug carriers for poorly water soluble drug molecules such as curcumin and 6-gingerol. Development of a drug carrier system which could overcome these limitations and perform controlled and targeted drug delivery is beneficial. This study describes a promising approach for the design of novel pH sensitive sodium alginate, hydroxyapatite bilayer coated iron oxide nanoparticle composite (IONP/HAp-NaAlg) via the co-precipitation approach. This system consists of a magnetic core for targeting and a NaAlg/HAp coating on the surface to accommodate the drug molecules. The nanocomposite was characterized using FT-IR spectroscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy and thermogravimetric analysis. The loading efficiency and loading capacity of curcumin and 6-gingerol were examined. drug releasing behavior of curcumin and 6-gingerol was studied at pH 7.4 and pH 5.3 over a period of seven days at 37 °C. The mechanism of drug release from the nanocomposite of each situation was studied using kinetic models and the results implied that, the release is typically via diffusion and a higher release was observed at pH 5.3. This bilayer coated system can be recognized as a potential drug delivery system for the purpose of curcumin and 6-gingerol release in targeted and controlled manner to treat diseases such as cancer.
Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting, objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making and a set of techniques, known under the collective heading multiple criteria decision analysis (MCDA), are useful for this purpose. MCDA methods are widely used in other sectors, and recently there has been an increase in health care applications. In 2014, ISPOR established an MCDA Emerging Good Practices Task Force. It was charged with establishing a common definition for MCDA in health care decision making and developing good practice guidelines for conducting MCDA to aid health care decision making. This initial ISPOR MCDA task force report provides an introduction to MCDA - it defines MCDA; provides examples of its use in different kinds of decision making in health care (including benefit risk analysis, health technology assessment, resource allocation, portfolio decision analysis, shared patient clinician decision making and prioritizing patients’ access to services); provides an overview of the principal methods of MCDA; and describes the key steps involved. Upon reviewing this report, readers should have a solid overview of MCDA methods and their potential for supporting health care decision making.
Because of the increase in the number of immunocompromised patients, the incidence of invasive fungal infections is growing, but the treatment efficiency remains unacceptably low. The most potent clinical systemic antifungals (azoles) are the derivatives of two scaffolds: ketoconazole and fluconazole. Being the safest antifungal drugs, they still have shortcomings, mainly because of pharmacokinetics and resistance. Here, we report the successful use of the target fungal enzyme, sterol 14 alpha-demethylase (CYP51), for structure-based design of novel antifungal drug candidates by minor modifications of VNI [(R)-N-(1-(2,4-dichlorophenyl)-2-(1H-imidazol-1-yl)ethyl)-4-(5-phenyl-1,3,4-oxadiazol-2-yl)benzamide)], an inhibitor of protozoan CYP51 that cures Chagas disease. The synthesis of fungi-oriented VNI derivatives, analysis of their potencies to inhibit CYP51s from two major fungal pathogens (Aspergillus fumigatus and Candida albicans), microsomal stability, effects in fungal cells, and structural characterization of A. fumigatus CYP51 in complexes with the most potent compound are described, offering a new antifungal drug scaffold and outlining directions for its further optimization.
The available studies have reported the benefits of statins on all-cause and cardiovascular mortality in chronic kidney disease (CKD) patients. However studies in end-stage renal disease patients on dialysis yielded conflicting results. Therefore, we performed a meta-analysis and provide the most reliable trial data to date on the impact of statin therapy on cardiovascular events and death from all causes in CKD patients. Data from PubMed, Web of Science, Cochrane Library, and Scopus for the years 1966 to October 2012 were searched. The final meta-analysis included 11 randomized controlled trials involving 21,295 participants with CKD. Among them 6857 were on dialysis. The use of statins in subjects with non-dialysis-dependent CKD resulted in a marked reduction in death from all causes (relative risk [RR]: 0.66; 95% confidence interval [CI]: 0.55–0.79; 0.05), but had the effect of reducing death from cardiac causes (RR: 0.79; 95%CI: 0.64–0.98; < 0.05) and cardiovascular events (RR: 0.81; 95%CI: 0.7–0.94; < 0.05). In conclusion, the use of statins should be indicated in cardiovascular disease prevention especially in patients with non-dialysis-dependent CKD. According to the very limited data the obtained results suggest caution in expecting a reduction in cardiovascular events in patients on dialysis.