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We show that if among the tested hypotheses the number of true hypotheses is not equal to the number of false hypotheses, then Neyman-Pearson theory of testing hypotheses does not warrant minimal epistemic reliability (the feature of driving to true conclusions more often than to false ones). We also argue that N-P does not protect from the possible negative effects of the pragmatic value-laden unequal setting of error probabilities on N-P’s epistemic reliability. Most importantly, we argue that in the case of a negative impact no methodological adjustment is available to neutralize it, so in such cases the discussed pragmatic-value-ladenness of N-P inevitably compromises the goal of attaining truth.

In this paper, we examine whether N-P can be seen as principally satisfying, in a minimal sense, some general epistemic standards and how pragmatic value-laden uneven setting of error probabilities can influence it. Using the concept of predictive value it is shown that Neyman-Pearson's theory of testing hypothesis offers at least minimal epistemic reliability only under the assumption that among the tested hypotheses the number of true hypotheses is equal to the number of false hypotheses. We also argue that N-P does not protect from the possible negative effects of the pragmatic value-laden unequal setting of error probabilities on N-P’s epistemic reliability. We show how pragmatic factors implemented in the form of the asymmetric setting of probabilities of two types of error may have a positive or negative impact on the epistemic reliability of N-P dependent on the case of the hypothesis tested and the assumption about the ratio of true to false hypotheses. More importantly, we argue that in the case of negative impact no methodological adjustment is available to neutralize it so in such cases the discussed pragmatic-value-ladenness of N-P inevitably compromises the goal of attainment of the truth.

Objectives: The aim of the study was to compare the access of patients with rare diseases (RDs) to biotechnological drugs in several Central and Eastern European countries (CEECs). We focused on the legislative pricing and reimbursement requirements, availability of biotechnological orphan medicinal products (BOMPs) for RDs, and reimbursement expenditures. Methods: A questionnaire-based survey was conducted among experts from 10 CEECs: Bulgaria, Croatia, Estonia, Greece, Hungary, Poland, Romania, Slovakia, Serbia, and Macedonia. The legal requirements for reimbursement and pricing of BOMPs were collected. All BOMPs and medicines without prior orphan designations were extracted from the European list of orphan medicinal products, 2017. The reimbursement status of these medicinal products in 2017 in the public coverage of the included CEECs as well as the share of their costs in relation to the total public pharmaceutical spending for the period from 2014 to 2016 were defined. Results: Our survey revealed that some differences in the legal requirements for pricing and reimbursement of BOMPs amongst the countries included in the study. All European Union countries have developed and implemented pharmacoeconomic guidelines with or without some specific reimbursement requirements for orphan medicinal products. Cost-effectiveness analysis, cost-utility analysis, Markov models, meta-analysis, and discount levels of costs and results were required only in Bulgaria, Poland and Hungary. The number of reimbursed BOMPs and biotechnological medicinal products for RDs without prior orphan designation was the highest in Hungary (17 and 40, respectively). Patient-based reimbursement schemes were available only in Hungary for 11 out of 17 BOMPs. Poland and Greece have the highest pharmaceutical expenditure of reimbursed BOMPs with are similar to 214 million and 180 million EUR, respectively in the observed period from 2014 to 2016. High proportion of the pharmaceutical expenditure o

Any serious attempt to give an account of the cognitive aspect of science – as contrasted with e.g. its social or cultural aspects – cannot ignore the automation revolution. In the conception presented in this paper the results of computer science are taken seriously and integrated with many of the ideas concerning what constitutes scientific inquiry that have been proposed at least since the early Middle Ages. The central idea is that of reliable inquiry. Science makes explicit and elaborates on the methods of acquiring beliefs in our daily life. The central question a scientific method applied to inquiry needs to face is if it is reliable in getting to the truth. On the reliabilist criteria presented here, if a method is logically warranted to get to the right answer – given data and background knowledge available – and to stick to it afterwards, then it is reliable. This is a normative theory, but the standard of reliability is adjusted to the domain of the inquiry at hand. This paper presents the central tenets of the reliabilist conception of science and briefly outlines the main results underlying it. Roughly, the aim of science is interesting truth about the world around us (scientific realism) and reliabilist epistemology affords us precise answers as to how far science can succeed in this task – given the methods, goals and background assumptions available. The philosophical task to deliver an adequate understanding of science is taken to be continuous with scientific research itself (naturalism), a major part of which is concerned with delivering causal explanations (causality) and can only be carried out with limited resources (computability). Many of the ideas integrated into the reliabilist conception of science and precisely articulated therewith have appeared earlier in both philosophy and science (history of ideas). I conclude the exposition of the ideas integrated into the reliabilist conception of science with discussions of sample case studies whe