Many researchers in medicine use animals for toxicology tests but it is surprising that most of the drugs, which pass through the legally binding toxicology set of tests on animals fail in the setting of human clinical trials. In statistical perspective, this suggests that animals are not accurate models for humans. Clinically, the research on “animal and non-animal preclinical testing has made clinical trials much safer by eliminating unsafe and toxic candidate compounds” (Robin, 2012). It is genuine to note, animal rights activists cite this statistics for the researchers to explore other methodologies regarding drug development. Although there seems to be no alternative for preclinical testing, the question is, how can the researchers develop the system that does not result in a high rate of failure? Such factors as the high costs of implementation of candidate drugs into the system and the adverse effects it can cause in humans, have lead to the continual usage of animals in preclinical testing of drugs. In this regard, animal testing could save the consumers from dreadful harm.
The extract majorly relates on how we react to uncertainty, when it comes to the errors humans make. Although research is based on logic and accuracy, different studies have their own separate outcome depending on how the study was done. To eventually quantify and analyse these work, researchers should take into consideration that the need to capture a wider picture of what is being addressed (Donnelly, 2009). For example, press releases are mostly considered to be of high quality if they have a positive influence on the audience. The basic study facts such as mentioning of harms in the study process, quantification of absolute risks, and the description of limitation of study are the fundamental concepts that should be addressed by the statistical figures (Donnelly, 2009).
To those of us in the science blogging biz and the respecptive findings in most instances are discouraging, however, are not surprising. As an example, although almost all statistical numbers reported the outcome and exposure accurately, valuable information was considered to be missing in most of them. For example in most of the press releases, only 23% quantified the core result using absolute risks. However, only 41% of the mentioned harmful cases, which are associated with interventions, are depicted as beneficial, whereas only 29% referred to any study limitations (Donnelly, 2009). Additionally, importance is a matter of perspective that should be adhered to in all the statistical analysis. Like for instance, the statistical shenanigans, which were recently uncovered at CRU East Anglia, are being used by the political power-mongers to herd the masses into their totalitarian Trojan horse (Walter, 2009). In today's world, use of statistics is a kind of critical thinking skill that is extremely essential in all areas.
Many people quote or use statistics without performing their in depth research on the, population/sample size, questions raised, and study design. People cite statistical results and consider them to be substantial proof of something. Factors such as source of funding, population under study, identification of randomized trials, study time frames, response survey rates, and accurate description of the study outcome and exposure should always be taken care of(Donnelly, 2009). The statistical main result should quantify on the absolute risks and the accuracy of the figures used. Although absolute risks are easily quantified, the assertion of harms in the process of study should be addressed as a separate measure.
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No matter how many drugs go on to fail in the human testing stages, the 94% potency figure must exhibit an adequate measure of safety in animal models before advancing to human systems. In the other context, it is hard for many people to realize the fact, whereby the negative correlation can be the positive correlation on the other side. That is the drugs that eventually test positive are considered to have 94% potency and are considered to be reliable. Hence statistic should never be taken out of context without knowing the substantial claims. The use of scientific, jargon, or technical language should be sparingly used in the explanation of statistical information for easy understanding by the audience. Considering the rejection rate of drugs submitted to animal testing which had passed PCNA testing, one would get the feeling of the need for effectiveness of animal testing (Robin, 2012). On the other hand, rejection of drugs in animal testing shows that human beings need protection: some rejections during animal testing are based on the fact that the drug is non-therapeutic.