Our data processing business provides processing services for non-clinical trial data, such as epidemiological data and data in preclinical animal experiments.
Epidemiology is the science of studying the distribution and determinants of disease and health in a specific population. It is an important part of preventive medicine and the basis of preventive medicine. Epidemiological research methods include monitoring, observation, hypothesis testing, analytical research, and experiments. Epidemiological data analysis uses specific methods to organize, describe, infer and summarize data. Epidemiological research issues include disease distribution, etiology and risk factors, diagnosis, prevention, and treatment evaluation.
A new drug from the discovery of candidate compounds to the clinical trials requires a large number of preclinical tests to ensure its safety and effectiveness when used on the human body. Preclinical drug evaluation mainly includes four aspects of research: pharmaceutical research (this part mainly includes research on physical and chemical properties, drug-forming properties, quality standards, etc.), pharmacodynamic studies, safety studies and pharmacokinetic studies.
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Our statisticians provide the most appropriate epidemiological research data analysis method according to your needs, and help you select the most valuable candidate drugs for clinical trial application through targeted preclinical data analysis.
The issues involved in epidemiological research are diverse, so the data analysis methods used should be targeted when dealing with epidemiological research data. Our services can solve your problems in all aspects of epidemiological data analysis. For example, when studying the frequency of onset, it is necessary to select the best evaluation index among the incidence rate, the attack rate, and the renewal rate according to the purpose of the study and the characteristics of the disease.
The effect is the size or effect of exposure or treatment on the outcome and is often expressed by the difference in incidence between the exposed and non-exposed groups or between the treatment group and the control group. The commonly used effect measures in epidemiological studies are: ① relative risk (RR) =I1I0; ② percentage of attributable risk (ARP) =(I1-I0)/I1; ③ relative risk less (CARR) = (I1-I0)/I0; ④ odds ratio (OR) = ad/bc; ⑤ rate difference (RD) = (I1-I0); ⑥ need to treat the number of people (NNT) = 1 / RD. Due to random errors, the results of each effect indicator cannot represent the true value of the effect, and the confidence interval is often used to represent the uncertainty of the effect estimate caused by the random error.
In the preclinical phase, drug evaluation is usually established on animal models. In general, statistical methods used in this phase can be divided into two types. One is statistical description, and the other is statistical inference. Statistical description is the most basic statistical analysis of the data, with the use of statistical indicators, statistics or statistical tables, so that it can reflect the basic characteristics of the data, which is conducive to the accurate and comprehensive understanding of the information contained in the data. Statistical inference is to use the information provided by the sample to infer the data distribution of the population, such as confidence interval, t test, analysis of variance, and so on.
We guarantee the confidentiality and sensitivity of our customers' data. We are committed to providing you with timely and high-quality deliverables. At the same time, we guarantee cost-effective, complete and concise reports.
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References:
1. Aizawa, M., Takayoshi, K., Kokuba, T., Kato, T., Yanase, T., & Yamakawa, M., et al. (2008) ‘Molecular epidemiological analysis of bovine ephemeral fever virus isolated in okinawa prefecture’, Journal of the Japan Veterinary Medical Association, 63(5), 363-366.
2. Patel, S. D., Pierce, L., Ciardiello, A. J., & Vannucci, S. J. (2014) ‘Neonatal encephalopathy: pre-clinical studies in neuroprotection’, Biochem Soc Trans, 42(2), 564-568.
3. Dobrovolskaia, M. A. (2015) ‘Pre-clinical immunotoxicity studies of nanotechnology-formulated drugs: challenges, considerations and strategy’, Journal of Controlled Release, 220(Pt B), 571-583.