Adaptive design is to automatically adjust the processing method, processing order, processing parameters, boundary conditions or constraints according to the data characteristics of the processed data in the process of processing and analyzing the data, so as to adapt to the statistical distribution characteristics and structural features of the processed data. The adaptive design aims to learn from the accumulated data and apply what is learned as quickly as possible. Therefore, adaptability is a design to enhance the test, rather than a remedy for inadequate planning.
Bayesian statistics is a theory based on the Bayesian probability interpretation of the statistical field. The two basic concepts in Bayesian statistics are the prior distribution and the posterior distribution. The prior distribution is the subjective judgment of a parameter probability distribution before you obtain the experimental observations. The prior distribution does not have to be objectively based. It can be partially or completely based on subjective beliefs. According to the prior distribution of sample distribution and unknown parameters, the conditional distribution of unknown parameters is obtained by using the method of conditional probability distribution in probability theory. Since this distribution is obtained after sampling, it is called a posterior distribution. The key to the Bayesian inference method is that any inference must be and only needs to be distributed according to the posterior, and can no longer involve the sample distribution.
Adaptive design and Bayesian statistics are commonly used data processing methods in clinical trials for drug development.
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Generally speaking, during clinical trial implementation, the adaptive modifications may include adjusting sample size and the distribution ratio of main materials among groups, increasing the treatment group, improving the overall design of the trial, changing the statistical method, and changing clinical trial results, variables and so on. For example, in clinical trials, the size of the sample needs to be adjusted when the initial expected efficacy is proven to be too large or too small. Bayesian statistics has penetrated various fields of medical research, including clinical trials, observational studies, diagnostics and screening tests. For specific problems in these areas, our statisticians can provide you with detailed services.
Adaptive design refers to the adjustment of the follow-up test plan based on the partial results obtained from the preliminary test after the start of the test without destroying the integrity and validity of the test, so as to timely discover and correct some unreasonable assumptions at the beginning of the test design. Greater flexibility within the adaptive design framework can contribute to better treatment for patients in the trials, effectiveness improving in drug development, and better use of available resources.
In recent years, with the development of evidence-based medicine, the practical value of Bayesian method in clinical decision making has received attention. The Bayesian-based algorithm introduces a way for individualized application of evidence-based medical evidence and becomes an important extension of evidence-based medicine. The Bayesian method can comprehensively analyze various related factors, infer the diagnosis probability, and realize the individualized evaluation of the clinical significance of the diagnostic experiment, which helps to objectively and accurately weight various clinical information and reduce the bias of the empirical method. Our experts are ready to help you with Bayesian design and statistics.
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References:
1. Berry, D. A., Müller, P., Grieve, A. P., Smith, M., Parke, T., & Blazek, R., et al. (2002) ‘Adaptive bayesian designs for dose-ranging drug trials’, Case Studies in Bayesian Statistics, 162, 99-181.
2. Paul Gallo, Christy ChuangStein, Vladimir Dragalin, Brenda Gaydos, Michael Krams, & José Pinheiro. (2006) ‘Adaptive designs in clinical drug development - an executive summary of the phrma working group’, Journal of Biopharmaceutical Statistics, 16(3), 275-283.
3. Carrillo-Calvet, H. (2007) ‘Diagnosis of breast cancer using bayesian networks: a case study’, Computers in Biology & Medicine, 37(11), 1553-1564.