Bayesian theory Siva PPT

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Bayesian Theory :

Bayesian Theory By Dr. Siva R Challa

Origin of Bayesian Theory:

Origin of Bayesian Theory

Bayesian Theory:

Bayesian Theory Bayesian theory was originally developed to improve forecast accuracy by combining subjective prediction with improvement from newly collected data. In the diagnosis of disease, the physician may make a preliminary diagnosis based on symptoms and physical examination. Later, the results of laboratory tests are received. The clinician then makes a new diagnostic forecast based on both sets of information Bayesian theory provides a method to weigh the prior information ( eg , physical diagnosis) and new information ( eg , results from laboratory tests) to estimate a new probability for predicting the disease

Bayesian Theory:

In developing a drug dosage regimen, we assess the patient's medical history and then use average or population pharmacokinetic parameters appropriate for the patient's condition to calculate the initial dose. After the initial dose, plasma or serum drug concentrations are obtained from the patient that provide new information to assess the adequacy of the dosage. The advantage of the Bayesian approach is the improvement in estimating the patient's pharmacokinetic parameters based on Bayesian probability versus an ordinary least-squares-based program Bayesian method is particularly useful when only a few blood samples are available Bayesian Theory

Bayesian Theory:

Because of inter- and intrasubject variability, the pharmacokinetic parameters of an individual patient must be estimated from limited data in the presence of unknown random error (assays, etc), known covariates and variables such as clearance, weight, and disease factor, etc, and possible structural (kinetic model) error. From knowledge of mean population pharmacokinetic parameters and their variability, Bayesian methods often employ a special weighted least-squares (WLS) approach and allow improved estimation of patient pharmacokinetic parameters when there is a lot of variation in data Bayesian Theory

Bayesian probability theory:

Bayesian probability theory Bayesian probability theory when applied to dosing of a drug involves a given pharmacokinetic parameter ( P ) and plasma or serum drug concentration ( C ), Then, the probability of a patient with a given pharmacokinetic parameter P , taking into account the measured concentration, is Prob( P / C ): where Prob( P ) = the probability of the patient's parameter within the assumed population distribution, Prob( C / P ) = the probability of measured concentration within the population, and Prob( C ) = the unconditional probability of the observed concentration.

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