Bayesian decision analysis

G. Graham, S. Gupta, and L. Aarons, Determination of an optimal dosage regimen using a Bayesian decision analysis of efficacy and adverse effect data. J Pharmacokinet Pharmacodyn 29 67-88 (2002). 

Hewett, P, Logan, P, Mulhausen, J., Ramachandran, G., and Baneijee, S. (2006). Rating exposure control using Bayesian decision analysis. J Occup Environ Hyg 3, 568—581. 

Regan P, Senn SJ (1997) Project prioritisation in drug development a case study in collaboration. In French S, Smith JQ (eds), Bayesian Decision Analysis. Arnold, London. 

In Sections 2 to 4, we review the technology of synthetic oligonucleotide microarrays and describe some of the popular statistical methods that are used to discover genes with differential expression in simple comparative experiments. A novel Bayesian procedure is introduced in Section 5 to analyze differential expression that addresses some of the limitations of current procedures. We proceed, in Section 6, by discussing the issue of sample size and describe two approaches to sample size determination in screening experiments with microarrays. The first approach is based on the concept of reproducibility, and the second approach uses a Bayesian decision-theoretic criterion to trade off information gain and experimental costs. We conclude, in Section 7, with a discussion of some of the open problems in the design and analysis of microarray experiments that need further research. 

Berry DA. 1995. Decision analysis and Bayesian methods in clinical trials. Cancer Treat. Res. 75 125-154. 

Bayesian designed trials provide probabilities of treatment effects that apply directly to the next patient who is similar to those treated in any completed or ongoing trial. This approach provides probabilities that can be used in formal decision analysis. 

In 1984, Viscusi and O Connor wrote an article for the American Economic Review about the effects of chemical hazard disclosure rules on workers propensity to qxiit. They titled it Adaptive Responses to Chemical Labeling Are Workers Bayesian Decision Makers , referring to Bayes Theorem in probability (see chapter 2). The real question that should be asked, however, is whether workers are Kantian decisionmakers do they accept or avoid risks on the basis of utility, as economists suppose, or do they value above all their autonomy as human beings in the tradition of Kant s categorical moral imperative This is an empirical question we will look for evidence of it in the historical and institutional record (chapter 4), and we will consider its implications for compensating differential theory and labor market analysis in general in chapter 5. 

Literature on the many techniques for making risk assessments is abundant. For example, in ANSI/ASSE Z690.3. Risk Assessment Techniques, reviews are included of 31 techniques. Examples are Primary Hazard Analysis, Fault Tree Analysis, Hazard and Operably Studies, Bow Tie Analysis, Scenario Analysis, Reliability Centered Maintenance, Markov Analysis, Bayesian Statistics and Bayes nets, and Multi-Criteria Decision Analysis. 

Fenton, N. M. Neil (2012). Risk Assessment and Decision Analysis With Bayesian Networks. CRC PressINC. 

Bayesian networks for multivariate reasoning about cause and effect within R D with a flow bottleneck model (Fig. 11.6) to help combine scientific and economic aspects of decision making. This model can, where research process decisions affect potential candidate value, further incorporate simple estimation of how the candidate value varies based on the target product profile. Factors such as ease of dosing in this profile can then be causally linked to the relevant predictors within the research process (e.g., bioavailability), to model the value of the predictive methods that might be used and to perform sensitivity analysis of how R D process choices affect the expected added... 

In all analyses, there is uncertainty about the accuracy of the results that may be dealt with via sensitivity analyses [1, 2]. In these analyses, one essentially asks the question What if These allow one to vary key values over clinically feasible ranges to determine whether the decision remains the same, that is, if the strategy initially found to be cost-effective remains the dominant strategy. By performing sensitivity analyses, one can increase the level of confidence in the conclusions. Sensitivity analyses also allow one to determine threshold values for these key parameters at which the decision would change. For example, in the previous example of a Bayesian evaluation embedded in a decision-analytic model of pancreatic cancer, a sensitivity analysis (Fig. 24.6) was conducted to evaluate the relationship... 

Berger JO. 1985. Statistical decision theory and Bayesian analysis. New York Springer. 

Methods for evaluating the performance and utility of uncertainty analysis in the context of probabilistic pesticide assessments are needed. This should include comparisons between assessment outputs and existing field data (e.g., avian field studies) to evaluate whether decision makers can rely on the assessment methods. Consideration should also be given to existing field data to refine generic assessment models, using Bayesian updating methods. 

The Bayesian analysis of BACLASS (a program of ARTHUR), where the decision function is obtained from the product of the marginal PDs computed by the smootted (symmetrical or skewed) histograms, may apparently be used with skewed distributions, without preliminary transformations of the original variables. 

J.O. Berger, Statistical Decision Theory and Bayesian Analysis, 2nd edn. (Springer-Verlag, New York, 1985)... 

St/pen/7sed Data Mining. Searching large volumes of data for hidden predictive relationships. Supervised analysis requires one or more "dependent" or response variables, to be predicted from a set of "independent" or predictor variables. The techniques used include various classification methods (decision tree, support vector, Bayesian) and various estimation methods (regression, neural nets). 

D 3D AD ADME ADMET ANN ARD BCI BCUT BNN C4.5 CART ClogP CoMFA CV Two dimensional Three dimensional Applicability domain Absorption, distribution metabolism, and excretion Absorption, distribution metabolism, excretion, and toxicity Artificial neural network Automatic relevance determination Bernard chemical information Burden, CAS, University of Texas descriptors Bayesian neural network Decision trees using information entropy Classification and regression tree Calculated partition coefficient between octanol and water Comparative molecular field analysis Cross-validation... 

In contrast to the hypothesis testing style of model selection/discrimination, the posterior predictive check (PPC) assesses the predictive performance of the model. This approach allows the user to reformulate the model selection decision to be based on how well the model performs. This approach has been described in detail by Gelman et al. (27) and is only briefly discussed here. PPC has been assessed for PK analysis in a non-Bayesian framework by Yano et al. (40). Yano and colleagues also provide a detailed assessment of the choice of test statistics. The more commonly used test statistic is a local feature of the data that has some importance for model predictions for example, the maximum or minimum concentration might be important for side effects or therapeutic success (see Duffull et al. (6)) and hence constitutes a feature of the data that the model would do well to describe accurately. The PPC can be defined along the fines that posterior refers to conditioning of the distribution of the parameters on the observed values of the data, predictive refers to the distribution of future unobserved quantities, and check refers to how well the predictions reflect the observations (41). This method is used to answer the question Does the observed data look plausible under the posterior distribution This method is therefore solely a check of internal consistency of the model in question. 

The identification of chemical features that cause compounds to be either selective or promiscuous is essential for predictive safety pharmacology methods to inform medicinal chemistry decisions. Therefore an analysis using Bayesian models along with ECFP descriptors was used. By using the compound annotation from the previous step, a multicategory Bayes model can be trained. The protocol is as follows ... 

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