Causality assessments bayesian assessment

Lane DA, Hutchinson TA, Jones JK, et al. A Bayesian Approach to Causality Assessment. University of Minnesota School of Statistics Tech Reps No 472 (no date available). 

Lane DA, Kramer MS, Hutchinson TA, et al. The causality assessment of adverse drug reactions using a Bayesian approach. Pharm Med 1987 2 265-83. 

Bayesian statistics are applicable to analyzing uncertainty in all phases of a risk assessment. Bayesian or probabilistic induction provides a quantitative way to estimate the plausibility of a proposed causality model (Howson and Urbach 1989), including the causal (conceptual) models central to chemical risk assessment (Newman and Evans 2002). Bayesian inductive methods quantify the plausibility of a conceptual model based on existing data and can accommodate a process of data augmentation (or pooling) until sufficient belief (or disbelief) has been accumulated about the proposed cause-effect model. Once a plausible conceptual model is defined, Bayesian methods can quantify uncertainties in parameter estimation or model predictions (predictive inferences). Relevant methods can be found in numerous textbooks, e.g., Carlin and Louis (2000) and Gelman et al. (1997). 

Central to any risk assessment is a model of causality. At the onset, a conceptual model is needed that identifies a plausible cause-effect relationship linking stressor exposure to some effect. Most ecological risk assessments rely heavily on weight-of-evidence or expert opinion methods to foster plausibility of the causal model. Unfortunately, such methods are prone to considerable error (Lane et al. 1987 Hutchinson and Lane 1989 Lane 1989), and attempts to quantify that error are rare. Although seldom used in risk assessment, Bayesian methods can explicitly quantify the plausibility of a causal model. 

Benichou C, Danan G. Causality assessment in the European pharmaceutical industry presentation of preliminary results of a new method. Drug Inf J 1992 26 589-92. Hutchinson T A. Computerised bayesian ADR assessment. Drug Inf J 1991 25 235 1. Lane DA, Hutchinson TA, Jones JK, Kramer MS, Naranjo CA. A Bayesian Approach to Causality Assessment. Universcity of Minnesota School of Statistics Tech Reps No 472 (no date available). 

Lane DA, Kramer MS, Hutchinson TA, Jones JK, Naranjo C. The causality assessment of adverse drug reactions using a bayesian approach. Pharm Med 1987 2 265-83. Naranjo CA, Busto U, Sellers EM, et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther 1981 30 239 5. 

It is an important duty of safety monitors working for a sponsor to make some sort of assessment of causal relationship between ADEs and treatment. Often a preliminary assessment has been made by the reporting physician. In the past such assessment was made by skilled judgement on the part of the physician and/or monitor. There has been some interesting work on applying Bayesian quantitative methods in order to provide numerical assessments of the probability that an ADE is really an ADR (Cowell et ah, 1993 Hutchinson et al., 1991a,b). Such single-case causality assessment is, in fact. 

Newman MC, Evans DA. 2002. Causal inference in risk assessments cognitive idols or Bayesian theory In Newman MC, Roberts M, Hale R, editors. Coastal and estuarine risk assessment. Boca Raton (FL) CRC Press, p 73-96. 

In this paper, Section 2 introduces the principles of Bayesian method and the inference of BBN. Section 3 describes the structure of the Bayesian causal modeling in the R MM system and the approaches of reliability assessment and maintenance decision-making. In Section 4, both the main functions and the implementation of the generic R MM system are provided to show the wide application scopes. Finally, Section 6 gives some conclusions. 

Advanced industrial R MM requires intelligent integrated reasoning towards accurate reliability assessment and optimal maintenance decisionmaking. By means of Bayesian inference and the causal modeling of Bayesian belief network, this paper presents the development and the... 

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