Causality assessments methods

Miremont G, Haramburu F, Begaud B, Pere JC, Dangoumau. Adverse drug reactions Physicians opinions versus a causality assessment method. Eur J Clin Pharmacol 1994 46 285-9. 

Rochon J, Protiva P, Seeff LB, et al. Reliability of the Roussel Uclaf Causality Assessment Method for assessing causality in drug-induced liver injury. Hepatology. 2008 48(4) 1175-1183. 

Reprinted from J Gin Epidemiol, Vol 46, Danan C, Benichou C. Causality assessment of adverse reactions to drugs-4. A novel method based on the conclusions of international consensus meetings- Application to drug-induced liver injuries pages 1323-1330, Copyright 1993, with permission from Elsevier. 

At the present time, several countries have well-organised and experienced spontaneous reporting systems which contribute the bulk of ADE reports. In particular the United States, the United Kingdom, France and the Scandinavian countries have records going back several decades, and can claim to have in their possession data of reasonable quality. The section on p. 438 considers the various methods of causality assessment that are available and the data that are required for their application. 

Assuming that the quality of the input is maximal then improving the output will be dependent upon the analysis and evaluation of the data. Because the spontaneous report alone will seldom, if ever, contain sufficient information to determine causality satisfactorily, it is usually necessary to seek additional data which may be available from hospital records, laboratory investigations or post-mortem reports. It is highly desirable that comparable methods and formats of reporting should be used as widely as possible, particularly if international comparisons are to be made. A major benefit of formalised systems of causality assessment, which will be considered in greater detail later in this chapter, is the element of standardisation that is brought to the process of interpretation. 

In the time that has elapsed since the previous edition of this book, it has been the authors impression that enthusiasm for formal methods of causality assessment has decreased to the extent that few, if any, pharmaceutical companies still use them. Nevertheless, brief comments on methods that have been developed over the years are needed to complete the picture. 

In recent years, a method of causality assessment based on Bayes theorem has been developed by a number of workers in the United States. " Its application to the evaluation... 

Hutchinson TA, Lane DA. Assessing methods for causality assessment of suspected adverse drug reactions. J Clin Epidemiol 1989 42 5-16. 

In recent years a method of causality assessment based on Bayes theorem has been developed by a number of workers in the US. " Its application to the evaluation of ADEs is shown in Table 14.12 and a practical method (from Hutchinson) is shown in Table 14.13 and Figure 14.2. This is probably the most sophisticated system developed so far, and it has been applied to a number of actual drug problems. An integrated approach to ADE assessment which permits the prediction of incidence extends the method beyond individual case evaluation. 

Table 14.11 Method for causality assessment of adverse drug reactions...

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. 

Agbabiaka, T. B., Savovic,J., Ernst, E. (2008). Methods for causality assessment of adverse drug reactions A systemadc review. Drug Safety An InternationalJournal of Medical Toxicology and Drug Experience, Bl(l), 21-37. 

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. 

In previous editions of this textbook a number of methods of determining causality have been described and an attempt made to evaluate their relative reliability and validity. It has to be understood that such statistically based methods have, over several dacades, been in competition with common observation and inspired guesswork for the assessment of adverse effects in respect of the need for regulatory action. This is not just a matter of idiosyncratic personal preference but is frequently imposed by the clinical and numerical evidence available. 

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. 

It is important to note that the LCA is a tool and cannot provide an all-encompassing assessment. One of the reasons is that industrial processes are interconnected globally, so that complete consideration of all these interdependencies is practically impossible. Also, the results of an LCA are approximations and simplifications of cumulative burdens to the environment and of resources used. Therefore, the LCA process does not directly measure actual environmental impact, predict effects, or represent causal linkages with specific effects. As a result, to meet the needs of the study users, it may be necessary to supplement the LCA with other tools or methods to provide a basis for decision making. These tools include risk assessment, site-specific environmental assessment, etc. As a part of the scoping process, it is useful to identify where and how these other tools will be used to augment the findings of the LCA [5]. 

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