Credibility factor

During the PHEA stage, the analyst has to identify likely human errors and possible ways of error detection and recovery. The PHEA prompts the analyst to examine the main performance-influencing factors (PIFs) (see Chapter 3) which can contribute to critical errors. All the task steps at the bottom level of the HTA are analyzed in turn to identify likely error modes, their potential for recovery, their safety or quality consequences, and the main performance-influencing factors (PIFs) which can give rise to these errors. In this case study, credible errors were found for the majority of the task steps and each error had multiple causes. An analysis of two operations from the HTA is presented to illustrate the outputs of the PHEA. Figure 7.12 shows a PHEA of the two following tasks Receive instructions to pump and Reset system. 

To be useful to those concerned with choices in the allocation of health and social care resources, the data for economic evaluations need to be timely, relevant, credible and accurate (Davies, 1998). As a minimum, the costs associated with the interventions should be estimated from activity data, which quantify resources used, and price or unit cost data. Often evidence from well-controlled prospective trials with high internal validity is required to establish whether differences in economic end points are directly attributable to the interventions. However, the economic evaluations of acetylcholinesterase inhibitors estimated costs from retrospective analysis of available datasets Qonsson et al, 1999b), analysis of published literature (e.g. Stewart et al, 1998) and expert opinion (e.g. O Brien et al, 1999 Neumann et al, 1999). This means that it is not clear whether differences in costs were due to the anticholinesterase inhibitors or to other factors such as availability of services in different areas, the living situation of the patient, or disease severity. 

A large number of considerations and factors must be entertained for the conception, development, preparation, assessment, characterization, and certification of RMs, including (a) end use requirements, (b) selection of materials, (c) preparation, (d) physical characterization, (e) chemical characterization, (f) certification, (g) documentation, and (h) distribution. Most of these have an overwhelming impact on the finally developed RM and on its credibility. This section deals with the steps, collectively denoted as collection and preparation, occurring early in the scheme of RM development. It treats general collection and preparation principles, and provides specific examples of preparative procedures. 

Several additional factors are important when considering the worst case credible event. 

Factors such as hazard type and source credibility have been identified as important in the establishment of effective strategies for risk communication (Frewer et al., 1997). One means by which to measure credibility is the Meyer s credibility index (McComas and Trumbo, 2001). This has proven useful for measuring source credibility in the context of environmental health-risk controversies, and it would seem to be relevant for measuring food risks in a food crisis situation. A key element of this credibility index is trust. 

Evaluation of the data surrounding the death by physicians who are unassociated with the clinical trial lends additional credibility to the report and conclusions. Physician biases probably will strongly influence their decision regarding the association of a patient s death with the clinical trial, and this factor must be considered in interpreting their report. This is particularly true for developing survival curves in cancer or other often fatal diseases, when deaths unrelated to the disease or to the treatment are excluded from the analysis. 

Credibility is built upon two factors expertise and freedom from bias. A bias is an opinion or feeling that strongly favors one side over others. Expertise is established by education, experience, job or position, reputation, and achievements. In general, the greater the expertise and the lower the potential for bias, the greater the credibility. 

To design general area gas detection, each area should be evaluated based on its volume for credible gas release hazards. The factors to consider when determining credible hazards are ... 

In using cross-validation it is essential to avoid, or at least minimize, bias to the free R factor itself. In the era of emerging automated procedures for modelling and refinement a frequent mistake is to set aside a fraction of reflections for minimization of the residual in reciprocal space and, at the same time, to use all data for computation of electron density and model rebuilding. Since local adjustment of the model in real space is equivalent to global phase adjustment in reciprocal space, the free reflection set becomes biased towards the current model and loses its validation credibility. 

There are two general types of aerosol source apportionment methods dispersion models and receptor models. Receptor models are divided into microscopic methods and chemical methods. Chemical mass balance, principal component factor analysis, target transformation factor analysis, etc. are all based on the same mathematical model and simply represent different approaches to solution of the fundamental receptor model equation. All require conservation of mass, as well as source composition information for qualitative analysis and a mass balance for a quantitative analysis. Each interpretive approach to the receptor model yields unique information useful in establishing the credibility of a study s final results. Source apportionment sutdies using the receptor model should include interpretation of the chemical data set by both multivariate methods. 

The complexity and volume of. available diffraction data requires that other than manual tediniques be used to match unknown to known spectra. Available computer programs have indeed simplified the problem of identifying an unknown substance (Refs 9,15,16,21 22). The work of Abel and Kemmey (Ref 16) in this area is worthy of note. Data taken from this report is presented as Table 4. The authors use values of 26 (<90°) to identify phase location instead of values of d in A. Major computer programs of this type endeavor to identify the crystal structure of an. unknown and cite a general factor of certainty to support the credibility of the analysis interpretation... 

There is limited evidence of carcinogenicity from studies in humans which indicates a causal interpretation is credible, but alternate explanations, such as chance, bias, or confounding factors, cannot adequately be excluded ... 

Whatever methods are employed to link assessment end points with measures of effect, it is important to apply the methods in a manner consistent with sound ecological and toxicological principles. For example, it is inappropriate to use structure-activity relationships to predict toxicity from chemical structure unless the chemical under consideration has a similar mode of toxic action to the reference chemicals. Similarly extrapolations from upland avian species to waterfowl may be more credible if factors such as differences in food preferences, physiology, and seasonal behavior (e.g., mating and migration habits) are considered. 

The committee s attention to those limitations and uncertainties is important for two reasons. First, full disclosure of limiting factors gives scientists and the public a fuller understanding of the reliability and credibility of biomonitoring results. It provides risk assessors with information needed to characterize risk conclusions fully, as called for by the National Research Council risk-assessment paradigm (NRC 1983 1994). Second, and equally important, the kinds of uncertainty define data gaps for immediate attention and related long-term research needs. 

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