methods considered. In other respects, however, many of the steps in this process are identical to those required for the other techniques.
As in all the methods, the first step in multiattribute accounting is to specify the menu of alternatives from which the projects will be selected. The second step is to define a set of valued objectives or criteria for the program (i.e., costs and benefits of various kinds). The result of the first two steps is to define the rows and columns of a matrix; a simplified example is shown in Table 2.1.
The third step is to fill in the cells in the matrix. Since multiattribute accounting requires no quantitative aggregation of scores across criteria (objectives), the entries in the matrix may be either quantitative or qualitative (e.g., high/medium/low). Table 2.1 contains both quantitative and qualitative information.
The fourth step is to determine if some candidates clearly dominate others, i.e., perform equally well or better on all objectives. In Table 2.1, vaccine C is dominated by vaccine B and, therefore, should be ranked below vaccine B in the final rankings.
The fifth step—the ranking itself—is left to the judgment of the decision makers or panels and is not an inherent part of the methodology. The only constraint imposed by the methodology is that dominance between pairs be preserved in the final rankings. The rest is left to intuitive judgment, which may be viewed either as an advantage or a limitation of the method.
The method of multiattribute scoring goes beyond multiattribute accounting by generating a composite score for each candidate project. This requires three additional steps: (1) entry of a quantitative score (xij) in each cell in the matrix corresponding to the jth criterion (objective) and the ith project (vaccine candidate); (2) specifying a set of weights, wj, which the individual factor scores will be combined; and (3) computing the weighted scores (si),
Projects are ranked according to these scores. As an intermediate step, scores for groups of criteria are often combined into subscores (e.g., a “Disease Impact” subscore composed of the first three criteria in Table 2.1), and then the subscores are combined. Also, the individual scores are often “normalized” to a 0–100 scale before weighting for computational convenience. Sometimes, multiplicative rather than additive aggregation rules are used.
A hypothetical example of the process of multiattribute scoring is shown in Table 2.2. The end result is that vaccine candidate A is ranked highest, followed by vaccines B, D, and C. If desired, a sensitivity analysis can be performed, in which the weights are varied to see whether the rankings change. If only one of the four vaccine candidates in Table 2.2 could be developed, a sensitivity analysis would be desirable because the scores of A and B are so close. How-