Background There has been a growing interest in the development and application of alternative decision-making frameworks within health care including multicriteria decision analysis (MCDA). oncology and presents Rabbit Polyclonal to GABBR2. an illustrative example of how MCDA can be applied to oncology. Findings Decisions in oncology involve trade-offs between possible benefits and harms. MCDA can help analyse trade-off preferences. A wide range of MCDA methods exist. Each method has its strengths and weaknesses. Choosing the appropriate method varies depending on the source and nature of information used to inform decision making. The literature review identified eight studies. The analytical hierarchy process (AHP) was the most often used method in the identified studies. Conclusion Overall MCDA appears to be a promising tool that can be used to assist clinical decision making in oncology. Nonetheless field testing is desirable before MCDA becomes an established decision-making tool in this field. = 1 … criteria; = scores for alternatives for different criteria; is at least as good as is therefore strictly preferred to (commonly noted is at least as good as is therefore strictly preferred to (commonly noted is at least DPC-423 as good as is therefore indifferent to (commonly noted is not at least as good as is therefore incomparable to (commonly noted is said to outrank another if the following conditions are both true:101 Concordance condition: outperforms on enough criteria of sufficient importance corresponding to the sum of the criteria weights (voting powers) kj. Veto condition: is not outperformed by in the sense of recording a significantly inferior performance on any one criterion. In other words a veto represents a maximum difference in terms of performance of DPC-423 alternatives against a criterion that cannot be compensated.39 The second step in the application of ELECTRE I deals with exploiting the outranking relations to find the kernel meaning the set of non-outranked alternatives. This can be achieved based on the algorithm presented in Fig. 2. Figure 2 Algorithm for exploiting outranking relations to find the kernel. A detailed application of ELECTRE I is presented in the next paragraphs. To apply ELECTRE I concordance index (CI) voting powers (kj) and veto values (vj) have to be determined by the decision-makers. For the purpose of this hypothetical scenario let us consider that the MDT was able to set the values respectively for CI kj and vj. These values are included in the modified performance matrix as shown in Table 2. Table 2 Performance matrix* for DPC-423 ELECTRE I Step 1 1. Constructing outranking relationships between competing alternatives Constructing outranking DPC-423 relationships between competing alternatives implies the pairwise comparison of alternatives. These comparisons are made below. Does a1 outrank a2? C(a1 a2) = 0.35 + DPC-423 0.15 = 0.5 < CI and no veto condition. Thus a1 does not outrank a2 and is noted [~(a1 S a2]. Does a2 outrank a1? C(a2 a1) = 0.30 + 0.20 = 0.5 ≤ C and no veto condition applies. Thus a1 does not outrank a2 and is noted [~(a2 S a1]. Does a1 outrank a3? C(a1 a3) = 0.15 < CI and no veto condition. Thus a1 does not outrank a3 and is noted [~ (a1 S a3]. Does a3 outrank a1? C(a3 a1) = 0.35 + 0.30 + 0.20 = 0.85 > CI and no veto condition applies. Thus a3 outranks a1 and is noted a3 S a1. Does a2 outrank a3? C(a2 a3) = 0.30 + 0.15 = 0.45 < CI and the veto condition applies to criterion C1. Thus a2 does not outrank a3 and is noted [~(a2 S a3]. Does a3 outrank a2? C(a3 a2) = 0.35 + 0.20 = 0.55 = CI and no veto condition applies. Thus a3 outranks a2 and is noted a3 S a2. Step 2 2. Finding the kernel the set of best alternatives The application of the algorithm presented in Fig. 2 results in the Fig. 3 shown below. Figure 3 Kernel obtained from the comparison of three hypothetical cancer treatments. DPC-423 Based on the ELECTRE I analysis the alternative a3 should be selected by the MDT. Additionally the committee can conduct sensitivity analyses to ensure that the findings are robust. This analysis would consist.