In the schedule for the evaluation of individual quality of life (SEIQoL) the weights for five individualized quality of life domains have been derived by judgment analysis and direct weighting (DW). Both weighted index scores were strongly correlated to the unweighted index score. The relationships between the index score and scores on a visual analogue level for global individual quality of life and global quality of life were similar whether or not the index score was determined with DW weights, with ACA weights, or without using weights. We conclude that, because weights did not improve the correlation between the index score and global quality of life scores, it seems sufficient to use the unweighted index score like a measure for global individual quality of life. value was moderate at both measurements (0.40 and 0.44, respectively). The reliability of the weights was moderate 1200133-34-1 IC50 for the DW (is the number of levels across all domains and is the quantity of domains, resulting in 3(20???5???1)???20?=?22 pairs [19]. This method presents a rule of thumb leading to three times the number of observations as guidelines available (for simulations on accuracy of prediction with numerous numbers of pairs, observe [20]). We offered 25 paired comparisons, where scenarios were defined using two (pairs 1C15) or three (pairs 16C25) domains. Finally, individuals filled out a questionnaire that resolved demographic factors such as age, sex, marital status, education, and religion. Computation of iQoL weights and index scores For the DW, the relative excess weight of a website is equal to the proportion of the pie chart that its sector represents, which can be read from a 100-point scale within the circumference. Relative weights for the ACA are determined as follows. First, individual utilities for those levels of functioning within the domains are derived by regular least-squares regression analysis, from participants’ answers to the pairwise comparisons, presuming a linear main effects additive model (for details observe [18]). Next, the relative weights for the domains are determined by dividing the range of each domain (power of highest level C power of least expensive level) from the sum of ranges of all domains, and multiplying by 100 [16, 19]. The relative weights are indicated as percentages (the five weights add up to 100%) and reflect the degree to which the difference between the best and worst levels of each website drives the decision to choose a specific scenario [16]. The index score for iQoL is definitely a weighted score, determined by multiplying the functioning scores for the domains with their related weights as derived from the DW HAS2 and the ACA method, and summing these. Further, an unweighted index score was determined by simply summing up the functioning scores and dividing by 5. Feasibility and validity The feasibility of the ACA was assessed by measuring the percentage of individuals 1200133-34-1 IC50 that were able to finish the task, by measuring the administration time, and by asking the individuals how they evaluated the ACA with respect to difficulty and acceptability. We asked individuals two quantitative items about the method becoming confronting (very, somewhat, not) or becoming unpleasant versus fun (1?=?very unpleasant, 5?=?much fun). Further, we also coded qualitative statements about the ACA becoming upsetting (feedback such 1200133-34-1 IC50 as unpleasant, mean, suicide questions, I felt just like a prisoner). Like a measure of difficulty we also assessed how often individuals chose the worst option inside a dominating pair, a pair in which one of the scenarios was on all domains better than the additional. The validity of the ACA was first studied by assessing the number of inconsistencies in the rank purchasing of utilities, that is, the number of pairs in which the utilities for two levels of functioning were ranked opposite to the direction of the levels of functioning. We analyzed whether age, health status, and level of education were related to answers to dominating pairs and the number of inconsistencies by Pearsons correlation and analysis of variance. Next, we assessed whether patients were willing to trade off a decrease from the best to the second-best functioning level on their most important domain with the largest improvement on their second important domain. This was done by computing the ratio between the difference in utilities for the largest benefit in the second important website and the difference in utilities for the two highest functioning levels of the most important website. A value.