In all cases, equation (1) provided a good fit of the data, with greater than 88% variance accounted for. Equation (1) also provided a good selleck catalog fit of individual participant��s simulated demand data (median R2 = .88, interquartile range = .70�C.93). Baseline �� parameter values derived from simulated demand curves (a measure of the essential value of cigarettes) were significantly correlated with baseline FTND scores (Spearman r = ?.255, p < .05). The correlation between �� parameter values and the number of cigarettes participants reported smoking each day at intake approached significance (Pearson r = ?.232, p = .07). Figure 1. Simulated demand for cigarettes across the programmed range of normalized prices for participants in the placebo and bupropion groups.

Curves are fit using the demand equation proposed by Hursh and Silberberg (2008), see text for details. A mixed factor ANOVA applied to peak smoking (Q0) revealed no significant main effects of time (baseline vs. follow-up, p = .09) or group (bupropion vs. placebo, p = .76). The Time �� Group interaction was not significant [F(1, 57) = 1.74, p = .19]. The same mixed factor ANOVA was applied to �� values derived from individual participants�� demand curves. No significant main effects of time [F(1, 57) = 1.15, p = .29] or group [F(1, 57) = 2.75, p = .10] were detected. Likewise, the Time �� Group interaction did not approach significance [F(1, 57) = 1.29, p = .26]. In sum, bupropion did not decrease peak smoking or the essential value of cigarettes.

Pmax and Omax values were calculated from the derived parameters of the demand curves and were subjected to the same mixed factor ANOVA. Pmax is the cigarette price (nonnormalized for the purpose of this analysis) at which the demand curve has a slope of ?1.0. More importantly, it is the price at which spending on cigarettes is predicted to asymptote; at higher prices, spending should decline. Omax is the predicted maximum amount that would be spent on cigarettes in a single day. Pmax and Omax were calculated using the spreadsheet written by S. R. Hursh (available at http://www.ibrinc.org/centers/bec/BEC_demand.html). No significant main effects of time were detected on either Pmax or Omax (p > .4 in both cases). Likewise, no main effect of group was detected (p > .4 in both cases), and the Time �� Group interaction did not approach significance (p > .

4 in both cases). The correlations between derived and obtained measures of peak spending and price at which peak spending occurred were strong (Omax: Pearson r = .64, p < .0001; Pmax: Pearson r = .79, p < .0001), and a mixed factor ANOVA applied to obtained measures did not reveal a Time �� Group interaction Carfilzomib (p > .2 in both cases). Thus, an effect of bupropion on cigarette purchases was not missed by using measures derived from the demand curves.