A Bayesian network (BN) is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. Formally, BNs are directed acyclic graphs whose nodes represent variables, and whose arcs encode the conditional dependencies among the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable, or a hypothesis. They are not restricted to represent random variables, which form the “Bayesian” aspect of a BN. Efficient algorithms exist that perform inference and learning in BNs. BNs that model sequences of variables are called dynamic BNs. In this context, [A. Harel, R. Kenett, and F. Ruggeri, Modeling web usability diagnostics on the basis of usage statistics, in Statistical Methods in eCommerce Research, W. Jank and G. Shmueli, eds., Wiley, 2008] provide a comparison between Markov Chains and BNs in the analysis of web usability from e-commerce data. A comparison of regression models, structural equation models, and BNs is presented in Anderson et al. [R.D. Anderson, R.D. Mackoy, V.B. Thompson, and G. Harrell, A bayesian network estimation of the service–profit Chain for transport service satisfaction, Decision Sciences 35(4), (2004), pp. 665–689]. In this article we apply BNs to the analysis of customer satisfaction surveys and demonstrate the potential of the approach. In particular, BNs offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers.

Available data about oxidative status in patients with end-stage renal disease (ESRD) or on dialysis are contradictory. The present cross-sectional study aimed to investigate the role of renal insufficiency and dialysis on lipid peroxidation. To separate the effects of uraemia from dialysis-induced stress, we enrolled 26 patients with renal insufficiency on conservative treatment (ESRD), 23 on peritoneal dialysis (PD), 30 on haemodialysis (HD) and 30 controls. Plasma malondialdehyde (MDA) levels, both total (tMDA) and free (fMDA), were measured as indexes of oxidative stress by gas chromatography-mass spectrometry. Bound MDA (bMDA) levels were calculated as the difference between tMDA and fMDA. Total and bMDA concentrations were significantly higher in patients than in controls (ESRD > HD > PD). In PD and HD patients, fMDA levels were similar and significantly higher than in ESRD. Multivariate analysis, with tMDA, fMDA and bMDA as dependent variables, showed similar and significant tMDA and bMDA relations with residual renal function (t = -2.160, P = 0.035) and albumin (t = -2.049, P = 0.045). Erythropoietin dose affected only fMDA values (t = -2.178, P = 0.034). Free and bMDA concentrations identified different MDA patterns. Bound MDA, not excreted by kidneys, accounts alone for high tMDA concentrations in ESRD patients, while both fMDA and bMDA contribute to tMDA values in dialysis patients. These findings show that increased tMDA could be indicative not only of recent lipid peroxidation, and they also highlight the importance of evaluating free, bound and total MDA in patients with reduced renal function in order to assess their oxidative status.