He used to tailor his prescriptions and frequency of controls to

He used to tailor his prescriptions and frequency of controls to each patient and phase of the disease, thus anticipating the tailored therapies and the patient empowerment presently considered as fundamental in chronic diseases. Furthermore, he suggested that physicians should

work outside the hospital in small coordinated teams, in which volunteers, dietitians and laboratory technicians would play a crucial role. Patient-centered care and the importance of nonmedical team members are clear from the first lines of his book. As far as we know, he was the first physician to stress the role of volunteers in CKD, DMXAA solubility dmso anticipating by decades nonprofit organizations such as the National Kidney Foundation.”
“Background: Missing outcome data are very

common in smoking cessation trials. It is often assumed that all such missing data are from participants who have been unsuccessful in giving up smoking (“”missing=smoking”"). Here we use data from a recent Internet based smoking cessation trial in order to investigate which of a set of a priori chosen baseline variables are predictive of missingness, and the evidence for and against the “”missing=smoking”" assumption.

Methods: We use a selection model, which models the probability that the outcome is observed given the outcome and other variables. The selection model includes Wnt signaling a parameter for which zero indicates that the data are Missing at Random (MAR) and large values indicate “”missing=smoking”". We examine the evidence for the predictive power of baseline variables in the context of a sensitivity analysis. We use data on the selleck chemicals number and type of attempts made to obtain outcome data in

order to estimate the association between smoking status and the missing data indicator.

Results: We apply our methods to the iQuit smoking cessation trial data. From the sensitivity analysis, we obtain strong evidence that older participants are more likely to provide outcome data. The model for the number and type of attempts to obtain outcome data confirms that age is a good predictor of missing data. There is weak evidence from this model that participants who have successfully given up smoking are more likely to provide outcome data but this evidence does not support the “”missing=smoking”" assumption. The probability that participants with missing outcome data are not smoking at the end of the trial is estimated to be between 0.14 and 0.19.

Conclusions: Those conducting smoking cessation trials, and wishing to perform an analysis that assumes the data are MAR, should collect and incorporate baseline variables into their models that are thought to be good predictors of missing data in order to make this assumption more plausible.

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