CF is an example of the so-called personalized medicine, aiming to fit treatment according to patient's genetic background.
Our goals are to determine the initial focus of treatment, identify the best-fit treatment plan and establish a baseline of initial symptoms in order to effectively monitor progress.
There was also consensus that the "real world use" and performance of medicines needs to be improved through a greater focus on the way a patient needs to fit treatment into their life, rather than the patient simply being given the drugs and left to take them, even if they are experiencing problems such as side-effects.
Diabetes teams therefore face challenges in fitting treatment plans for all families.
Imaging tumour perfusion and hypoxia over the treatment course would be required to find the best-fit treatment.
Differences between the two levels of treatment (immediately post infusion, and > 10 min post infusion) were assessed by fitting "treatment" contrasts (with the pre-treatment mean being considered a control), and assessing the significance of the two parameter estimates.
Routines were also considered beneficial in fitting treatment regimens into family life and could help avoid child resistance developing as children came to expect their treatment as part of 'normal' routine.
To estimate the efficacy of ACV and of IDU, the non-linear mixed effect (NLME) model (logistic regression model, based on maximum likelihood method) fitting treatment as a fixed effect and study as a random effect, was performed.
We fitted treatment and patriline, their interaction, and time of measurement as fixed effects, where treatment distinguished between inbred and outbred founders for experiment 1, and between control, female rescue, male rescue for experiment 2. We specified time of measurement also as a repeated effect.
Some studies, with specific treatments addressed at defined subgroups, showed better results than others, where a "one size fits all" treatment was used [ 5, 6].
