A predominant symptom is defined as the symptom with the highest

A predominant symptom is defined as the symptom with the highest ranking and all other symptoms are>=2 / 10 lower ranked. Complexity is defined as>=3 symptoms with>=6/10, with the exception of fatigue and anorexia (threshold>=9/10). To explore patients’ subjective adaptation to illness and burden of treatment two linear analogue self-assessment (LASA) indicators are included, assessing perceived SB590885 manufacturer adjustment to chronic illness (PACIS); [34] (‘no effort at all’ – ‘a great deal of effort) and overall treatment burden (‘not at all’ – ’severely’). The indicator for PACIS was confirmed to be responsive to cytotoxic side-effects, mental distress, and psychosocial dysfunction in patients with

Inhibitors,research,lifescience,medical early breast cancer [36]. It is suitable to describe patients’ adaptation over time. The instruments are validated [37]. The indicator for overall treatment burden has been validated regarding side-effects of antiemetic and cytotoxic

therapy [38]. As indicator for decision-making preferences, the difference in number of mismatched decision-making preferences Inhibitors,research,lifescience,medical between week 3 and 6 will be compared between the two arms. Patients’ preferences for involvement in decision making will be assessed by a Inhibitors,research,lifescience,medical measure adapted from previous studies [39]. The patient chooses from among five categories ranging from ‘the doctor should make the decision using all that he/she knows about the treatment’ to ‘I should make the decision using all that I know and learn about the treatment’. In addition the physician Inhibitors,research,lifescience,medical is asked to choose from among the same five categories how he/she estimates the patients’ preferences. A mismatch is defined as follows: the patient ranks #1 or #2 and the physician #4 or #5 or vice versa. For neutral patients or physicians no mismatch is possible per definition. Sample size calculation Sample sizes are calculated for an inequality test for two means of change in QoL in a cluster randomized design using the software package

NCSS 2004 – PASS 2002, according to the formulation of Donner and Klar, assuming a two-sided Inhibitors,research,lifescience,medical significance level of 0.05, and a statistical power of 0.8 [40]. Further assumptions on design parameters are an overall variance (s2) of 400, an intracluster correlation coefficient (ICC, estimated by the ratio of between-cluster variation to overall variance) of 0.05 , an effect size (between-arm difference in G-QoL to be detected) of 10, and the cluster size (the number of evaluable patients per physician) [40]. For the cluster size several options are considered, but it is expected to stop Cell Host & Microbe the trial at a cluster size of 8 with 12 physicians per arm, yielding a total sample size of 192 evaluable patients. Since the initial estimate of the ICC might not be appropriate, an interim analysis to adjust the sample size as suggested in Lake et al. is foreseen [41]. Once data for the first 100 patients are available, estimates of within-cluster variation and between-cluster variation are obtained. If the resulting ICC has to be at least 1.

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