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As such, this can be thought of as an adjusted intention-to-treat analysis, which better determines the underlying true effect of the study treatment and also estimates an effect for a fully adherent patient group, while avoiding the biases inherent in a per-protocol analysis.
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An important task of this model is to estimate an effect of LOGSE after controlling for cohort effects.
The advantage of using a cox regression model (as compared to the use of a log rank test only) is that the cox model allows for estimating an effect of a particular variable.
Instead we aimed to estimate an effect size for a phase III trial.
We also estimated an effect of PSA disease progression on transition rates towards higher ECOG states.
The RMLV analysis estimated an effect size of 23 and the BLUP analysis an effect size of 18.
The Molassiotis results, given that they come from a randomized controlled trial, merit primary consideration in estimating an effect size.
A pragmatic approach to sample size calculation was taken as there were no available data to estimate an effect size.
We were not able to detect studies sufficiently comparable to ours to find a basis for estimating an effect size.
The study was powered at >80% to estimate an effect size ≥0.15 (Cohen) for the primary fully adjusted model.
The questionnaire (appendix 1), designed specifically for this trial, aimed to estimate an effect size for the e-PIS study.
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