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23 24 25 We used the two stage generalised least squares trend estimation method, which first estimated study specific slope lines and then combined with studies in which the slopes were directly reported to obtain an overall average slope.
For each of the utility estimations and the VAS, three models were estimated: study arm and socio-demographic variables (models 1, 4 and 7); study arm and self-reported health variables (models 2, 5 and 8); study arm, demographics and self-reported health variables (models 3, 6 and 9).
At α=0.05 and a desired power of 0.9, estimated study intervals ranged from 11to6969 years depending on species, trend, software, and study design.
We estimated study power using genetic power calculator [42].
We estimated study sample-specific mean window periods for the BED and Ax-AI assays, and combined BED/Ax-AI algorithm.
The estimated study completion date is february 2011.
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In these analyses, we estimated study-specific trends of endometrial cancer risk per additional cup daily.
The estimated study-specific KL values varied by several orders of magnitude.
Briefly, we focused on women without breast cancer and estimated study-specific linear age trends in the medians of the logit-transformed MD values using quantile regression.
We estimated study-specific absolute mortality rates and variances by histology and smoking habit (never/ever/current/former) based on relative risk estimates derived from studies published in the 20th century, coupled with WHO mortality data for age 70 74 for the relevant country and period.
For such complex study designs, computer simulation is a useful alternative for estimating study power.
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