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A raw prevalence estimate was generated by first using inverse probability weighting.
The variability of the incremental cost-effectiveness ratio estimate was generated by bootstrap resampling (5000 iterations) of the cost and utility values simultaneously and examining the bootstrap distribution over the quadrants of the cost-effectiveness plane.
The approximate Bayesian bootstrap imputed data sets were generated using Splus; the separate log odds ratios for each imputed data set calculated from equation 11 were fitted using BUGS; and the overall multiple imputed estimate was generated by code written in Splus.
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The state estimate is generated by a weighted sum of the estimates produced by the bank of observers and the parameter estimate is selected to be the one that corresponds to the weighted signal with the largest value.
This estimate
Further, a separate set of adjusted incidence estimates was generated by adjusting estimates with study-specific FRR for the BED and Ax-AI assays, and combined BED/Ax-AI algorithm.
Individual sirolimus clearance estimates were generated by using a Bayesian estimator (MW/Pharm version 3.6, Mediware, Groningen, The Netherlands) as previously described.
Power estimates are generated by multiplying the active power of the network by the estimated or actual network utilization.
Neural pitch estimates were generated by extracting the frequency and amplitude values of the components shown in the lowest two panels of Figure 2, using a search window 20 Hz wide centered on each component of interest.
The estimates were generated by level of fruit and vegetable consumption ( < 5 v. ≥ 5 servings/d).
a.All estimates are generated by combining analysis of five imputed data sets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com