The comment by Latasa (2014) highlights an important point for the compilation and analysis of data on phytoplankton growth and microzooplankton grazing rates by the dilution technique—the desirability of reporting and utilizing all data. This is a useful and refreshing perspective given statements made elsewhere that dilution results with insignificant regression slopes (i.e., nonsignificant g...

Background : MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a ...

We present a candidate reference method for the determination of serum theophylline by isotope dilution gas chromatography-mass spectrometry, using extractive alkylation in sample preparation. The maximum method bias was < 1.0%. The mean method imprecision (CV) was 0.63% (range 0.37-0.96%), calculated from the results of six measurements independently performed on 3 days. The maximum total erro...

Bias formulae are derived for ecological regression estimators. These formulae are useful for determining the direction and magnitude of bias in estimation. It is shown that when group cohesion is higher in areas with higher concentrations of group members and when polarization is higher in more homogeneous areas, ecological regression estimates of polarization will tend to be biased upward. Bo...

An inductively coupled mass spectrometric method was developed for the direct determination of iodine in urine. The application of isotope dilution analysis with added 129I offers new possibilities for automatic and accurate determinations. The sample preparation consists of dilution with an ammonia solution containing 129I. The validation was made by comparison with the results obtained in ano...

This study proposes a new use of goal programming for empirically estimating a regression quantile hyperplane. The approach can yield regression quantile estimates that are less sensitive to not only non-Gaussian error distribut.ions but also a small sample size t.han conventional regression quantile methods. The performance of regression quantile estimates is compared with least absolute value...