Abstract
The estimator of effect size, the sample mean difference divided by the sample standard error of the difference is studied in the context of mixed models and is related to the analysis of on-farm trials. A single treatment is compared against possibly different controls using a completely randomized design on each farm. A lower (1-α)100% confidence limit on mean difference of the treatment and the average control is obtained. The best linear unbiased predictors (BLUPs) of the mean difference of the treatment and the individual controls as well as the lower (1-α)100% prediction limits are provided. The effect of omitting or not omitting the farm-by-treatment interaction variance component in the weighting process is assessed using two numerical examples.
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Recommended Citation
Njuho, Peter M. and Milliken, George A.
(1995).
"MIXED MODELS APPROACH TO ON-FARM TRIALS: AN ALTERNATIVE TO META-ANALYSIS FOR COMPARING ONE TREATMENT TO POSSIBLY
DIFFERENT CONTROLS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1343
MIXED MODELS APPROACH TO ON-FARM TRIALS: AN ALTERNATIVE TO META-ANALYSIS FOR COMPARING ONE TREATMENT TO POSSIBLY DIFFERENT CONTROLS
The estimator of effect size, the sample mean difference divided by the sample standard error of the difference is studied in the context of mixed models and is related to the analysis of on-farm trials. A single treatment is compared against possibly different controls using a completely randomized design on each farm. A lower (1-α)100% confidence limit on mean difference of the treatment and the average control is obtained. The best linear unbiased predictors (BLUPs) of the mean difference of the treatment and the individual controls as well as the lower (1-α)100% prediction limits are provided. The effect of omitting or not omitting the farm-by-treatment interaction variance component in the weighting process is assessed using two numerical examples.
