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The main hypothesis will be tested using logistic regression controlling for baseline values and cluster using robust standard errors.
Differences between treatment groups will be tested using logistic regression analysis controlling for baseline HbA1c value and baseline HAMD-score.
The significance of possible differences will be tested using logistic multileveling modeling [ 56] with the subject and assessment time as levels and condition as independent variable.
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Deforestation and forest regrowth are tested using logistic regression analysis and an information criterion approach to rank the models and predictor variables.
Breeding forest birds were recorded and breeding of target species in the sample sites as functions of landscape descriptors was tested using logistic regression.
Association with diabetes (METSIM) was tested using logistic regression analysis.
Pair-wise interactions among all independent SNPs were tested using logistic regression.
Appropriateness of pooling the observed sensitivities and specificities over different sites was tested using logistic regression models.
In the Malawian and Gambian case-control studies, associations were tested using logistic regression analysis and conditional logistic regression with stratification by ethnicity, respectively.
Interactions between different polymorphisms were tested using logistic regression in R testing using the number of minor alleles as predictor variable thus implementing an Armitage test of trend.
Interactions between different polymorphisms were tested using logistic regression in R using the number of minor alleles as predictor variable, therefore implementing an Armitage test of trend.
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