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Surprisingly, in each case one choice is superior to the other for exploring models with main effects and a small number of 2-factor interactions.
We are interested in the full set of historical cohort comparison; hence, the Wald statistic was used to test the coefficient for each historical cohort against the others in models with main effects (see Table 3).
To explore whether clinical symptom severity (BISS total) moderated the effect of social disadvantage on quality of life and functioning, we fit linear regression models with main effects for disadvantage and clinical severity as well as their interaction.
Severity of local and solicited adverse events was compared using cumulative logit models with main effects for dose group and CPG, with each participant scored for maximum severity events as follows: severity = 0 (no AE observed), severity = 1 (mild), severity = 2 (moderate) and severity = 3 (severe).
Models with main effects and interaction terms were fitted and compared with models with main effects only.
Conditional logit models with main effects and interactions terms were used for analysis.
Similar(41)
D-optimal designs are constructed for a model with main effects and first-order interactions.
For this situation we present optimal designs in a discrete setting when the alternatives are specified by an analysis of variance model with main effects only.
A D-optimal minimax design criterion is proposed to construct two-level fractional factorial designs, which can be used to estimate a linear model with main effects and some specified interactions.
A main effects model was compared with a model with main and two-way interaction effects.
Thus, we reported this simple later model with main effect of interest.
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