Including all available studies in our IPD meta-analyses will maximise our ability to study the factors associated with heterogeneity in model intercepts and coefficients and diagnostic odds ratios.
The Plackett-Burman design is based on the first order model (Eq. 1): Y = beta_{0} + sum {beta_{i} X_{i} } (1 where: Y is the response (biomass or succinic acid concentration), β0 is the model intercept, βi is the linear coefficient, and Xi is the level of the independent variable.
Risk adjustment was undertaken, generating the "expected" series, using a random coefficient logistic model (intercept as ICU site and "slope" as (centred) APACHE III score; unstructured covariance using adaptive quadrature, estimated via the Stata™ module "xtmelogit" [ 24]), as previously described in detail [ 20], and extended to both ventilated and non-ventilated patients.
Model parameters (slope, intercept, coefficients of beta distribution) are calculated for each pair and mentioned in Table 4.
This technique is based on the first-polynomial model according to Eq. (1): Y = β 0 + ∑ β i x i (1 where Y is the response, which is the concentration of AMVL, β 0 is the model intercept, β i is the linear coefficient, and x i is the level of independent variables.
Hence all combinations of the 8 variables (PC1, PC2, NDVI anomalies, temperature anomalies, body condition, body length, current abundance and past June abundance) were tested in both part of the "hurdle" model (count and binomial) from null model (intercept only) to full model (one coefficient estimated for each variables in both parts of the model).
The inverse logit of the linear combination of the subject's measurements in the validation data with the regression coefficients and the intercept coefficient estimated by the previously mentioned multiple logistic regression model was taken as the score value.
Estimates from this two-stage approach can be mapped to estimates from hierarchical models, in the sense that they allow for random effects in the intercept coefficient.
21 The models estimated the means, variances and covariances of the random coefficients for the quantitative covariates FEV1% predicted, BMI and the model intercept.
F o : Fuel curve intercept coefficient (fuel/hour/kW).
