A series of logistic regression indicators, like, for instance, prediction deterioration when a particular predictor is missing, assessment of the significance of the weight coefficients (B) of each predictor, or the extent to which the odds ratio (OR) changes when a predictor is removed, may be used to identify these predictors.
In the multiple regression analysis, indicator variables were employed as follows; one indicator variable coded as 0/1 for variable with two categories (use of β-blockers on admission) and two indicator variables for variables with three categories (changes in medication profiles of RAAS-I and diuretics) were generated.
One measure is a residual from an hourly log wage regression on indicator variables for white; calendar year; month of year; the interaction of an age quartic with educational attainment and sex; the interaction of educational attainment, sex, and presence of children under eighteen; and all interacted with married (spouse present).
In addition to monthly MSA unemployment rate, the base regression includes indicator variables for education (5 dummies for 6 categories), age (9), gender, marital status (2), children in household (2), foreign born (2), union member, public employment, hours on primary job (6), industry (12), occupation (16), region (8), MSA size (5), month, and year dummies.
In addition to the regressors shown, the base regression includes indicator variables for education (5 dummies for 6 categories), age (9), gender, marital status (2), children in household (2), foreign born (2), union member, public employment, hours on primary job (6), industry (12), occupation (16), region (8), MSA size (5), month, and year dummies.
Data were analysed as a two level, mixed, multivariate regression with indicator crossed with practice.
In the multivariate logistic regression, missing indicator variables were used for maternal age, BMI, and height.
p-value calculated using multiple linear GEE regression comparing indicator scores by survey method; p-values <0.05 are in bold.
In order to model g(S0 t)), the transformed baseline survival function, the standard procedure is to insert in the regression model indicator functions for each pseudo-time.
