From this model, we computed the propensity to receive ATIV for each person-season of observation and added that to the data as an additional, derived variable.
Next, we computed the propensity score-adjusted relative risk of death during hospitalization, comparing AKI-RRT patients with non-AKI-RRT patients, using a generalized linear model with a log-link function and a binomial error distribution [ 19, 20].
For the propensity score analysis we generated a multivariable logistic regression model that predicted beta-blocker use among ICU patients based on the covariate profile listed in Table 1 and computed the propensity score (that is, the probability of beta-blocker use) for all ICU patients.
To investigate conditional vulnerability of dropout, we computed the propensity score (from 0 to 1) by using logistic regression; the dependent variable was high school dropout and the independent variables (covariates) were sex, age, maternal education level, health and health behavior measures, psychosocial factors, and school-related factors.
The Saps II score was not used to compute the propensity score because some patients were under the age of 16 and had an invalid Saps II score.
We estimated a logit to compute the propensity score.
34 Prior hospitalisations, ER and office visits due to asthma, adherence to inhaled corticosteroid (ICS) medications and average number of short-acting β agonists during the study period were used to compute the propensity scores.
A measure of criterion (c) or of the propensity of participants to produce a positive recognition response was also computed (c = (Z hits) + Z(false alarms)) ⁎ − 0.5, see Table 3).
We evaluate the propensity of two domains to co-occur in proteins of a given genome by computing their co-occurrence score CS which ranges from 0 to 9.9.
We have computed the normalized binding propensity of nucleotides in E. coli, H. sapiens, S. cerevisiae, thermophiles and archaea, and the results are presented in Figure 2.
