Sentence examples for outcome were created from inspiring English sources

Two study variables, Baseline predictor and Follow-up outcome, were created (see Figure 1).

To assess the distribution of explanatory variables, contingency tables for categorical variables and summary statistics for continuous variables (alone and classified by the outcome) were created.

A base model for each neurological outcome was created with an outcome-specific set of covariates using backward elimination.

The outcome was created and compared to the curricula of the various academic institutions to ensure inclusion of important topics.

The first binary outcome was created from two questions examining spousal communication (a) Have you ever discussed how many children to have with your husband?

One final file for each outcome was created after exclusion of extreme and missing data on PTB and SGA birth (NPTB= 353,006; NSGA = 352,727).

To account for this, a second set of models for each outcome was created in which the potentially co-occurring risk behaviors under study are included as independent variables (along with the original set of covariates).

A binary physical activity outcome was created indicating whether the participant met the current U.S. physical activity recommendation of 500 MET minutes/week of activity via walking (i.e., equivalent to 150 minutes/week of moderate-intensity activity) [ 1].

Analyses were also undertaken on the four individual continuous variables from which the beneficial change outcome was created (DINE subset for dietary fat and fruit and vegetable intake, IPAQ score for physical activity, number of cigarettes smoked daily, and AUDIT-C score for alcohol intake).

A binary outcome was created, where participants who responded 'almost never' to 'very often' (2 5 on scale) to either of these questions were considered to have smoking in the home 3 months after childbirth, and participants who responded 'never' to have a smoke-free home.

To estimate the effect that preexisting conditions or index surgery duration might have on the attributable effect of SSI on total costs, a matched linear regression with log-transformed total costs as the outcome was created with the predictors SSI/no SSI, chronic disease score (CDS), and index surgery duration entered as variables into the model.