Examples of 'outcome variables' in a sentence
Meaning of "outcome variables"
Outcome variables are the key measures or indicators used in a research study or experiment to assess the effects or results of specific interventions, treatments, or factors. They can be quantitative (such as numerical ratings or measurements) or qualitative (such as subjective assessments or descriptions) and are used to evaluate the impact or effectiveness of a particular intervention or study
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- plural of outcome variable
How to use "outcome variables" in a sentence
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outcome variables
Outcome variables and method of data extraction.
Strong effects on both outcome variables.
Outcome variables are described in the relevant chapters.
Standard descriptive statistics were computed for all outcome variables.
Outcome variables were death and mean duration of hospital admission.
Careful consideration should be given to the choice of outcome variables.
Outcome variables include time to completion and number of errors.
Four models were then run to analyze the outcome variables by sex.
Primary outcome variables were hospital discharge and overall mortality.
Appropriate descriptive methods will be used for all outcome variables.
Three primary outcome variables were defined in the study protocol.
Days with pain and migraine frequency were the primary outcome variables.
Outcome variables were.
The results of the study are positive in relation to all analysed outcome variables.
The following outcome variables were applied by the observer blinded to the groups.
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Retention in treatment and mortality among heroin users were the main outcome variables.
These outcome variables will be tested for their association with air pollution.
Changes in the repeated measures of outcome variables were analyzed via latent curve analysis.
The outcome variables of interest were split into clinical targets and process of care.
We find no statistically significant effect for suicidal shootings on any outcome variables of interest.
Continuous outcome variables were analysed using a mixed effect model for repeated measures.
Empirical studies have shown that depressive symptoms vary substantially according to antecedents and outcome variables.
Information on the outcome variables and the control characteristics were derived from these surveys.
Subsequently, bivariate analysis was performed using depression and stress as outcome variables.
Regressions will examine the same outcome variables that the previous three chapters have examined.
Outcome variables The outcome variables are of critical importance in the evaluation of treatment effectiveness.
Furthermore, it is assumed that all outcome variables are independent of one another.
The outcome variables were categorized as yes / no.
Overall, results from the secondary outcome variables supported the primary objective.
The outcome variables were symptoms of vertigo, tinnitus and hypoacusis.
Mixed-effects models for continuous outcome variables with auto-correlated residuals.
The analyzed outcome variables were PICU discharge event discharge or death and length of hospitalization.
The epidemiologist is interested in relationships between variables, chiefly exposure and outcome variables.
The primary outcome variables were all-cause and cardiovascular mortality.
Logistic regression, also called a logit model, is used to model dichotomous outcome variables.
The outcome variables were stress, prevalent health problems and ways of coping.
Afterwards, the participants were assessed for the collection of the outcome variables as described below.
Two outcome variables were established, duration of stay and reason for discharge.
Initially, there was a univariate analysis between the outcome variables and potential associated factors.
Outcome variables were perineal pain and edema, pharmacological analgesia and adverse reactions.
Second, it captures gaps in outcome variables rather than gaps in input variables.
Probit regression, also called a probit model, is used to model dichotomous or binary outcome variables.
The outcome variables were self-reported morbidities, such as diabetes mellitus DM and dyslipidemia.
Table 2 shows the results of multivariate analyses of associations between socioeconomic variables and outcome variables.
Outcome variables in the regression models included percent predicted FEV1 and FVC.
Multiple comparisons of the different groups were made using the t-test for primary outcome variables.
Are the outcome variables quantifiable?
The data were collected through a structured tool that contained sociodemographic, clinical, catheter and outcome variables.
The efficacy outcome variables are,.
Five CDR composite scores were used as the primary / secondary outcome variables.
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Examples of using Outcome
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That is the outcome of its life in a form
Two outputs will contribute to this outcome
Enforcement outcome eligible for a whistleblower award
Examples of using Variables
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Variables are introduced in order to track such bequests
The plan of the variables is the following
Variables to be probed as part of the study include
