The phrase "a backward logistic regression model" is correct and usable in written English.
It can be used in statistical or data analysis contexts when discussing a specific type of regression analysis that involves backward elimination of variables.
Example: "In our study, we applied a backward logistic regression model to identify the most significant predictors of patient outcomes."
Alternatives: "a backward elimination logistic model" or "a backward selection logistic regression".
All factors were included in a backward logistic regression model.
Variables with a p-value ≤0.2 on univariate analyses were included in a backward logistic regression model for each domain.
The result was compared with a backward logistic regression model by entering the same variables as the above model, and this led to a similar result.
Our analysis included, however, in a backward logistic regression model, all variables identified in univariate analysis and adjusted for severity of disease.
Variables found to be significant on univariable analysis were entered into a backward logistic regression model, and adjusted odds ratios calculated for each variable.
We adjusted for covariates obtained from baseline data (Table 1) using a backward logistic regression model, only if the covariates were judged to be clinically relevant and if baseline values differed significantly (level 0.1) between respondents and non-respondents.
A stepwise backward logistic regression model and a likelihood ratio test allowed demonstration of the most significant predictors of use of prayer or spiritual healing.
χ Tests, analyses of variance (to determine associations) and a stepwise backward logistic regression model (for the most significant predictors) using a likelihood ratio test were used to determine the outcome measures.
Statistically significant (p<0.05) variables were included in a conditional backward logistic regression model.
Variables found by the univariate analysis to be statistically significant (p<0.05) were included in a conditional backward logistic regression model.
We used a stepwise backward logistic regression model exploring the individual strength of the possible predictors.
