Your English writing platform
Discover LudwigSuggestions(1)
Exact(1)
Covariate-adjusted OR for oily fish consumption were derived with logistic regression models.
Similar(59)
All other coefficients derived with binary logistic regression.
A multivariable conditional logistic regression model was derived with 30-day mortality as the dependent variable, and the propensity score as the matching variable [ 20].
For the development of the plotting position formula, the theoretical reduced variates were derived with consideration of the shape parameter of the generalized logistic distribution.
Oily fish consumption was highly skewed with a large percentage of non-consumers, and so was converted to a binary outcome variable (0 = no consumption, 1 = any consumption) with OR derived from logistic regression models adjusted for age, sex, ethnicity, total energy intake and survey year.
Representative drug resistance surveillance data reported to the World Health Organization between 1994 and 2011 were analysed to test the association between MDR-TB and age group (children aged <15 years versus adults aged ≥15 years), using odds ratios derived by logistic regression with robust standard errors.
Odds ratios (ORs) with 95% confidence intervals (CIs) for the association of potential risk factors with GERD were derived from logistic regression models.
Odds ratios (ORs) with 95% confidence intervals (CIs) for the associations of obesity and other risk factors with GERD were derived from logistic regression models.
Therefore, we repeated all analyses using drop-out weights for each participant derived from logistic regression models with loss to follow-up as an outcome variable.
Binary and polytomous logistic regression models were used to calculate the odds ratios (OR) and their 95%% confidence intervals (CI) for each quartile of calcium intake, and tests for trend were derived from logistic regression models with a single term representing the medians of each quartile group.
Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test comparing the observed versus expected probability of death within each risk group/decile derived from logistic regression models with mortality as the dependent variable and the model (SICK, LODS, PEDIA) as independent variables.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com