Liu Y, Wang Z, Guo HC, Yu SX, Sheng H (2013) Modelling the effect of weather conditions on cyanobacterial bloom outbreaks in Lake Dianchi: a rough decision-adjusted logistic regression model. Environ Model Assess 18:199–207Liu Y,Wang Z,Guo H C,et al.Modelling the effect of weather ...
Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. If several risk factors for disease are considered in the same multiple logistic regression model, and some of these risk factors are measured wi...
1 utilized a logistic model for their risk-adjusted control chart, our approach, as detailed in "Risk adjusted EWMA control chart" section, is based on residuals derived from the Accelerated Failure Time (AFT) regression model by Tighkhorshid et al.6. In our proposed design, these residuals ...
We used the multiple imputation method to construct a logistic regression model. We used R and the Empower package (The R Foundation; http://www.r-project.org; version 4.2.0) for the statistical analyses, and a P value < 0.05 was considered as statistically significant....
The propensity score was calculated using logistic regression. For the matched cohort, matching for propensity score using “nearest neighbor” matching was performed, the maximum allowed distance was 0.001. Males and females were matched 1:1.
Logistic regression was used to adjust for confounding between factors, identifying risk factors with the strongest prognostic value for the outcome of severe and intermediate complications. The resulting model was tested by back-validation and validation. Results: The derived risk adjustment included all...
The most natural choice to model probability of excess zeros is to use a logistic regression model: logit( p i )= x i β (23) Impact of covariates on count data modeled through NB regression: log( λ i )= x i γ (24) Given π = P r(Y > 0), the probability of ...
Should it be considered as a pooled estimate adjusted for study-features, the same way we would adjust for confounders, say, in a logistic regression model? I have been taking a look in books and articles on the subject but have not been able so far to come up with a reasonably ...
A logistic regression model was developed to estimate risk-adjusted mortality. Trauma centers were then ranked based on their observed-to-expected (O/E) mortality ratio with 90% confidence intervals (CIs) and classified by outlier status: low outliers/high performers had a 90% CI for O/E ...
The method incorporates a probability of repossession (foreclosure) model developed with logistic regression and a haircut model using an OLS regression. The haircut represents the discount factor to be applied to the estimated sale price of the property, given that repossession occurs. The two ...