Identifying and Handling Continuous Variables for Logistic Regression Analysis

Feb 13, 2025, 4:09 AM

Excel for Analysis vizly.xlsx

Missing file

My independent variable is Fracture types, which in this case is between B-Type and C-Type fractures (it is a classification for Tibial plateau fractures).
The dependent variables of my data are Age, Sex, Mechanism of injury, Management, Comorbidities and complications (as described below)
The mechanism of injury is divided into high energy trauma and low energy trauma.
Surgical treatment as demonstrated in the excel is divided into Surgical Mx (ORIF - Open Reduction and Internal Fixation) or not (conservative management).
The outcomes of the management and complications following treatment were recorded - superficial infection, deep infection, CPN Palsy, compartment syndrome, PTOA (Post-traumatic Osteoarthritis), DVT (Deep Vein Thrombosis), any complications.
With fracture types (B type v C Type) as my independent variable - I am trying to run a logistic regression analysis between my independent variable and co-variables to determine if each fracture type plays a significant role or not in each of these.
To perform a logistic regression analysis with Fracture type as the independent variable and the other variables as dependent variables, we need to:
  1. Encode the categorical variables: Convert Fracture type into numerical values (e.g., B-Type as 0 and C-Type as 1).
  2. Prepare the dependent variables: Ensure all dependent variables are in a suitable format for logistic regression (binary or categorical).
  3. Fit a logistic regression model: Use a logistic regression model to determine the significance of Fracture type on each dependent variable.
Let's start by encoding the Fracture type and preparing the data for logistic regression.
The Fracture type column has been successfully encoded into numerical values, where C-Type is represented as 1. Now, we will proceed to fit logistic regression models to determine the significance of Fracture type on each of the dependent variables.
Let's start by fitting a logistic regression model for each dependent variable and evaluate the significance of the Fracture type.
There are some missing values in the dataset, particularly in the AGE, SEX, MECHANISM of INJURY, Fracture type.1, and other columns. To proceed with logistic regression, we should handle these missing values.
We can either fill these missing values with a suitable method (e.g., mean, median, mode) or drop the rows with missing values. For the purpose of this analysis, let's drop the rows with missing values to ensure the integrity of the logistic regression model.
Let's proceed with dropping the rows with missing values.
All missing values have been successfully removed from the dataset. We can now proceed with fitting logistic regression models to evaluate the impact of Fracture type on each of the dependent variables.
Let's start by fitting a logistic regression model for each dependent variable and assess the significance of Fracture type.
The error encountered is a ValueError indicating an "Unknown label type: unknown." This error typically occurs when attempting to fit a classification model (like logistic regression) on a target variable that is not binary or categorical.
In our case, the dependent variables include both binary/categorical variables and continuous variables. Logistic regression is suitable for binary or categorical outcomes, not continuous ones.
To resolve this, we should:
  1. Ensure that the dependent variables are binary or categorical: For logistic regression, the dependent variable should be binary (0 or 1) or categorical with a limited number of categories.
  2. Convert continuous variables to categorical if needed: If any dependent variable is continuous, consider converting it to a binary or categorical variable if it makes sense for the analysis.
Let's identify which dependent variables are continuous and handle them appropriately.
To proceed with logistic regression, we need to ensure that all dependent variables are binary or categorical. Let's identify which variables are continuous and consider converting them to binary or categorical variables if appropriate.
Next Steps:
  1. Identify continuous variables among the dependent variables.
  2. Convert continuous variables to binary or categorical if it makes sense for the analysis.
  3. Re-run logistic regression for binary/categorical dependent variables.
Would you like to proceed with converting continuous variables to binary/categorical, or do you have specific variables in mind for logistic regression?

Run logistic regression analysis between Fracture types and my covariables

To perform logistic regression analysis between Fracture type and the covariables, we need to ensure that the covariables are suitable for logistic regression. This means they should be binary or categorical.
Let's proceed with fitting logistic regression models for each covariable that is binary or categorical. We'll evaluate the impact of Fracture type on these covariables.
The logistic regression analysis between Fracture type and the covariables has been completed. Here are the key results:
  1. SEX:
    • Precision: 0.59 for class 0, 0.55 for class 1
    • Recall: 0.49 for class 0, 0.64 for class 1
    • Accuracy: 56.64%
  2. MECHANISM of INJURY:
    • Precision: 0.52 for class 0, 0.59 for class 1
    • Recall: 0.63 for class 0, 0.48 for class 1
    • Accuracy: 55.24%
  3. MANAGEMENT (ORIF):
    • Precision: 0.56 for class 0, 0.0 for class 1
    • Recall: 1.0 for class 0, 0.0 for class 1
    • Accuracy: 89.51%
  4. Comorbidities:
    • Precision: 0.0 for class 0, 0.57 for class 1
    • Recall: 0.0 for class 0, 1.0 for class 1
    • Accuracy: 56.64%
The results indicate varying levels of precision, recall, and accuracy across different covariables. Some covariables, like MANAGEMENT (ORIF), show high accuracy but with limitations in class 1 prediction.
Would you like to explore any specific covariable further or perform additional analysis?
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