### Usage of the web-tool:

#### Data upload

Load your data set in *.txt file format using this tab.

#### Combination Approach

Use this tab to combine diagnostic tests. **dtComb** supports 142 combination methods for combining diagnostic tests.

First, it is decided which one to choose in the 4 main combination approaches. There are 8 combination methods in linear combination approach, 7 in non-linear combination approach, 14 in mathematical operators and 113 combination methods in machine learning algorithms.

**Linear Combination Methods**
The binary logistic regression model is used. However, for a more straightforward interpretation, slope values are rounded to a given **digit number**, and the combination score is computed.

Su and Liu???s combination score is obtained by using **Fisher???s discriminant function** under the assumption of a multivariate normal distribution model and proportional covariance matrices.

A binary logistic regression model is fitted using the **maximum-likelihood method**.

This method linearly combines the minimum and maximum values of the markers by finding a ** parameter ??** that maximizes the corresponding **Mann-Whitney statistic**.

Uses the same binary logistic regression model. The combination score is obtained by proportioning the slope values to calculate the **?? parameter** .

Pepe, Cai, and Langton combination score is obtained by using **AUC as the parameter** of a logistic regression model

Minimax method is an extension of Su & Liu???s method.

Todor and Saplacan???s method uses trigonometric functions to calculate the combination score. The combination score is obtained by the **?? value** that optimizes the corresponding AUC.

**Nonlinear Combination Methods**
The method builds a logistic regression model with the feature space created and returns the probability of a positive event for each observation. It is implemented with **degrees** of the fitted polynomials taken from the user.

Ridge regression is a penalizing method used to estimate the coefficients of highly correlated variables and in this case the polynomial feature space created from two biomarkers. For the implementation of the method, glmnet library is used with two functions: cv.glmnet() to run a cross validation model to determine the tuning parameter **??** and glmnet() to fit the model with the selected tuning parameter.It is implemented with **degrees** of the fitted polynomials taken from the user.

Lasso regression is also a penalizing method with one difference is that at the end this method returns the coefficients of **some features as 0**, makes this method useful for feature elimination as well. The implementation is similar to Ridge regression, cross validation for parameter selection and model fit are implemented with glmnet library.It is implemented with **degrees** of the fitted polynomials taken from the user.

Elastic Net regression is obtained by combining the penalties of Ridge and Lasso regression to get the best of both models. The model again includes a tuning parameter ?? as well as a **mixing parameter ??** taken form the user which takes a value between 0 (ridge) and 1 (lasso) to determine the weights of the loss functions of Ridge and Lasso regressions. It is implemented with **degrees** of the fitted polynomials taken from the user.

**In nonlinear approaches, polynomial, ridge and lasso regression methods, an interaction that may exist between two diagnostic tests can be included in the model. For this, the Include of interaction option must be selected as TRUE.**
With the applications of regression models in a polynomial feature space the second non-linear approach to combining biomarkers comes from applying several regression models to the dataset using a function derived from piecewise polynomials. Splines are implemented with **degrees of freedom** and **degrees** of the fitted polynomials taken from the user. For the implementation splines library is used to build piecewise logistic regression models with base splines.

In addition to the basic spline structure, Generalized Additive Models are applied with natural cubic splines and smoothing splines using the gam library in R. It is implemented with degrees of freedom taken from the user.

**Mathematical Operators**
Add, Subtract, Multiply ve Divide methods represent basic arithmetic operators.

The distance measures included in the package are Euclidean, Manhattan, Chebyshev, Kulczynski_d, Lorentzian, Avg, Taneja, and Kumar-Johnson.

These methods, in which one of the two diagnostic tests is taken as base and the other as an exponent, are indicated by the names baseinexp (markers_{1} ^{markers2}) and expinbase (markers_{2} ^{markers1}).

**Machine-Learning Algorithms**
113 machine learning algorithms available in the caret, library which are used to train classification models using machine learning algorithms and make predictions using these models, are used within the scope of Machine-Learning Algorithms.IMPORTANT: See available-model for further information about the methods used in this methods. All resampling and Preprocessing methods included in the Caret package can also be used in dtComb while content is ML algorithms.

#### Resampling methods

#### Standardization methods

#### There are options under the Advanced checkbox, such as cuttoff method, direction, and confidence levels.

There are 34 methods available to determine the optimum cutoff. Cutoff methods can be found in the

OptimalCutpoints package of R.

### Outputs

When the analysis is complete, the **Download Model** button appears. With the help of this button, then the user can download the model he has trained to make predictions.

#### ROC Curve

An **ROC Curve** appears you when the analysis is complete. Here you can see ROC Curve of both the generated Combination Score, Marker 1 and Marker 2.

#### AUC Table

Under **AUC Table** subtab, you can get area under the curve (AUC) value and its standard error, confidence interval and statistical significance, instantly.

#### ROC Coordinates

Each false positive and true positive points can be found under **ROC Coordinates** subtab for each marker.

#### Multiple Comparisons Table

**Multiple Comparisons Table** alt tab can be used to make pairwise statistical comparisons for ROC curves of two markers and combination scores.

#### Cut points

#### Performance Measures

#### Plots

#### Predict