• Life table,
• Median residual lifetime,
• Charts for the survival distribution function (SDF), the probability density, the hazard rate,
• Charts -Log(SDF), Log(-Log(SDF)),
• Comparison tests of the survival functions (Log-rank, Wilcoxon, Tarone-Ware).

• Kaplan-Meier table,
• Mean and median residual lifetime with confidence intervals,
• Survival charts (SDF), -Log(SDF), Log(-Log(SDF)),
• Comparison tests of the survival functions (Log-rank, Wilcoxon, Tarone-Ware).

• Cox model - A Cox model is a well-recognised statistical technique for exploring the relationship between the survival of a patient and several explanatory variables. A Cox model provides an estimate of the treatment effect on survival after adjustment for other explanatory variables. It allows us to estimate the hazard (or risk) of death, or other event of interest, for individuals, given their prognostic variables. Interpreting a Cox model involves examining the coefficients for each explanatory variable. A positive regression coefficient for an explanatory variable means that the hazard is higher. Conversely, a negative regression coefficient implies a better prognosis for patients with higher values of that variable.
• Taking into account strata - When the proportional hazards hypothesis does not hold, the model can be stratified. If the hypothesis holds on sub-samples, then the partial likelihood is estimated on each sub-sample and these partial likelihoods are summed in order to obtain the estimated partial likelihood. In XLSTAT, strata are defined using a qualitative variable.

This method was first developed during World War II to develop effective means of detecting Japanese aircrafts. It was then applied more generally to signal detection and medicine where it is now widely used. The problem is as follows: we study a phenomenon, often binary (for example, the presence or absence of a disease) and we want to develop a test to detect effectively the occurrence of a precise event (for example, the presence of the disease). Once the test has been applied to a given population, several indices are used to evaluate the test. The following basic values are used:

• True positive (TP): Number of cases that the test declares positive and that are truly positive.
• False positive (FP): Number of cases that the test declares positive and that in reality are negative.
• True negative (VN): Number of cases that the test declares negative and that are truly negative.
• False negative (FN): Number of cases that the test declares negative and that in reality are positive.
• N = TP+FP+TN+FN

XLSTAT computes the following indices:

• Sensitivity (equivalent to the True Positive Rate): Proportion of positive cases that are well detected by the test. In other words, the sensitivity measures how the test is effective when used on positive individuals. The test is perfect for positive individuals when sensitivity is 1, equivalent to a random draw when sensitivity is 0.5. If it is below 0.5, the test is counter-performing and it would be useful to reverse the rule so that sensitivity is higher than 0.5 (provided that this does not affect the specificity). The mathematical definition is given by: Sensitivity = TP/(TP + FN).
• Specificity (also called True Negative Rate): proportion of negative cases that are well detected by the test. In other words, specificity measures how the test is effective when used on negative individuals. The test is perfect for negative individuals when the specificity is 1, equivalent to a random draw when the specificity is 0.5. If it is below 0.5, the test is counter performing-and it would be useful to reverse the rule so that specificity is higher than 0.5 (provided that this does not affect the sensitivity). The mathematical definition is given by: Specificity = TN/(TN + FP).
• False Positive Rate (FPR): Proportion of negative cases that the test detects as positive (FPR = 1-Spécificité).
• False Negative Rate (FNR): Proportion of positive cases that the test detects as negative (FNR = 1-Sensibilité) Prevalence: relative frequency of the event of interest in the total sample (TP+FN)/N.
• Positive Predictive Value (PPV): Proportion of truly positive cases among the positive cases detected by the test. We have PPV = TP / (TP + FP), or PPV = Sensitivity x Prevalence / [(Sensitivity x Prevalence + (1-Specificity)(1-Prevalence)]. It is a fundamental value that depends on the prevalence, an index that is independent of the quality of the test.
• Negative Predictive Value (NPV): Proportion of truly negative cases among the negative cases detected by the test. We have NPV = TN / (TN + FN), or PPV = Specificity x (1 - Prevalence) / [(Specificity (1-Prevalence) + (1-Sensibility) x Prevalence]. This index depends also on the prevalence that is independent of the quality of the test.
• Positive Likelihood Ratio (LR+): This ratio indicates to which point an individual has more chances to be positive in reality when the test is telling it is positive. We have LR+ = Sensitivity / (1-Specificity). The LR+ is a positive or null value.
• Negative Likelihood Ratio (LR-): This ratio indicates to which point an individual has more chances to be negative in reality when the test is telling it is positive. We have LR- = (1-Sensitivity) / (Specificity). The LR- is a positive or null value.
• Odds ratio: The odds ratio indicates how much an individual is more likely to be positive if the test is positive, compared to cases where the test is negative. For example, an odds ratio of 2 means that the chance that the positive event occurs is twice higher if the test is positive than if it is negative. The odds ratio is a positive or null value. We have Odds ratio = TPxTN / (FPxFN).
• Relative risk: The relative risk is a ratio that measures how better the test behaves when it is a positive report than when it is negative. For example, a relative risk of 2 means that the test is twice more powerful when it is positive that when it is negative. A value close to 1 corresponds to a case of independence between the rows and columns, and to a test that performs as well when it is positive as when it is negative. Relative risk is a null or positive value given by: Relative risk = TP/(TP+FP) / (FN/(FN+TN)).

• Use this tool to generate an ROC curve that allows to represent the evolution of the proportion of true positive cases (also called sensitivity) as a function of the proportion of false positives cases (corresponding to 1 minus specificity), and to evaluate a binary classifier such as a test to diagnose a disease, or to control the presence of defects on a manufactured product.

• Bland Altman analysis and plot Use this feature to compare a method to a reference method or to a comparative method.
• Paired t test Use this tool to perform a paired t test to compare two methods.
• Difference plot Use this tool to draw a difference plot to compare two methods.