The goal of this platform is to automate the statistical processes for studying the differences between multiple groups. Before explaining the tests performed, we will review the basic concepts needed to interpret these tests.
Initially, the selected tests are:
ANOVA is a parametric test used to determine if there is a significant difference between two or more groups for a given measurement. Group formation is done using categorical variables. The null hypothesis (H0) for this test is the equality of all groups (no significant difference), while the alternative hypothesis (H1) is that at least one group differs from the others. If the p-value is below our significance level, we reject the null hypothesis, and we can perform a Tukey test (Emmeans) to estimate the means of each group and determine which ones are different.
In a study measuring PIE, the dependent variable would be PIE, and the explanatory variables could be the product and time (0H, 6H, etc.).
The Tukey test is used after rejecting the null hypothesis in an ANOVA. It helps determine which groups differ from each other.
Mixed models combine ANOVA and the Tukey test, allowing us to detect if there are differences between multiple groups within a population and measure those differences if they exist. Unlike ANOVA, it relies on two types of effects:
We assume that fixed effects react similarly across all individuals, while variations are captured by random effects.
In a study measuring PIE, the dependent variable would be PIE. The fixed effects could be the product and time (0H, 6H, etc.), and the panelist would be a random effect. In a test studying the cellular response to different products, the dependent variable could be collagen production, the product concentration could be a fixed effect, and the cell line could be a random effect.
The Kaplan-Meier method is used to estimate survival probabilities over time. It allows for visualizing survival data and helps determine if different groups (e.g., treatment vs. control) exhibit different survival patterns over time.
This method is widely applied in clinical studies where the outcome of interest is time to event (such as time to death, disease recurrence, or other outcomes). The Kaplan-Meier curve provides an intuitive representation of survival data and can be compared across different groups.
In a study measuring the longevity of lipstick wear, the event could be defined as the point when the lipstick wears off. Different curves can represent different lipstick products, allowing for comparison.
The Cox regression model, also known as the proportional hazards model, is used to assess the effect of multiple variables on survival time. Unlike Kaplan-Meier, which focuses on one group or treatment at a time, Cox regression allows for the inclusion of covariates and the examination of their influence on survival while adjusting for other factors.
This model assumes proportional hazards, meaning the effect of the covariates on the hazard (risk of event occurrence) is constant over time.
In a study on the long-lasting effect of lipstick, factors such as product type, application technique, and environmental conditions could be included in the model to assess their effect on how long the lipstick stays on.
The Log Rank test is used to compare the survival distributions of two or more groups. It is a non-parametric test and can be used alongside Kaplan-Meier curves to determine whether there is a statistically significant difference between the survival curves of different groups.
The null hypothesis for the Log Rank test is that the survival curves are equal across the groups being compared.
In a comparison of different lipstick brands, the Log Rank test could be used to determine if there is a significant difference in the longevity of wear across the different brands.