Journal of Data Science

We introduce the stepp packages for R and Stata that implement the subpopulation treatment effect pattern plot (STEPP) method. STEPP is a nonparametric graphical tool aimed at examining possible heterogeneous treatment effects in subpopulations defined on a continuous covariate or composite score. More pecifically, STEPP considers overlapping subpopulations defined with respect to a continuous covariate (or risk index) and it estimates a treatment effect for each subpopulation. It also produces confidence regions and tests for treatment effect heterogeneity among the subpopulations. The original method has been extended in different directions such as different survival contexts, outcome types, or more efficient procedures for identifying the overlapping subpopulations. In this paper, we also introduce a novel method to determine the number of subjects within the subpopulations by minimizing the variability of the sizes of the subpopulations generated by a specific parameter combination. We illustrate the packages using both synthetic data and publicly available data sets. The most intensive computations in R are implemented in Fortran, while the Stata version exploits the powerful Mata language.

Abstract: Nowadays, extensive amounts of data are stored which require the development of specialized methods for data analysis in an understandable way. In medical data analysis many potential factors are usually introduced to determine an outcome response variable. The main objective of variable selection is enhancing the prediction performance of the predictor variables and identifying correctly and parsimoniously the faster and more cost-effective predictors that have an important influence on the response. Various variable selection techniques are used to improve predictability and obtain the “best” model derived from a screening procedure. In our study, we propose a variable subset selection method which extends to the classification case the idea of selecting variables and combines a nonparametric criterion with a likelihood based criterion. In this work, the Area Under the ROC Curve (AUC) criterion is used from another viewpoint in order to determine more directly the important factors. The proposed method revealed a modification (BIC) of the modified Bayesian Information Criterion (mBIC). The comparison of the introduced BIC to existing variable selection methods is performed by some simulating experiments and the Type I and Type II error rates are calculated. Additionally, the proposed method is applied successfully to a high-dimensional Trauma data analysis, and its good predictive properties are confirmed.

In this article, we considered the analysis of data with a non-normally distributed response variable. In particular, we extended an existing Area Under the Curve (AUC) regression model that handles only two discrete covariates to a general AUC regression model that can be used to analyze data with unrestricted number of discrete covariates. Comparing with other similar methods which require iterative algorithms and bootstrap procedure, our method involved only closed-form formulae for parameter estimation. Additionally, we also discussed the issue of model identifiability. Our model has broad applicability in clinical trials due to the ease of interpretation on model parameters. We applied our model to analyze a clinical trial evaluating the effects of educational brochures for preventing Fetal Alcohol Spectrum Disorders (FASD). Finally, for a variety of simulation scenarios, our method produced parameter estimates with small biases and confidence intervals with nominal coverage probabilities.