Clinical trials: robust tests are wonderful for imperfect data.

Robust tests are tests that can handle the inclusion into a data file of some outliers without largely changing the overall test results. Despite the risk of non-Gaussian data in clinical trials, robust tests are virtually never performed. The objective of this study was to review important robust tests and to assess whether they provide better sensitivity of testing than standard tests do.

Multistage regression, a novel method for making better predictions from your efficacy data.

Multistage regression is rarely used in therapeutic research, despite the multistage pattern of many medical conditions. Using an example of an efficacy study of a new laxative, path analysis and the 2-stage least square method were compared with standard linear regression. Standard linear regression showed a significant effect of the predictor "noncompliance" on drug efficacy at P=0.

Clinical Trials: Spline Modeling is Wonderful for Nonlinear Effects.

Traditionally, nonlinear relationships like the smooth shapes of airplanes, boats, and motor cars were constructed from scale models using stretched thin wooden strips, otherwise called splines. In the past decades, mechanical spline methods have been replaced with their mathematical counterparts. The objective of the study was to study whether spline modeling can adequately assess the relationships between exposure and outcome variables in a clinical trial and also to study whether it can detect patterns in a trial that are relevant but go unobserved with simpler regression models.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used.

Machine Learning in Therapeutic Research: The Hard Work of Outlier Detection in Large Data.

With large data files, outlier recognition requires a more sophisticated approach than the traditional data plots and regression lines. In addition, the number of outliers tends to rise linearly with the data's sample size. The objective of this study was to examine whether balanced iterative reducing and clustering using hierarchies (BIRCH) clustering is able to detect previously unrecognized outlier data.