SOLAS for Missing Data Analysis

Pharmaceutical Clinical Trials

Missing values are inevitable in most clinical trial studies. Using SOLAS to impute missing values in primary endpoint data reduces potential bias, while increasing the accuracy of subsequent analysis. Incorporating SOLAS into your clinical trial protocol, also helps you comply with FDA & EMA guidelines on missing data & sensitivity analysis.

Market Research Surveys

The Hot Deck/Nearest Neighbor method in SOLAS allows market researchers to intuitively & scientifically impute missing survey data. SOLAS guides you through the hot-decking process, so that you understand how the completed dataset is compiled, allowing you to explain the survey results with confidence to colleagues & clients.

Academic Research Projects

Academic researchers will love the new collapse missing data pattern feature. This unique visualisation tool allows you to quickly & simply interpret missing values in your data. Academic organizations can also benefit from reduced researcher pricing and special offer student discounts: Check out our great academic prices here.

Government & Population Studies

The new 64-Bit capability boosts SOLAS’s processing powering and allows government agencies to impute missing values in much larger datasets. In addition, the Predictive Mean Matching multiple imputation method in SOLAS (as described by Roderick J. Little 1988) works well with very large survey and population studies data.