The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p06ciqes4eaqo6n0zyqtz9p21nfb.htm>, a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p0jvbbqkt0gs2cn1lo4zndbqs1pe.htm>, tabulation functions which can create any table in different styles <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/n1ql5xnu0k3kdtn11gwa5hc7u435.htm> and other more readable functions. The code is optimized to work fast even with datasets of over a million observations.

There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.

Methods for statistical analysis of count data and quantal data.
    For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>).
    For the analysis of quantal data an implementation of the Closure Principle Fisher–Freeman–Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated.
    All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach.