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Showing posts from October, 2019

Graphics in SAS SGPLOT illustrating ANOVA analysis results.

Output and graphics from statistical programming packages are often time-consuming to read and interpret. In peer-reviewed publications you usually provide both a written assessment, tables and graphics illustrating data and analysis results. ANOVA analysis is still a very common analysis technique and it is possible to beautify the output from analysis using PROC SGPLOT ods graphics; proc format lib=work; value timevar 12='0-12 hrs' 18='12-18 hrs' 24='18-24 hrs' 32='Cumulated 0-24 hrs' ; run; proc sql;   create table estimates     (  Treatment char(12) label='Treatment Group'      , Time      num      label='Visit number'      , Time2     num      label='Visit number'      , TimeVar   char(18) label='Visit number'      , Mark      char(8)  label='p values'      , Est       num      label='Est'      , LCL       num      label='LCL'      , UCL       num      label='UCL'  

T tests in censored normal distribution

T-tests assume normal distributed random variates. We experience designs in which data take on a particular value and are otherwise normal distributed on a half axis. It might be the case in experiments in which a regimen is only applied due to symptoms or the participant's request. In such case a censored normal distribution assumption will increase the strength of the experiment, i.e. statistical power will be increased and assumptions of an underlying normal distribution will be easier to justify. The suggested test is based on the chi-square likelihood ratio test. The example is two independent groups of identical independently distributed samples and the null hypothesis is identical distributions. The density function splits into single probability, given as the integral of a normal density from negative infinity to the lower bound and the density of the normal distribution for values larger than the lower bound. The expression involves the distribution function for a G