:Confident results for complex regression :
LogXact 10: Exact inference for logistic regression
The complexity of conducting regression analysis over multiple covariates is well-documented. The challenge only intensifies when coupled with small sample sizes or missing data sets. LogXact aims to provide simple and accurate solutions for such difficulties.
LogXact can handle many varieties of response data including continuous and binary, polytonomous, count, and missing data. Users can expect confidence in their results with LogXact’s advanced regression techniques.
The advantages of LogXact 10
LogXact 10 offers pioneering methods in exact inference and regression modeling to provide rapid and accurate analysis. Powerful algorithms are built to analyze stratified and unstratified data sets of all sizes. Cytrel-developed algorithms are carefully tested, highy validated in practice, and compliant the guidance found in the FDA's CFR Part 11.
Read the Guidance: CFR art 11, Electronic Records; Electronic Signatures — Scope and Application
Replay the StatXact® 10 Instructional Webinar Free
Utah State's Professor Chris Corcoran demonstrates how to solve difficult research problems using the exact statistical methods in StatXact
LogXact remains an industry leader in all facets of regression modeling using exact inference. It is the first to offer the widely acclaimed and highly demanded PMLE procedure that minimizes separation bias while exhibiting significantly lower error rates compared to typical maximum likelihood estimators.
The Toolkit: LogXact performs regression analysis for continuous, binary, polytonomous and count data. It can also apply advanced regression techniques to data sets with missing values.
Missing Covariates: Only in LogXact can users accurately fit general linear models in cases of missing categorical covariates (models include Logit, Probit, CLoglog, Poisson and Normal.)
Large Data Sets: LogXact provides options to handle large data sets using exact methods, Monte Carlo sampling and Markhov Chain Monte Carlo sampling. A useful ‘Exploration Mode’ allows users to specify parameters to build networks that satisfy analysis needs.
New Developments: LogXact has recently added several features to its world-class toolkit including Firth’s PMLE procedure; the calculation of mid-p corrected confidence intervals for a variety of models; best subset selection in binary logistic regression; the force inclusion of variable to the best subsets; and profile likelihood confidence intervals for parameters of binary logistic regression.Read our published articles on Logistic Regression here