Time Series Analysis : Univariate and Multivariate Methods William W.S. Wei
Publisher: Addison Wesley

Overall survival and time to treatment in 620 untreated CLL patients were analyzed retrospectively to evaluate the multivariate independence and predictive power of mutational status of immunoglobulin heavy chain variable gene segments This model was subsequently validated in independent patients series also using time to first treatment as end-point [4-8]. €Methods: This study examined spatial and temporal heterogeneities in the risk of malaria and the influencing factors on malaria incidence using geographical information system (GIS) and time series analysis. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series analysis: Univariate and multivariate methods. Time.Series.Analysis.Univariate.and.Multivariate.Methods.pdf. Is multivariate better than univariate? Univariate and multivariate Cox models were used to verify independent prognostic power of each parameter. Analyses of the patients with gene The average age of the 80 cases analyzed by cDNA microarrays was 65.0 years at time of primary surgery (range 28.2 to 87.7 years), similar to the average age of 64.4 years (range 28.2 to 91.5 years) for the total series. Time Series Analysis : Univariate and Multivariate Methods. Both univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Multivariate methods are very important in economics and much less so in other applications of forecasting.