Applied Longitudinal Data Analysis for Epidemiology: A Practical GuideThis book discusses the most important techniques available for longitudinal data analysis, from simple techniques such as the paired t-test and summary statistics, to more sophisticated ones such as generalized estimating of equations and mixed model analysis. A distinction is made between longitudinal analysis with continuous, dichotomous and categorical outcome variables. The emphasis of the discussion lies in the interpretation and comparison of the results of the different techniques. The second edition includes new chapters on the role of the time variable and presents new features of longitudinal data analysis. Explanations have been clarified where necessary and several chapters have been completely rewritten. The analysis of data from experimental studies and the problem of missing data in longitudinal studies are discussed. Finally, an extensive overview and comparison of different software packages is provided. This practical guide is essential for non-statisticians and researchers working with longitudinal data from epidemiological and clinical studies. |
Contents
Continuous outcome variables | 16 |
Continuous outcome variables relationships with other variables | 51 |
The modeling of time | 86 |
Other possibilities for modeling longitudinal data | 103 |
Dichotomous outcome variables | 119 |
Categorical and count outcome variables | 141 |
Analysis of experimental studies | 163 |
Other editions - View all
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide Jos W. R. Twisk Limited preview - 2013 |
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide Jos W. R. Twisk Limited preview - 2003 |
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide Jos W. R. Twisk No preview available - 2013 |
Common terms and phrases
_cons adjusted for clustering analysis and mixed autoregressive model baseline value calculated categorical outcome variable categorical variable chi2 clustering on id compared Conf confidence interval continuous outcome variable covariates X1 dichotomous outcome variable Equation example dataset exchangeable correlation structure first follow-up measurement id Number likelihood ratio test linear mixed model linear regression log likelihood logistic GEE analysis logistic mixed model logistic regression longitudinal analysis longitudinal data analysis longitudinal relationship longitudinal study MANOVA for repeated missing data mixed model analysis multiple imputation Number of groups Number of obs number of subjects Obs per group observations for subject odds ratio outcome variable Yand Output p-value P>IzI Poisson Poisson regression Prob proportion of change random intercept random slope regression analysis regression coefficient repeated measurements Scale parameter Semirobust shows the results slope for X2 SPSS Squares df standard errors sum of squares Table time-points variance Wald within-subject Ycat Ydich