On Data: Longitudinal Data

 "Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject." (Scott L Zeger & Kung-Yee Liang, "Longitudinal Data Analysis for Discrete and Continuous Outcomes", Biometrics Vol. 42(1), 1986)

"Longitudinal data sets in which the outcome variable cannot be transformed to be Gaussian are more difficult to analyze for two reasons. First, simple models for the conditional expectation of the outcome do not imply equally simple models for the marginal expectation, as is the case for Gaussian data. Hence, the analyst must choose to model either the marginal or conditional expectation. Second, likelihood analyses often lead to estimators of the regression coefficients which are consistent only when the time dependence is correctly specified." (Scott L Zeger & Kung-Yee Liang, "Longitudinal Data Analysis for Discrete and Continuous Outcomes", Biometrics Vol. 42(1), 1986)

"Longitudinal data comprise repeated observations over time on each of many individuals. Longitudinal data are in contrast to cross-sectional data where only a single response is available for each person. The statistical analysis of longitudinal data presents special opportunities and challenges because the repeated outcomes for one individual tend to be correlated with one another." (Scott L Zeger & Kung‐Yee Liang, "An overview of methods for the analysis of longitudinal data", Statistics in medicine vol. 11, 1992)

"We have two objectives for statistical models of longitudinal data: (1) to adopt the conventional regression tools, which relate the response variables to the explanatory variables; and (2) to account for the within subject correlation." (Scott L Zeger & Kung‐Yee Liang, "An overview of methods for the analysis of longitudinal data", Statistics in medicine vol. 11, 1992)

"Analysis of longitudinal data tends to be simpler because subjects can usually be assumed independent. Valid inferences can be made by borrowing strength across people. That is, the consistency of a pattern across subjects is the basis for substantive conclusions. For this reason, inferences from longitudinal studies can be made more robust to model assumptions than those from time series data, particularly to assumptions about the nature of the correlation." (Peter J Diggle et al, "Analysis of Longitudinal Data", 2002)

"The defining feature of a longitudinal data set is repeated observations on individuals enabling direct study of change. Longitudinal data require special statistical methods because the set of observations on one subject tends to be intercorrelated. This correlation must be taken into account to draw valid scientific inferences." (Peter J Diggle et al, "Analysis of Longitudinal Data", 2002)