Analysis of Longitudinal Data (Oxford Statistical Science Series): Medicine & Health Science Books @ Longitudinal Data Analysis. Longitudinal data (also known as panel data) arises when you measure a response variable of interest repeatedly through time for multiple subjects. Thus, longitudinal data combines the characteristics of both cross-sectional data and time-series data. Longitudinal data allows researchers to assess temporal disease aspects, but the analysis is complicated by complex correlation structures, irregularly spaced visits, missing data, and mixtures of time-varying and static covariate effects. We review modern statistical methods designed for these challenges.‎Challenges of Longitudinal · ‎Starter Methods for · ‎Modern Methods for.


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This lack of understanding can lead to inappropriate or inefficient analysis, inaccurate results, and simplistic or wrong interpretations, conclusions, and judgments.

Longitudinal Data Analysis Procedures

While we analysis of longitudinal data that sophisticated and advanced analysis of longitudinal data models cannot, and should not, compensate for poor study design and execution, we also maintain that solely using simplistic analytic methods can scuttle detection of important signals and effects, even in well-designed and -conducted studies.

In this review, we provide, using an informal and straightforward style, an organized overview of the types of methods available and suggest approaches for situations under which they may be appropriate.

For readers wanting user-friendly drop-down menus, SPSS [ 3 ]and JMP [ 4 ] software provide analysis options with some of the advanced modeling techniques reviewed here e. In our discussions, we focus more on longitudinal observational research prospective and retrospectiveand to a lesser extent on randomized interventions or randomized clinical trials.

We also primarily focus examples on characterizing and modeling progression, i. Moreover, we focus mostly on research designs in which the dependent variable outcome is an essentially continuous numeric variable, the most common analysis of longitudinal data, and where only one is studied at a time univariate, not multivariate analysis with respect to the dependent variable.

Emphasis of This Review and Further Reading Although we aim for coverage of all major relevant methods of analysis, no review of longitudinal methods can hope to exhaustively cover every specialized, custom-built, ad analysis of longitudinal data or improvised analysis method developed for this kind of research.

Neither does this paper attempt to discuss all the methodological and design issues relevant to longitudinal studies, but analysis of longitudinal data primarily on data analysis and those methodological issues closely tied to that.


Further, measurement issues not intimately connected to our analysis methods cannot be pursued here because of space limitations, though we feel they are important and too often overlooked by clinical researchers.

We do not emphasize research designs that involve a very large number of measurements e. Similarly, Event History Methods, which analyze time until some clinically meaningful event, often analysis of longitudinal data, e.

While time series and event history analyses are powerful methods, they are analysis of longitudinal data relatively more developed and established techniques with a long history of abundant literature on their use and interpretation, and are also not usually applicable to the focus of examining predictive effects on a numeric variable assessed on relatively few observations across time for each of many subjects.

We touch upon these and other methods for the sake of completeness, contrast, and clarification of which niche each method does and does not fill across a broad constellation of methods, and include them within a general flow chart of longitudinal methods.

We give priority to breadth of coverage over depth. Many important details, including mathematical derivations and formulas, can be pursued in references and elsewhere in the literature.

An Overview of Longitudinal Data Analysis Methods for Neurological Research

We provide a systematic perspective on old and newly emerging techniques in the rapidly developing area of longitudinal research. While analysis of longitudinal data primary purpose is to present a review of existing methods and not to introduce new techniques, we describe, mainly for illustrative purposes, some of our own variations and extrapolations in the application of these methods.

Another reason for writing this article is that we feel there is an, as yet, unmet need for a review in the clinical neuroscience literature that covers a broad overview of longitudinal analysis methods in a deliberate manner that is accessible to researchers without an advanced background in statistics or modeling.

There are many examples of applications of longitudinal analysis, and methodological papers on longitudinal statistical techniques that are intended for statistically analysis of longitudinal data audiences [ 56 ] are more narrow in breadth in terms of the methods discussed [ 78910 ] or are more focused on the specific concerns of a more restricted substantive area of neurological research [ 11 analysis of longitudinal data.

An Overview of Longitudinal Data Analysis Methods for Neurological Research

An excellent article by Petkova and Teresi [ 12 ] provides a sophisticated discussion of random-effect models, but is more technical and less broad in coverage. There are some excellent new or recently updated textbooks on longitudinal analysis of longitudinal data analysis [ 1415 ], which we highly recommend for reference and further reading.


While data QA and pre-processing are analysis of longitudinal data and unexciting, they are essential first steps to ensure proper and efficient data modeling — if one cannot spare the time to redo everything, then one must give sufficient attention to QA up-front.

Data cleaning includes thorough examination of missing data, searching for duplicate records, statistical and graphical screens, and setting up programming checks to alert you to improper data values due to input or transcription errors or outliers to be considered.

Importance of Iterative Graphical Data Analysis before, during and after Modeling Steps Graphs should not just be limited to analysis of longitudinal data in manuscripts or slides in a presentation to illustrate a point.