Abstract
BackgroundHuman immunology is a growing field of research in which experimental, clinical, and analytical methods of many life science disciplines are utilized. Classic epidemiological study designs, including observational longitudinal birth cohort studies, offer strong potential for gaining new knowledge and insights into immune response to pathogens in humans. However, rigorous discussion of methodological issues related to designs and statistical analysis that are appropriate for longitudinal studies is lacking.MethodsIn this communication we address key questions of quality and validity of traditional and recently developed statistical tools applied to measures of immune responses. For this purpose we use data on humoral immune response (IR) associated with the first cryptosporidial diarrhea in a birth cohort of children residing in an urban slum in south India. The main objective is to detect the difference and derive inferences for a change in IR measured at two time points, before (pre) and after (post) an event of interest. We illustrate the use and interpretation of analytical and data visualization techniques including generalized linear and additive models, data-driven smoothing, and combinations of box-, scatter-, and needle-plots.ResultsWe provide step-by-step instructions for conducting a thorough and relatively simple analytical investigation, describe the challenges and pitfalls, and offer practical solutions for comprehensive examination of data. We illustrate how the assumption of time irrelevance can be handled in a study with a pre-post design. We demonstrate how one can study the dynamics of IR in humans by considering the timing of response following an event of interest and seasonal fluctuation of exposure by proper alignment of time of measurements. This alignment of calendar time of measurements and a child's age at the event of interest allows us to explore interactions between IR, seasonal exposures and age at first infection.ConclusionsThe use of traditional statistical techniques to analyze immunological data derived from observational human studies can result in loss of important information. Detailed analysis using well-tailored techniques allows the depiction of new features of immune response to a pathogen in longitudinal studies in humans. The proposed staged approach has prominent implications for future study designs and analyses.
Highlights
Human immunology is a growing field of research in which experimental, clinical, and analytical methods of many life science disciplines are utilized
Rigorous discussion of methodological issues related to designs and statistical analysis that are appropriate for longitudinal studies is lacking
We addressed key questions of quality and validity of traditional and recently developed statistical tools applied to measures of immune responses and present examples that provide compelling evidence for the complexity of “field” sampling and the need for thorough examination of data originating from studies with a pre-post design
Summary
Human immunology is a growing field of research in which experimental, clinical, and analytical methods of many life science disciplines are utilized. Classic epidemiological study designs, including observational longitudinal birth cohort studies, offer strong potential for gaining new knowledge and insights into immune response to pathogens in humans. Human immunology is a growing field and includes methodologies of many experimental and clinical disciplines: molecular biology, microbiology, immunogenetics, clinical immunology, pathophysiology, epidemiology, and potentially others. In a study aimed to examine the effect of “an event”, say “infection by a pathogen” on a marker of an immune response such as antibody levels, a design or protocol for measuring such an effect in a fully controlled experiment may differ dramatically in a murine model and in a cohort of newborn children. If a study subject contributes two measurements: one before “an event of interest”, called a baseline measure and another measurement taken after an event, we are dealing with a so-called “repeated measurement” scenario, a pre-post design, which is the focus of this communication
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