Analysis of human immune responses in quasi-experimental settings: tutorial in biostatistics

Abstract

Abstract Background 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. However, rigorous discussion of methodological issues related to designs and statistical analysis that are appropriate for longitudinal studies is lacking. Methods In 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. Results We 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. Conclusions The 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.