Matched Longitudinal Analysis of Biomarkers Associated with Survival

The identification of host or pathogen factors linked to clinical outcome is a common goal in many animal studies of infectious diseases. When the disease is fatal, statistical analysis of such factors may be biased frommissing observations due to deaths. For example, when observations of a subject are censored before completing the intended study period, the complete trajectory will not be observed. Even if the factor is not associated with outcome, comparisons of data from survivors with those from nonsur-vivors may lead to the wrong conclusions regarding associations with survival. Comparisons between subjects must account for differing observation lengths for those who survive relative to those who do not. Analyzing data over an interval common to all subjects provides one solution but requires eliminating data, some of which may be informative about the differences between groups. Here, we present a novel approach, matched longitudinal analysis (MLA), for analyzing such data based onmatching biomarker intervals for survivors and nonsurvivors. We describe the results from simulation studies and from a study of mon-keypox virus infection in nonhuman primates. In our application, MLA identified lowmonocyte chemoattractant protein-1 (MCP-1) levels as having a statistically significant association with survival, whereas the alternative methods did not identify an association. The method has general application to longitudinal studies that seek to find associations of biomarker changes with survival. In studies of high-consequence pathogens, human studies of in-fection are typically not possible, making animal models the primary basis for evaluating both the immunological processe