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The HIV epidemiology literature is replete with studies that categorize continuous predictors, such as age and CD4+ cell count. It is well known that such an approach is suboptimal, but it persists in part because results are easy to interpret after categorization. Splines may be used to incorporate continuous predictors with smoothed curves into regression models without categorization or linearity assumptions. Properly presenting and interpreting results from analyses with splines is critical for their widespread use. With data from 13,706 antiretroviral initiators in Latin America, we demonstrate how to interpret results from a Cox regression model using restricted cubic splines.