The Mechanistic-empirical pavement design guide (MEPDG) provides theoretically superior methodology, as compared with its predecessor, for the design and analysis of pavement structures. The mechanistic part refers to simulating pavement–tire interaction to calculate critical responses within pavement. The empirical part means prediction of pavement distress propagation over time using transfer functions that link a critical pavement response to a particular pavement distress. The mechanistic part of MEPDG simulates tire–pavement interaction in three steps: subdivision of pavement layers; complex modulus calculation at the middepth of each sublayer, considering velocity and temperature; and running the multilayered elastic theory (MLET) software, JULEA. Although MEDPG has a grounded methodology for pavement analysis, it has a number of limitations and unrealistic simplifications that result in inaccurate response predictions. These limitations are primarily related to the pavement analysis approach used in the MEPDG framework, MLET. By contrast, finite-element (FE) analysis has proven to be a promising numerical approach for overcoming these limitations and simulating pavement more accurately and realistically. Although comparison of MLET with FE analysis has been studied, the difference between FE and MEPDG simulations has not been quantified. This study fills that gap by developing linear equations that connect pavement responses produced by these two approaches to pavement analysis. The equations are developed for ten different pavement responses, using a total of 336 cases simulated using FE and MEPDG analyses. The cases modeled in simulations were selected to capture extreme conditions, i.e., thick and thin pavement structures with strong and weak material properties. The equations developed can help pavement researchers understand quantitatively the effect of MEPDG limitations. In addition, the equations may be used as adjustment factors for MEPDG to compute pavement responses more realistically without using computationally expensive approaches, such as FE analysis.