Dataset for: Sampling strategies for approximating patient variability in population-based finite element studies of total hip replacement

Total hip replacements must be robust to patient variability for long-term success in the population. The challenge during the design process is evaluating an implant in a diverse population but the computational cost of simulating a population of subject-specific finite element (FE) models is not practical. We examined five strategies to generate representative subsets of subjects from a cohort of 103 implanted hip joint FE models to approximate the variability in output metrics. Comparing to the median and distribution of the 95th percentile composite peak micromotion (CPM) and polar gap in the full cohort (CPM median: 136 µm, interquartile range (IQR): 74 – 230 µm) (Polar Gap median: 467 µm, IQR: 434 – 548 µm), the Anatomic Sampling strategy (12 subjects) achieved the best balance of computational cost and approximation of the output metrics (CPM median: 169 μm, IQR: 78 – 236 μm) (Polar Gap median: 469 μm, IQR: 448 – 537 μm). Convex Hull Sampling (41 subjects) more closely captured the output metrics (CPM median: 99 μm, IQR: 70 – 191 μm) (Polar Gap median: 456 μm, IQR: 418 - 533 μm) but required over three times the number of subjects. Volume reduction of the convex hull captured the extremes of variability with subsets of 5 to 20 subjects, while the Largest Minimum-Distance strategy captured the variability toward the middle of the cohort. These strategies can estimate the level of variability in FE model output metrics with a low computational cost when evaluating implants during the design process.