A latent Markov model separates density-driven masking from latent breast-cancer risk, but the reported masking-induced risk increase is a sensitivity consequence, not an empirical estimate.
Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data
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Decoupling risk and masking in mammographic density under irregular follow up using a latent Markov progression detection framework
A latent Markov model separates density-driven masking from latent breast-cancer risk, but the reported masking-induced risk increase is a sensitivity consequence, not an empirical estimate.