Mendelian randomization using semiparametric linear transformation models.
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DOI: 10.1002/sim.8449
Abstract: Mendelian randomization (MR) uses genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome in the presence of unknown confounding. We are interested in the… read more here.
Keywords: using semiparametric; semiparametric linear; linear transformation; mendelian randomization ... See more keywords
Semiparametric linear transformation models for indirectly observed outcomes.
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DOI: 10.1002/sim.8903
Abstract: We propose a regression framework to analyze outcomes that are indirectly observed via one or multiple proxies. Semiparametric transformation models, including Cox proportional hazards regression, turn out to be well suited to model the association… read more here.
Keywords: indirectly observed; semiparametric linear; linear transformation; multiple proxies ... See more keywords
Individual participant data meta-analysis with mixed-effects transformation models.
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DOI: 10.1093/biostatistics/kxab045
Abstract: One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most… read more here.
Keywords: participant data; transformation models; individual participant; mixed effects ... See more keywords
Penalized estimation of semiparametric transformation models with interval-censored data and application to Alzheimer’s disease
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DOI: 10.1177/0962280219884720
Abstract: Variable selection or feature extraction is fundamental to identify important risk factors from a large number of covariates and has applications in many fields. In particular, its applications in failure time data analysis have been… read more here.
Keywords: semiparametric transformation; alzheimer disease; interval censored; censored data ... See more keywords
Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data.
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DOI: 10.5705/ss.202021.0051
Abstract: In this paper, we consider a class of partially linear transformation models with interval-censored competing risks data. Under a semiparametric generalized odds rate specification for the cause-specific cumulative incidence function, we obtain optimal estimators of… read more here.
Keywords: class partially; linear transformation; interval censored; transformation models ... See more keywords