Two-phase cohort sampling designs, or sometimes known as retrospective
sampling designs, are often used in large cohort studies for saving sampling time
and cost. Commonly used designs include case–cohort design, case–control design,
nested case–control design, and so on. Efforts had been taken to improve the estimation
efficiency under these commonly used designs. We propose a different retrospective
sampling design, called end-point design, under the class of semiparametric trans-
formation models. An inverse probability weighting likelihood approach is designed
for estimating the model parameters, and the proposed design shows higher efficiency
than the case–cohort and case–control design with comparable size of covariates ascer-
tainment. We also consider variable selection under the proposed design. A specially
designed objective function with adaptive lasso penalty is proposed. The large sample
properties of the proposed estimation and variable selection procedure are developed.
Extensive simulation studies are carried out to show favorable evidence for the pro-
posed approaches. A real data set is analyzed for illustration.

• Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design