ODS

Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event outcome, and continuous outcome ODS design (Zhou et al. 2002) doi:10.1111/j.0006-341X.2002.00413.x. Because ODS data has biased sampling nature, standard statistical analysis such as linear regression will lead to biases estimates of the population parameters. This package implements four statistical methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) doi:10.1111/j.1541-0420.2010.01500.x. (3) Analyze a secondary outcome in continuous ODS design (Pan et al. 2018) doi:10.1002/sim.7672. (4) An estimated likelihood method analyzing a secondary outcome in case-cohort data (Pan et al. 2017) doi:10.1111/biom.12838.

Last 30 days

This package has been downloaded 127 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.

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Last 365 days

This package has been downloaded 1,637 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 27 downloads.

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