Outcomedependent sampling (ODS) schemes are costeffective 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 casecontrol for binary outcome, casecohort for timetoevent outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006341X.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.15410420.2010.01500.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 casecohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.
Package details 


Author  Yinghao Pan [aut, cre], Haibo Zhou [aut], Mark Weaver [aut], Guoyou Qin [aut], Jianwen Cai [aut] 
Maintainer  Yinghao Pan <[email protected]> 
License  GPL (>= 2) 
Version  0.2.0 
URL  https://github.com/YinghaoPan/ODS 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.