casecohort_data_secondary

Data example for the secondary analysis in case-cohort design

datasets

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>.

Yinghao Pan

Statistical Methods for Outcome-Dependent Sampling Designs

Haibo Zhou

Mark Weaver

Guoyou Qin

Jianwen Cai

casecohort_data_secondary function

A data frame with 1000 rows and 15 columns:<dl class="dl-horizontal">
 <dt>subj_ind</dt><dd>An indicator variable for each subject: 1 = SRS, 2 =
 supplemental cases, 0 = NVsample</dd> <dt>T</dt><dd>observation time for failure
 outcome</dd> <dt>Delta</dt><dd>event indicator</dd> <dt>Y2</dt><dd>a continuous secondary
 outcome</dd><dt>X</dt><dd>expensive exposure</dd> <dt>Z11</dt><dd>first covariate in the
 linear regression model</dd> <dt>Z12</dt><dd>second covariate in the linear
 regression model</dd><dt>Z13</dt><dd>third covariate in the linear regression model</dd>
 <dt>Z14</dt><dd>fourth covariate in the linear regression model</dd><dt>Z21</dt><dd>first
 covariate in the Cox model</dd> <dt>Z22</dt><dd>second covariate in the Cox
 model</dd><dt>Z23</dt><dd>third covariate in the Cox model</dd> <dt>Z31</dt><dd>first
 covariate that is related to the conditional distribution of X given other
 covariates</dd><dt>Z32</dt><dd>second covariate that is related to the conditional
 distribution</dd> <dt>Z33</dt><dd>thid covariate that is related to the conditional
 distribution</dd> </dl>

Format

Data example for the secondary analysis in case-cohort design — casecohort_data_secondary

A data frame with 1000 rows and 15 columns:<dl class='dl-horizontal'>
 <dt>subj_ind</dt><dd>An indicator variable for each subject: 1 = SRS, 2 =
 supplemental cases, 0 = NVsample</dd> <dt>T</dt><dd>observation time for failure
 outcome</dd> <dt>Delta</dt><dd>event indicator</dd> <dt>Y2</dt><dd>a continuous secondary
 outcome</dd><dt>X</dt><dd>expensive exposure</dd> <dt>Z11</dt><dd>first covariate in the
 linear regression model</dd> <dt>Z12</dt><dd>second covariate in the linear
 regression model</dd><dt>Z13</dt><dd>third covariate in the linear regression model</dd>
 <dt>Z14</dt><dd>fourth covariate in the linear regression model</dd><dt>Z21</dt><dd>first
 covariate in the Cox model</dd> <dt>Z22</dt><dd>second covariate in the Cox
 model</dd><dt>Z23</dt><dd>third covariate in the Cox model</dd> <dt>Z31</dt><dd>first
 covariate that is related to the conditional distribution of X given other
 covariates</dd><dt>Z32</dt><dd>second covariate that is related to the conditional
 distribution</dd> <dt>Z33</dt><dd>thid covariate that is related to the conditional
 distribution</dd> </dl>

casecohort_data_secondary: Data example for the secondary analysis in case-cohort design

Description

Usage

Arguments

Format