This function computes the bounds on the average treatment effect among always-observed pairs (ATOP) with pre-specified sensivity parameters when some of the outcome data are missing. The sensivity parameter characterizes the degree of the within-pair similarity. The confidence intervals for the ATOP are also computed.
ATOPsens(Ya, Yb, Ra, Rb, Ta, Tb, gamma, l, u, alpha, rep)
A vector of the outcomes of the first unit in the matched pairs. The missing values for Ya
should be coded
as NA
.
A vector of the outcomes of the second unit in the matched pairs. The missing values for Yb
should be coded
as NA
.
A vector of the missing data indicators of the first unit in the matched pairs.
A vector of the missing data indicators of the second unit in the matched pairs.
A vector of the treatment conditions of the first unit in the matched pairs.
A vector of the treatment conditions of the second unit in the matched pairs.
The sensitivity parameter which charaterizes the degree of the within-pair similarity.
The lower limit of the outcome.
The upper limit of the outcome.
A positive scalar that is less than or equal to 0.5. This will
determine the (1-alpha
) level of confidence intervals. The default is
0.05
.
The number of repetitions for bootstraping.
A list of class ATOPsens
which contains the following items:
The lower bound for the ATOP.
The upper bound for the ATOP.
The lower limit of the confidence interval for the ATOP.
The upper limit of the confidence interval for the ATOP.
For the details of the method implemented by this function, see the references.
Kosuke Imai and Zhichao Jiang (2018). “A Sensitivity Analysis for Missing Outcomes Due to Truncation-by-Death under the Matched-Pairs Design”, Statistics in Medicine.
# NOT RUN {
data(seguro)
attach(seguro)
ATOPobs(Ya,Yb,Ra,Rb,Ta,Tb,gamma=0.95,kappa1=1,kappa0=1,l=0,u=1,alpha=0.05,rep=100)
# }
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