# Outcome-Dependent Sampling with Interval-Censored Failure Time Data Provides an outcome-dependent sampling (ODS) design with interval-censored failure time data, where the observed sample is enriched by selectively including certain more informative failure subjects. The method is a sieve semiparametric maximum empirical likelihood approach for fitting the proportional hazards model to data from the interval- censoring ODS design. ```r ODSDesignIC(U, V, del1, del2, z, mVal, ind, a1, a2, beta = NULL, maxit = 10L, verbose = TRUE, ...) ``` ## Arguments - `U`: numeric vector (n); examination time. See Details for further information. - `V`: numeric vector (n); examination time. See Details for further information. - `del1`: integer vector (n); indicator of a left-censored observation I(T<=U). See Details for further information. - `del2`: integer vector (n); indicator of an interval-censored observation I(U TK); therefore, define U = 0, V = TK, and (del1,del2)=(0,0). For interval-censored data, the failure occurred between two follow-up examinations, e.g. T2 < TF < T3; therefore, define U and V to be the two consecutive follow-up examination times bracketing the failure time TF and (del1,del2)=(0,1). ## Examples ```r data(odsData) result <- ODSDesignIC(U = odsData$U, V = odsData$V, del1 = odsData$del1, del2 = odsData$del2, z = odsData$z, mVal = 1L, ind = odsData$ind, a1 = 0.43, a2 = 0.45, beta = NULL, maxit = 10L, verbose = TRUE) print(result) mVal(result) estimate(result) optimObj(result) minAIC(result) summary(result) ``` ## References Zhou, Q., Cai, J., and Zhou, H. (2018). Outcome-dependent sampling with interval-censored failure time data. Biometrics, 74(1): 58--67.

ODSDesignIC function