Predicting time to ovarian carcinoma recurrence using protein markers

Original citation: J Clin Invest. 2010;123(9):3740–3750. doi:10.1172/JCI68509.

Citation for this erratum: J Clin Invest. 2013;123(12):5410. doi:10.1172/JCI74035.

Some expressions and notations related to Equations 1 and 2 were presented incorrectly. The correct text and equations are below.

The coefficients (β) in Cox’s regression model are estimated by maximizing the partial likelihood function subject to a constraint on the L1-norm of the coefficients. The lasso estimator (β̂) maximizes the objective function given below:

(Equation 1)

Here l(β) is the log partial likelihood in the Cox model; for the exact form of this function, see ref. 41. The tuning parameter, λ in Equation 1, was chosen by 10-fold cross-validation. For the implementation, we used the R package “glmnet” (39).

PROVAR was defined for each of the 222 TCGA samples as the sum of the estimated coefficients multiplied by protein expression levels, as shown below. Here i represents patients (i = 1, ..., 222), j represents proteins with nonzero coefficients (j = 1, ..., m), β̂_j is the lasso coefficient of the jth protein marker, and X_ij is the expression level of the jth protein for the ith patient.

(Equation 2)

The JCI regrets the error.