Information bounds for Cox regression models with missing data

Details Information bounds for Cox regression models with missing data Information bounds for Cox regression models with missing data

2004-06-23

arxiv.org/abs/math/0406452v1

Annals of Statistics 2004, Vol. 32, No. 2, 723-753

Mathematics

Statistics Theory

Scientific paper

10.1214/009053604000000157

We derive information bounds for the regression parameters in Cox models when data are missing at random. These calculations are of interest for understanding the behavior of efficient estimation in case-cohort designs, a type of two-phase design often used in cohort studies. The derivations make use of key lemmas appearing in Robins, Rotnitzky and Zhao [J. Amer. Statist. Assoc. 89 (1994) 846-866] and Robins, Hsieh and Newey [J. Roy. Statist. Soc. Ser. B 57 (1995) 409-424], but in a form suited for our purposes here. We begin by summarizing the results of Robins, Rotnitzky and Zhao in a form that leads directly to the projection method which will be of use for our model of interest. We then proceed to derive new information bounds for the regression parameters of the Cox model with data Missing At Random (MAR). In the final section we exemplify our calculations with several models of interest in cohort studies, including an i.i.d. version of the classical case-cohort design of Prentice [Biometrika 73 (1986) 1-11]

Affiliated with

Also associated with

No associations

Rating

Information bounds for Cox regression models with missing data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Information bounds for Cox regression models with missing data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Information bounds for Cox regression models with missing data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-247831