×

Asymptotic behavior of the unconditional NPMLE of the length-biased survivor function from right censored prevalent cohort data. (English) Zbl 1086.62113

Summary: Right censored survival data collected on a cohort of prevalent cases with constant incidence are length-biased, and may be used to estimate the length-biased (i.e., prevalent-case) survival function. When the incidence rate is constant, so-called stationarity of the incidence, it is more efficient to use this structure for unconditional statistical inference than to carry out an analysis by conditioning on the observed truncation times. It is well known that, due to the informative censoring for prevalent cohort data, the Kaplan Meier estimator is not the unconditional NPMLE of the length-biased survival function and the asymptotic properties of the NPMLE do not follow from any known result.
We present here a detailed derivation of the asymptotic properties of the NPMLE of the length-biased survival function from right censored prevalent cohort survival data with follow-up. In particular, we show that the NPMLE is uniformly strongly consistent, converges weakly to a Gaussian process, and is asymptotically efficient. One important spin-off from these results is that they yield the asymptotic properties of the NPMLE of the incident-case survival function [see M. Asgharian, C. E. M’Lan and D. B. Wolfson J. Am. Stat. Assoc. 97, No. 457, 201–209 (2002; Zbl 1073.62561)], which is often of prime interest in a prevalent cohort study.
Our results generalize those given by Y. Vardi and C.-H. Zhang [Ann. Stat. 20, No. 2, 1022–1039 (1992; Zbl 0761.62056)] under multiplicative censoring, which we show arises as a degenerate case in a prevalent cohort setting.

MSC:

62N02 Estimation in survival analysis and censored data
62G20 Asymptotic properties of nonparametric inference
62N01 Censored data models

References:

[1] Andersen, P. K., Borgan, Ø., Gill, R. D. and Keiding, N. (1993). Statistical Models Based on Counting Processes. Springer, New York. · Zbl 0769.62061
[2] Asgharian, M., Wolfson, D. B. and Zhang, X. (2004). A simple criterion for the stationarity of the incidence rate from prevalent cohort studies. Technical Report 2004-01, Dept. Mathematics and Statistics, McGill Univ.
[3] Asgharian, M., Wolfson, D. B. and Zhang, X. (2005). Checking stationarity of the incidence rate using prevalent cohort survival data. Statistics in Medicine . · doi:10.1002/sim.2326
[4] Asgharian, M., M’Lan, C. E. and Wolfson, D. B. (2002). Length-biased sampling with right censoring: An unconditional approach. J. Amer. Statist. Assoc. 97 201–209. · Zbl 1073.62561 · doi:10.1198/016214502753479347
[5] Beran, R. (1977). Estimating a distribution function. Ann. Statist. 5 400–404. JSTOR: · Zbl 0379.62024 · doi:10.1214/aos/1176343806
[6] Bickel, P. J., Klaassen, C. A. J., Ritov, Y. and Wellner, J. A. (1993). Efficient and Adaptive Estimation for Semiparametric Models. Johns Hopkins Univ. Press, Baltimore. · Zbl 0786.62001
[7] Brillinger, D. R. (1986). The natural variability of vital rates and associated statistics (with discussion). Biometrics 42 693–734. · Zbl 0611.62136 · doi:10.2307/2530689
[8] Csörgő, M. and Révész, P. (1981). Strong Approximations in Probability and Statistics . Academic Press, New York. · Zbl 0539.60029
[9] Feller, W. (1971). An Introduction to Probability Theory and Its Applications 2 , 2nd ed. Wiley, New York. · Zbl 0219.60003
[10] Gilbert, P. B., Lele, S. R. and Vardi, Y. (1999). Maximum likelihood estimation in semiparametric selection bias models with application to AIDS vaccine trials. Biometrika 86 27–43. · Zbl 0917.62061 · doi:10.1093/biomet/86.1.27
[11] Gill, R. D., Vardi, Y. and Wellner, J. A. (1988). Large sample theory of empirical distributions in biased sampling models. Ann. Statist. 16 1069–1112. JSTOR: · Zbl 0668.62024 · doi:10.1214/aos/1176350948
[12] Groeneboom, P. and Wellner, J. A. (1992). Information Bounds and Nonparametric Maximum Likelihood Estimation. Birkhäuser, Basel. · Zbl 0757.62017
[13] Huang, Y. and Wang, M.-C. (1995). Estimating the occurrence rate for prevalent survival data in competing risks models. J. Amer. Statist. Assoc. 90 1406–1415. · Zbl 0868.62082 · doi:10.2307/2291532
[14] Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data , 2nd ed. Wiley, New York. · Zbl 1012.62104
[15] Keiding, N. (1990). Statistical inference in the Lexis diagram. Philos. Trans. Roy. Soc. London Ser. A 332 487–509. · Zbl 0714.62102 · doi:10.1098/rsta.1990.0128
[16] Keiding, N., Kvist, K., Hartvig, H., Tvede, M. and Juul, S. (2002). Estimating time to pregnancy from current durations in a cross-sectional sample. Biostatistics 3 565–578. · Zbl 1138.62353 · doi:10.1093/biostatistics/3.4.565
[17] Lexis, W. (1875). Einleitung in die Theorie der Bevölkerungsstatistik . Trübner, Strassburg. Pages 5–7 translated in (1977). Mathematical Demography (D. Smith and N. Keyfitz, eds.) 39–41. Springer, Berlin.
[18] Lund, J. (2000). Sampling bias in population studies—How to use the Lexis diagram. Scand. J. Statist. 27 589–604. · Zbl 0962.62109 · doi:10.1111/1467-9469.00210
[19] Parthasarathy, K. R. (1967). Probability Measures on Metric Spaces. Academic Press, New York. · Zbl 0153.19101
[20] van Es, B., Klaassen, C. A. J. and Oudshoorn, K. (2000). Survival analysis under cross-sectional sampling: Length bias and multiplicative censoring. J. Statist. Plann. Inference 91 295–312. · Zbl 0969.62062 · doi:10.1016/S0378-3758(00)00183-X
[21] Vardi, Y. (1982). Nonparametric estimation in the presence of length bias. Ann. Statist. 10 616–620. JSTOR: · Zbl 0491.62034 · doi:10.1214/aos/1176345802
[22] Vardi, Y. (1985). Empirical distributions in selection bias models (with discussion). Ann. Statist. 13 178–205. JSTOR: · Zbl 0578.62047 · doi:10.1214/aos/1176346585
[23] Vardi, Y. (1989). Multiplicative censoring, renewal processes, deconvolution and decreasing density: Nonparametric estimation. Biometrika 76 751–761. · Zbl 0678.62051 · doi:10.1093/biomet/76.4.751
[24] Vardi, Y. and Zhang, C.-H. (1992). Large sample study of empirical distributions in a random-multiplicative censoring model. Ann. Statist. 20 1022–1039. JSTOR: · Zbl 0761.62056 · doi:10.1214/aos/1176348668
[25] Wang, M.-C. (1991). Nonparametric estimation from cross-sectional survival data. J. Amer. Statist. Assoc. 86 130–143. · Zbl 0739.62026 · doi:10.2307/2289722
[26] Wang, M.-C., Brookmeyer, R. and Jewell, N. P. (1993). Statistical models for prevalent cohort data. Biometrics 49 1–11. · Zbl 0771.62079 · doi:10.2307/2532597
[27] Wang, M.-C., Jewell, N. P. and Tsai, W.-Y. (1986). Asymptotic properties of the product limit estimate under random truncation. Ann. Statist. 14 1597–1605. JSTOR: · Zbl 0656.62048 · doi:10.1214/aos/1176350180
[28] Wolfson, C., Wolfson, D., Asgharian, M., M’Lan, C. E., Østbye, T., Rockwood, K. and Hogan, D., for the Clinical Progression of Dementia Study Group (2001). A reevaluation of the duration of survival after the onset of dementia. New England J. Medicine 344 1111–1116.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.