A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

Huang, Chiung Yu; Qin, Jing; Follmann, Dean

doi:10.1093/biomet/asr072

Cited by 27 publications

(20 citation statements)

References 21 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, proper estimation methods for length-biased data should be used. 17,19,24 We demonstrated some existing methodologies using the example of CSHA cohort study.…”

Section: Discussionmentioning

confidence: 99%

Sample size calculations for prevalent cohort designs

Líu

Shen

Ning

et al. 2016

Stat Methods Med Res

View full text Add to dashboard Cite

Cross-sectional prevalent cohort design has drawn considerable interests in the studies of association between risk factors and time-to-event outcome. The sampling scheme in such design gives rise to length-biased data that require specialized analysis strategy but can improve study efficiency. The power and sample size calculation methods are however lacking for studies with prevalent cohort design, and using the formula developed for traditional survival data may overestimate sample size. We derive the sample size formulas that are appropriate for the design of cross-sectional prevalent cohort studies, under the assumptions of exponentially distributed event time and uniform follow-up for cross-sectional prevalent cohort design. We perform numerical and simulation studies to compare the sample size requirements for achieving the same power between prevalent cohort and incident cohort designs. We also use a large prospective prevalent cohort study to demonstrate the procedure. Using rigorous designs and proper analysis tools, the prospective prevalent cohort design can be more efficient than the incident cohort design with the same total sample sizes and study durations.

show abstract

“…Instead, proper estimation methods for length-biased data should be used. 17,19,24 We demonstrated some existing methodologies using the example of CSHA cohort study.…”

Section: Discussionmentioning

confidence: 99%

Sample size calculations for prevalent cohort designs

Líu

Shen

Ning

et al. 2016

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…If the underlying truncation time is uniformly distributed, left truncation reduces to length‐biased sampling (Vardi, ), that is, the probability of selecting a subject is proportional to the length of his or her underlying failure time; see a comprehensive review by Shen et al (). Among the newly developed regression methods for length‐biased data, many show considerable improvement of efficiency in estimation compared with the conditional approach by incorporating information from the observed truncation times (Qin and Shen, ; Qin et al, ; Huang et al, ; Huang and Qin, ; Ning et al, ). Nevertheless, when the uniform truncation assumption is violated, these methods may yield inconsistent estimates (Huang and Qin, ).…”

Section: Introductionmentioning

confidence: 99%

A Pairwise Likelihood Augmented Cox Estimator for Left-truncated Data

Kim

Qin

et al. 2017

Biometrics

View full text Add to dashboard Cite

Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. We propose a semiparametric method for left-truncated data under the Cox model with no parametric distributional assumption about the truncation times. Our approach is to make inference based on the conditional likelihood augmented with a pairwise likelihood, which eliminates the truncation distribution, yet retains the information about the regression coefficients and the baseline hazard function in the marginal likelihood. An iterative algorithm is provided to solve for the regression coefficients and the baseline hazard function simultaneously. By empirical process and U-process theories, it has been shown that the proposed estimator is consistent and asymptotically normal with a closed-form consistent variance estimator. Simulation studies show substantial efficiency gain of our estimator in both the regression coefficients and the cumulative baseline hazard function over the conditional approach estimator. When the uniform truncation assumption holds, our estimator enjoys smaller biases and efficiency comparable to that of the full maximum likelihood estimator. An application to the analysis of a chronic kidney disease cohort study illustrates the utility of the method.

show abstract

“…For model (1), if the cirrhosis time T itself is subject to both censoring and truncation, we can use the methods proposed in [12,22,23] and [25]. For the analysis of lengthbiased data, which may appear in observational studies where the observed samples are not randomly selected from the population of interest but with probability proportional to their length [24], recent research includes [3,15,16,18,19,28].…”

Section: Introductionmentioning

confidence: 99%

A proportional hazards model for time-to-event data with epidemiological bias

Zhang

Dai

2016

Journal of Multivariate Analysis

View full text Add to dashboard Cite

In hepatitis C virus (HCV) epidemiological studies, the estimation of progression to cirrhosis and prognostic effects of associated risk factors is of particular importance when projecting national disease burden. However, the progression estimates obtained from conventional methods could be distorted due to a referral bias [11]. In recent years, several approaches have been developed to handle this epidemiological bias in analyzing time-to-event data. This paper proposes a new estimation approach for this problem under a semiparametric proportional hazards framework. The new method uses a martingale approach based on the mean rate function, rather than the traditional hazard rate function, and develops an iterative algorithm to estimate the Cox regression parameter and baseline hazard rate simultaneously. The consistency and asymptotic properties of the proposed estimators are derived theoretically and evaluated via simulation studies. The new * Corresponding author. Email address: hdaia@essex.ac.uk (Hongsheng Dai).

show abstract

A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

Cited by 27 publications

References 21 publications

Sample size calculations for prevalent cohort designs

Sample size calculations for prevalent cohort designs

A Pairwise Likelihood Augmented Cox Estimator for Left-truncated Data

A proportional hazards model for time-to-event data with epidemiological bias

Contact Info

Product

Resources

About