A pairwise pseudo‐likelihood approach for left‐truncated and interval‐censored data under the Cox model

Wang, Peijie; Li, Danning; Sun, Jianguo

doi:10.1111/biom.13394

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…The CTOT method cannot be directly used to analyze the qPCR absolute quantification for differential changes. However, statistical methods that deal with left-truncated or interval-censored data may be adapted or leveraged [ 12 , 81 , 82 ].…”

Section: Discussionmentioning

confidence: 99%

A robust biostatistical method leverages informative but uncertainly determined qPCR data for biomarker detection, early diagnosis, and treatment

et al. 2022

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As a common medium-throughput technique, qPCR (quantitative real-time polymerase chain reaction) is widely used to measure levels of nucleic acids. In addition to accurate and complete data, experimenters have unavoidably observed some incomplete and uncertainly determined qPCR data because of intrinsically low overall amounts of biological materials, such as nucleic acids present in biofluids. When there are samples with uncertainly determined qPCR data, some investigators apply the statistical complete-case method by excluding the subset of samples with uncertainly determined data from analysis (CO), while others simply choose not to analyze (CNA) these datasets altogether. To include as many observations as possible in analysis for interesting differential changes between groups, some investigators set incomplete observations equal to the maximum quality qPCR cycle (MC), such as 32 and 40. Although straightforward, these methods may decrease the sample size, skew the data distribution, and compromise statistical power and research reproducibility across replicate qPCR studies. To overcome the shortcomings of the existing, commonly-used qPCR data analysis methods and to join the efforts in advancing statistical analysis in rigorous preclinical research, we propose a robust nonparametric statistical cycle-to-threshold method (CTOT) to analyze incomplete qPCR data for two-group comparisons. CTOT incorporates important characteristics of qPCR data and time-to-event statistical methodology, resulting in a novel analytical method for qPCR data that is built around good quality data from all subjects, certainly determined or not. Considering the benchmark full data (BFD), we compared the abilities of CTOT, CO, MC, and CNA statistical methods to detect interesting differential changes between groups with informative but uncertainly determined qPCR data. Our simulations and applications show that CTOT improves the power of detecting and confirming differential changes in many situations over the three commonly used methods without excess type I errors. The robust nonparametric statistical method of CTOT helps leverage qPCR technology and increase the power to detect differential changes that may assist decision making with respect to biomarker detection and early diagnosis, with the goal of improving the management of patient healthcare.

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Section: Discussionmentioning

confidence: 99%

A robust biostatistical method leverages informative but uncertainly determined qPCR data for biomarker detection, early diagnosis, and treatment

et al. 2022

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“…Among others, one area that often produces such data is epidemiological studies on disease progression such as HIV and AIDS. For the situation, left truncation may often occur together too, and thus, one has to face left-truncated and doubly interval-censored data (Wang et al, 2015a(Wang et al, , 2015b(Wang et al, , 2018(Wang et al, , 2020. However, there seems to exist little literature on variable selection for these situations.…”

Section: Directions For Future Researchmentioning

confidence: 99%

Variable Selection for Interval‐censored Failure Time Data

Sun

2021

Int Statistical Rev

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Summary Variable selection for interval‐censored failure time data has recently attracted a great deal of attention along with the analysis of interval‐censored data in both method developments and practical applications. Interval‐censored data are a general type of time‐to‐event or failure time data where the failure time of interest is known or observed only to lie in an interval instead of being observed exactly. They often occur in different forms and in many fields, including demographical studies, epidemiological studies, medical or public health researches and social sciences. This paper will discuss and review the existing methods for variable selection based on interval‐censored data, a relatively new but important topic, along with some directions or issues that need more research.

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“…Inspired by the work of Huang and Qin [ 14 ], Wu et al [ 26 ] proposed a pairwise likelihood augmented estimator for the PH model with the left-truncated and right-censored data. Furthermore, Wang et al [ 27 ] considered the analysis of left-truncated and interval-censored data with the PH model, and developed a sieve maximum likelihood estimation procedure by accommodating the pairwise likelihood function of the truncation times.…”

Section: Introductionmentioning

confidence: 99%

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring

Shao

et al. 2023

BMC Med Res Methodol

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Background Failure time data frequently occur in many medical studies and often accompany with various types of censoring. In some applications, left truncation may occur and can induce biased sampling, which makes the practical data analysis become more complicated. The existing analysis methods for left-truncated data have some limitations in that they either focus only on a special type of censored data or fail to flexibly utilize the distribution information of the truncation times for inference. Therefore, it is essential to develop a reliable and efficient method for the analysis of left-truncated failure time data with various types of censoring. Method This paper concerns regression analysis of left-truncated failure time data with the proportional hazards model under various types of censoring mechanisms, including right censoring, interval censoring and a mixture of them. The proposed pairwise pseudo-likelihood estimation method is essentially built on a combination of the conditional likelihood and the pairwise likelihood that eliminates the nuisance truncation distribution function or avoids its estimation. To implement the presented method, a flexible EM algorithm is developed by utilizing the idea of self-consistent estimating equation. A main feature of the algorithm is that it involves closed-form estimators of the large-dimensional nuisance parameters and is thus computationally stable and reliable. In addition, an R package is developed. Results The numerical results obtained from extensive simulation studies suggest that the proposed pairwise pseudo-likelihood method performs reasonably well in practical situations and is obviously more efficient than the conditional likelihood approach as expected. The analysis results of the MHCPS data with the proposed pairwise pseudo-likelihood method indicate that males have significantly higher risk of losing active life than females. In contrast, the conditional likelihood method recognizes this effect as non-significant, which is because the conditional likelihood method often loses some estimation efficiency compared with the proposed method. Conclusions The proposed method provides a general and helpful tool to conduct the Cox’s regression analysis of left-truncated failure time data under various types of censoring.

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A pairwise pseudo‐likelihood approach for left‐truncated and interval‐censored data under the Cox model

Cited by 14 publications

References 25 publications

A robust biostatistical method leverages informative but uncertainly determined qPCR data for biomarker detection, early diagnosis, and treatment

A robust biostatistical method leverages informative but uncertainly determined qPCR data for biomarker detection, early diagnosis, and treatment

Variable Selection for Interval‐censored Failure Time Data

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring

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