A note on pseudo‐observations and left‐truncation

Grand, Mia Klinten; Putter, Hein; Allignol, Arthur; Andersen, Per Kragh

doi:10.1002/bimj.201700274

Cited by 11 publications

(16 citation statements)

References 21 publications

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“…In the simulations, considerable bias is observed in estimating the intercept in some cases, likely due to the presence of extremely small negative-valued POs that behave as outliers in the subsequent GEE analysis. Similar problems were observed in the simulations in Anderson et al 3 and Grand et al 22 The bias problem may be due to the low precision of product-limit estimator at the tail. Moreover, there can be very few subjects at risk at the beginning under left truncation.…”

Section: Discussionsupporting

confidence: 72%

“…Lastly, we establish the asymptotic properties of proposed estimators, whereas important theoretical justification is often lacking in many existing work using the POs. 3,18,22 F I G U R E 4 Estimated RMST by the combination of receipt of chemotherapy, ER/PR status, and age at diagnosis, using the nonparametric method, multivariable regression model of RMST with the linear link, multivariable regression model of RMST with the log link, and integrated multivariable Cox model survival curve. n is the number of patients in each combination…”

Section: Discussionmentioning

confidence: 99%

“…3,19 The PO approach has also been used in regression modeling of competing risks data 20,21 and the Cox regression analysis of left-truncated right-censored data. 22 In this article, we propose to extend the PO approach in Andersen et al 3 to left truncated right-censored data and directly model RMST as a function of baseline covariates based on POs under general censoring mechanisms. The PO approach has the advantage of handling complex issues related to left truncation and right censoring in the first step of generating POs and then using POs as responses in a generalized linear model for uncensored data.…”

Section: Introductionmentioning

confidence: 99%

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Regression modeling of restricted mean survival time for left‐truncated right‐censored data

Rong

Ning

Zhu

2022

Statistics in Medicine

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The restricted mean survival time (RMST) is a clinically meaningful summary measure in studies with survival outcomes. Statistical methods have been developed for regression analysis of RMST to investigate impacts of covariates on RMST, which is a useful alternative to the Cox regression analysis. However, existing methods for regression modeling of RMST are not applicable to left-truncated right-censored data that arise frequently in prevalent cohort studies, for which the sampling bias due to left truncation and informative censoring induced by the prevalent sampling scheme must be properly addressed.The pseudo-observation (PO) approach has been used in regression modeling of RMST for right-censored data and competing-risks data. For left-truncated right-censored data, we propose to directly model RMST as a function of baseline covariates based on POs under general censoring mechanisms. We adjust for the potential covariate-dependent censoring or dependent censoring by the inverse probability of censoring weighting method. We establish large sample properties of the proposed estimators and assess their finite sample performances by simulation studies under various scenarios. We apply the proposed methods to a prevalent cohort of women diagnosed with stage IV breast cancer identified from surveillance, epidemiology, and end results-medicare linked database. K E Y W O R D Sgeneral censoring mechanisms, inverse probability of censoring weighting, left-truncated right-censored data, pseudo-observations, restricted mean survival time 1

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Section: Discussionsupporting

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regression modeling of restricted mean survival time for left‐truncated right‐censored data

Rong

Ning

Zhu

2022

Statistics in Medicine

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“…To model the cumulative probability of a first live birth, we used the pseudo-observations approach with delayed entry 20 and with death as a competing event. 21 The relative probability of a first live birth at ages 25, 30, 35, 40, and 45 years was estimated as risk ratios (RRs) between the cumulative probability in survivors and population comparisons.…”

Section: Discussionmentioning

confidence: 99%

Temporal changes in the probability of live birth among female survivors of childhood cancer: A population‐based Adult Life After Childhood Cancer in Scandinavia (ALiCCS) study in five nordic countries

Licht

Rugbjerg

Andersen

et al. 2021

Cancer

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and the Adult Life After Childhood Cancer in Scandinavia Study Group BACKGROUND: During the past 4 decades, there has been a growing focus on preserving the fertility of patients with childhood cancer; however, no large studies have been conducted of live births across treatment decades during this period. Therefore, the authors estimated the potential birth deficit in female childhood cancer survivors and the probability of live births. METHODS: In total, 8886 women were identified in the 5 Nordic cancer registries in whom a childhood cancer had been diagnosed during 1954 through 2006. A population comparison cohort of 62,903 women was randomly selected from the central population registries matched by age and country. All women were followed for live births recorded in medical birth registries. The cumulative probability and the risk ratio (RR) with 95% confidence intervals (CIs) of a live birth were calculated by maternal age across treatment decades. RESULTS: The probability of a live birth increased with treatment decade, and, at age 30 years, the rate for survivors most recently diagnosed was close to the rate among the general population

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“…Of note, this relates to the conditional partial or temporary life expectancy in demography as proposed by [7]. The pseudo-value approach can be extended to scenarios with delayed entry (left truncation), see Grand and Putter [8] and Grand et al [9].…”

Section: Methodsmentioning

confidence: 99%

A Note on the Measurement of Socioeconomic Inequalities in Life Years Lost by Cause of Death

Latouche¹,

Andersen²,

Rey³

et al. 2019

Epidemiology

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Quantifying socioeconomic inequalities in health in absolute terms is of prime interest for decision-making and for international comparisons. The Slope Index of Inequality (SII), an index that quantifies absolute socioeconomic inequalities, was recently formalized, particularly in the context of mortality differences measured in the rate or hazard scale. However, absolute inequalities using either rates or hazards do not translate into a time dimension, which makes their interpretation difficult for policy makers. We propose an extension of the SII in terms of the expected number of life years lost before an upper age, as well as its decomposition by cause of death. The SII in the life years lost metric quantifies the extent to which life expectancy is shortened when comparing the higher and lower ends of the socioeconomic scale. The methodology proposed builds on recent developments in survival analysis for decomposing the number of life years lost according to cause of death using a pseudo-value approach. We illustrate our proposal using a representative 1% sample of the French population. The SII in the life years lost metric is easily understood, and the decomposition of the all-cause mortality SII into parts attributable to given causes provides a sound estimation of the burden of different causes of death on absolute socioeconomic inequalities in mortality.

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A note on pseudo‐observations and left‐truncation

Cited by 11 publications

References 21 publications

Regression modeling of restricted mean survival time for left‐truncated right‐censored data

Regression modeling of restricted mean survival time for left‐truncated right‐censored data

Temporal changes in the probability of live birth among female survivors of childhood cancer: A population‐based Adult Life After Childhood Cancer in Scandinavia (ALiCCS) study in five nordic countries

A Note on the Measurement of Socioeconomic Inequalities in Life Years Lost by Cause of Death

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