Length-Biased Sampling With Right Censoring

Asgharian, Masoud; M'Lan, Cyr Emile; Wolfson, David B.

doi:10.1198/016214502753479347

Cited by 183 publications

(208 citation statements)

References 22 publications

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“…This assumption was crucial in the analysis car-ried out by [2] and by [14], with the purpose of estimating survival with dementia, from onset. Furthermore, the independence assumption is necessary in a general nonparametric setting if one is to avoid model non-identifiability.…”

Section: The Canadian Study Of Health and Aging (Csha)mentioning

confidence: 99%

Testing the assumptions for the analysis of survival data arising from a prevalent cohort study with follow-up

Addona¹,

Atherton²,

Wolfson³

2012

The International Journal of Biostatistics

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In a prevalent cohort study with follow-up subjects identified as prevalent cases are followed until failure (defined suitably) or censoring. When the dates of the initiating events of these prevalent cases are ascertainable, each observed datum point consists of a backward recurrence time and a possibly censored forward recurrence time. Their sum is well known to be the left truncated lifetime. It is common to term these left truncated lifetimes "length biased" if the initiating event times of all the incident cases (including those not observed through the prevalent sampling scheme) follow a stationary Poisson process. Statistical inference is then said to be carried out under stationarity. Whether or not stationarity holds, a further assumption needed for estimation of the incident survivor function is the independence of the lifetimes and their accompanying truncation times. That is, it must be assumed that survival does not depend on the calendar date of the initiating event. We show how this assumption may be checked under stationarity, even though only the backward recurrence times and their associated (possibly censored) forward recurrence times are observed. We prove that independence of the lifetimes and truncation times is equivalent to equality in distribution of the backward and forward recurrence times, and exploit this equivalence as a means of testing the former hypothesis. A simulation study is conducted to investigate the power and Type 1 error rate of our proposed tests, which include a bootstrap procedure that takes into account the pairwise dependence between the forward and backward recurrence times, as well as the potential censoring of only one of the members of each pair. We illustrate our methods using data from the Canadian Study of Health and Aging. We also point out an equivalence of the problem presented here to a non-standard changepoint problem.

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Section: The Canadian Study Of Health and Aging (Csha)mentioning

confidence: 99%

Testing the assumptions for the analysis of survival data arising from a prevalent cohort study with follow-up

Addona¹,

Atherton²,

Wolfson³

2012

The International Journal of Biostatistics

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“…Censoring applies to the residual disease duration because it is study dependent and acts exclusively upon a subject's time under study. As in Wang (1991) and Asgharian et al (2002), the residual censoring mechanism is assumed to be uninformative: specifically, (T, X − T ) and C are assumed to be independent. In many applications, censoring occurs for administrative or other reasons unrelated to participants' history; this assumption is adequate in such situations.…”

Section: ·2 Probabilistic Frameworkmentioning

confidence: 99%

“…In particular, individuals with longer disease durations are overrepresented by the sampling scheme. This bias in survival has been widely studied (Cox & Oakes, 1984, p. 179;Tsai et al, 1987;Wang, 1991;Asgharian et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric incidence estimation from prevalent cohort survival data

2012

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SUMMARYIncidence is an important epidemiological concept most suitably studied using an incident cohort study. However, data are often collected from the more feasible prevalent cohort study, whereby diseased individuals are recruited through a cross-sectional survey and followed in time. In the absence of temporal trends in survival, we derive an efficient nonparametric estimator of the cumulative incidence based on such data and study its asymptotic properties. Arbitrary calendar time variations in disease incidence are allowed. Age-specific incidence and adjustments for both stratified sampling and temporal variations in survival are also discussed. Simulation results are presented and data from the Canadian Study of Health and Aging are analysed to infer the incidence of dementia in the Canadian elderly population.

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“…It would, however, be of interest to extend the method to the general left truncation where the left truncation distribution is unknown. This latter approach is robust against departure from stationarity, though it is less efficient when the stationarity assumption holds [45,46,54].…”

Section: Discussionmentioning

confidence: 99%

“…Several methods have been proposed to adjust for the lengthbiased sampling such as the nonparametric maximum likelihood estimator [45][46][47], the truncation productlimit estimator [48] and the maximum pseudo-partial likelihood estimator [49]. Here, we use the method introduced by Huang and Qin [50] which incorporates the information from the marginal distribution of the …”

Section: Survival Curvesmentioning

confidence: 99%

Double Bias: Estimation of Causal Effects from Length-Biased Samples in the Presence of Confounding

Ertefaie

Asgharian

Stephens

2015

The International Journal of Biostatistics

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Length bias in survival data occurs in observational studies when, for example, subjects with shorter lifetimes are less likely to be present in the recorded data. In this paper, we consider estimating the causal exposure (treatment) effect on survival time from observational data when, in addition to the lack of randomization and consequent potential for confounding, the data constitute a length-biased sample; we hence term this a double-bias problem. We develop estimating equations that can be used to estimate the causal effect indexing the structural Cox proportional hazard and accelerated failure time models for point exposures in double-bias settings. The approaches rely on propensity score-based adjustments, and we demonstrate that estimation of the propensity score must be adjusted to acknowledge the length-biased sampling. Large sample properties of the estimators are established and their small sample behavior is studied using simulations. We apply the proposed methods to a set of, partly synthesized, length-biased survival data collected as part of the Canadian Study of Health and Aging (CSHA) to compare survival of subjects with dementia among institutionalized patients versus those recruited from the community and depict their adjusted survival curves.

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Length-Biased Sampling With Right Censoring

Cited by 183 publications

References 22 publications

Testing the assumptions for the analysis of survival data arising from a prevalent cohort study with follow-up

Testing the assumptions for the analysis of survival data arising from a prevalent cohort study with follow-up

Nonparametric incidence estimation from prevalent cohort survival data

Double Bias: Estimation of Causal Effects from Length-Biased Samples in the Presence of Confounding

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