Effect of length sampling bias on labeled mitotic index waves

Schotz, W.E.; Zelen, Marvin

doi:10.1016/0022-5193(71)90175-5

Cited by 18 publications

(6 citation statements)

References 8 publications

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“…where C V Y denotes the coefficient of variation (relative standard deviation) of Y , as shown also in Schotz and Zelen [14]. In considering the benefit of screening, the primary interest is in the effect of the LBS phenomenon on the clinical duration, Z , which, unlike Y , is observable.…”

Section: Single Sampling Timementioning

confidence: 99%

“…Recall that, for a case that arose in (0, ] and was screen detected at screen k, 0<X < and X +Y >k ⇒ Y >(k −1) . Equation (14) shows that the density of Y * (k) can be expressed as a product of y (that depends on k) and the underlying density…”

Section: Periodic Screeningmentioning

confidence: 99%

“…The general case of size-biased sampling arises when the measurement process favors those experimental units having larger observed values. This phenomenon arises, for example, in surveys of hospital patients (patients whose visits are longer have a greater likelihood of being sampled than those with shorter visits [11]); product reliability studies using a warranty database (units in the database or in service for a longer time are more likely to be part of the study [12]); particle densities in a compound (larger particles are more likely to be presented for measurement; [13]) and measurement of cell cycle durations [14]. In these cases, the density of the size-biased sample is proportional to both the underlying density and to its size.…”

Section: Introductionmentioning

confidence: 99%

“…area or volume) sampled univariate density. In a relevant article, Schotz and Zelen [14] derive joint probability density function of the durations of the four phases in the life cycle of a proliferating cell, where cells can be labeled only during their second phases (whose durations cannot be measured) and can be observed only during their final phases. Their results apply to our situation when only one screen is offered; below we generalize to the situation involving multiple screens.…”

Section: Introductionmentioning

confidence: 99%

“…Apart from Schotz and Zelen [14], most of the literature that discusses LBS does not apply to the disease screening context, because the length-biased sampled variable in a screening trial (i.e. the sojourn time) cannot be observed: the start of the preclinical duration is unobservable (except theoretically, if screening was conducted continuously, which is practically infeasible).…”

Section: Introductionmentioning

confidence: 99%

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Effect of length biased sampling of unobserved sojourn times on the survival distribution when disease is screen detected

Kafadar

Prorok

2009

Statistics in Medicine

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Data can arise as a length-biased sample rather than as a random sample; e.g. a sample of patients in hospitals or of network cable lines (experimental units with longer stays or longer lines have greater likelihoods of being sampled). The distribution arising from a single length-biased sampling (LBS) time has been derived (e.g. (The Statistical Analysis of Discrete Time Events. Oxford Press: London, 1972)) and applies when the observed outcome relates to the random variable subjected to LBS. Zelen (Breast Cancer: Trends in Research and Treatment. Raven Press: New York, 1976; 287-301) noted that cases of disease detected from a screening program likewise form a length-biased sample among all cases, since longer sojourn times afford greater likelihoods of being screen detected. In contrast to the samples on hospital stays and cable lines, however, the length-biased sojourns (preclinical durations) cannot be observed, although their subsequent clinical durations (survival times) are. This article quantifies the effect of LBS of the sojourn times (or pre-clinical durations) on the distribution of the observed clinical durations when cases undergo periodic screening for the early detection of disease. We show that, when preclinical and clinical durations are positively correlated, the mean, median, and quartiles of the distribution of the clinical duration from screen-detected cases can be substantially inflated-even in the absence of any benefit on survival from the screening procedure. Screening studies that report mean survival time need to take account of the fact that, even in the absence of any real benefit, the mean survival among cases in the screen-detected group will be longer than that among interval cases or among cases that arise in the control arm, above and beyond lead time bias, simply by virtue of the LBS phenomenon

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Section: Single Sampling Timementioning

confidence: 99%

Section: Periodic Screeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effect of length biased sampling of unobserved sojourn times on the survival distribution when disease is screen detected

Kafadar

Prorok

2009

Statistics in Medicine

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Quantification of length‐bias in screening trials with covariate‐dependent test sensitivity

2015

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Length-biased sampling exists in screening programs where longer duration disease is detected during the preclinical stage because a longer sojourn time (preclinical duration) has a higher probability of being screen detected. By modeling the course of disease, we quantify the effect of length-biased sampling on clinical duration when cases are subject to periodic screening with variable test sensitivity. We use the highly flexible bivariate lognormal density to jointly model preclinical and clinical durations, and we model screening test sensitivity as a function of the sojourn time and number of previous false negative screens. We show that the mean clinical duration among screen-detected cases can be up to 40% higher, with shrinking standard deviation, than those among nonscreen-detected cases, due to biased sampling alone, irrespective of any possible benefit (increased survival time arising from earlier detection or reduction in mortality). These findings will aid in the design and interpretation of screening trials.

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Computational methods in medical decision making: to screen or not to screen?

Kafadar

Prorok

2005

Statistics in Medicine

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Screening for a disease such as cancer is often regarded as a beneficial and successful strategy for reducing mortality. However, as with any clinical treatment or intervention, benefit cannot be assumed, and screening can entail both costs and harms, so the screening as a 'treatment' must undergo evaluation. An evaluation requires a definition of the treatment 'benefit', design of studies to measure that benefit with as little bias and variance as possible, and the development of methods for estimating the potential benefit. In screening studies, the factors most central to the evaluation are unobservable (e.g. earliest point in time at which disease becomes detectable, or 'preclinical'; time at which disease might have been detected in the absence of screening; test sensitivity). Thus, screening programs should be evaluated on scenarios in which these factors are varied, to ensure the robustness of the estimated benefit under a variety of circumstances. This article describes the importance of computational methods and simulations to assess the benefit of screening programs, particularly for cancer, based on randomized screening trials, with special attention to benefit time, lead time, and bias due to length-biased sampling.

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Effect of length sampling bias on labeled mitotic index waves

Cited by 18 publications

References 8 publications

Effect of length biased sampling of unobserved sojourn times on the survival distribution when disease is screen detected

Effect of length biased sampling of unobserved sojourn times on the survival distribution when disease is screen detected

Quantification of length‐bias in screening trials with covariate‐dependent test sensitivity

Computational methods in medical decision making: to screen or not to screen?

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