Referent sampling, family history and relative risk: the role of length-biased sampling

Davidov, Ori; Zelen, Marvin

doi:10.1093/biostatistics/2.2.173

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…Retrospective studies may yield overstated effect estimates for family size; in these studies, the probability of recruiting a patient is positively associated with family size. Because the probability of having an affected family member also depends on the number of relatives a patient has, the proportion of patients with affected relatives can be higher than the proportion of controls with affected relatives even in the absence of familial clustering of prostate carcinoma 62–64. In prospective studies, however, this “length bias sampling” does not occur, as cases originate from a predefined cohort that is not dependent on family size.…”

Section: Discussionmentioning

confidence: 99%

Empiric risk of prostate carcinoma for relatives of patients with prostate carcinoma

Zeegers¹,

Jellema²,

Ostrer³

2003

Cancer

179

108

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BACKGROUND Although narrative reviews have concluded that there is strong support for familial clustering of prostate carcinoma, the association has never systematically been quantified in reviews. The purpose of this meta–analysis was to summarize and quantify the recurrence risk ratio with emphasis on the degree of relation, the specific relationship of the family member, the number of affected family members, and the age at diagnosis. METHODS Publications were identified through computerized database searches for epidemiologic studies published up to December 2002. In addition, references cited in original and review papers were examined. Three blinded reviewers extracted both qualitative and quantitative information from each paper. Using random effects meta–regression analyses, the authors calculated summary recurrence risk ratios (Sλ). The reviewers also evaluated changes in Sλ according to differences in study methodology. RESULTS Thirty–three epidemiologic studies were included in the current review. Sλ was 2.53 (95% confidence interval, 2.24–2.85) for first–degree family members. Sλ appeared to be greater for men with an affected brother than for men with an affected father. Sλ for men who had second–degree relatives with prostate carcinoma was only slightly elevated. The nature of the familial clustering is such that Sλ increases with decreasing age of the patient and family members, with increasing genetic relatedness of the affected relative, and with increasing number of individuals affected within a family. CONCLUSIONS The studies that were reviewed consistently demonstrate that family history is a significant risk factor for development of prostate carcinoma. Cancer 2003;97:1894–903. © 2003 American Cancer Society. DOI 10.1002/cncr.11262

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Section: Discussionmentioning

confidence: 99%

Empiric risk of prostate carcinoma for relatives of patients with prostate carcinoma

Zeegers¹,

Jellema²,

Ostrer³

2003

Cancer

179

108

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“…Because of lead‐time bias in cancer‐screening trials, a tumor may be detected according to its individual size (Ghosh, 2008). More examples of size‐biased sampling have been observed in industrial fiber testing (Cox, 1969), family studies of rare genetic diseases (Patil and Rao, 1978; Davidov and Zelen; 2001), etiological studies (Simon, 1980), and chronic/early disease modeling (Zelen, 2005).…”

Section: Introductionmentioning

confidence: 99%

Semiparametric Regression in Size‐Biased Sampling

Chen¹

2010

Biometrics

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SummarySize-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of the covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that the regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.

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“…For example, in case-control studies for assessing the role of FH, one assembles a registry by collecting FHs from individuals diagnosed with the disease and then compares them with a control group, whose FHs are also collected [16]. This registry is not a random sample of families or individuals from the underlying population, since families with multiple cases could be overrepresented (ascertainment bias).…”

Section: Discussionmentioning

confidence: 99%

A statistical comparison of different family history scores

Murad

Kalter‐Leibovici

Chetrit

et al. 2006

Statistics in Medicine

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Family history (FH) scores are used for estimating the familial risk (FR), i.e. the level of risk for a particular disease among members of that family. An FH score is created from reports about the disease status of the relatives in each family. The most commonly used score is the dichotomous score (positive when at least one relative is affected), which does not consider the family size, number of affected relatives nor each relative's risk factor profile. Authors have proposed many other FH scores that overcome these deficiencies by using external expected risks adjusted for important risk factors. We consider the use of FH scores in studies, which investigate risk factors for a disease and where family risk is considered as a confounder, and examine through simulations the performance of a variety of FH scores in controlling the FR status. We also examine performance in predicting true FR status. For both criteria, only small differences were found between most of the FH scores, although the dichotomous score performed the poorest. Since the proportion score (the proportion of first-degree relatives of the index who have the disease) is the simplest to calculate, use of this score seems to be justified.

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Referent sampling, family history and relative risk: the role of length-biased sampling

Cited by 16 publications

References 12 publications

Empiric risk of prostate carcinoma for relatives of patients with prostate carcinoma

Empiric risk of prostate carcinoma for relatives of patients with prostate carcinoma

Semiparametric Regression in Size‐Biased Sampling

A statistical comparison of different family history scores

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