Evidence map›Paper›PMID 18266893›Full record

ArticleBiometrics2008

Comparison of properties of tests for assessing tumor clonality.

Irina Ostrovnaya, Venkatraman E Seshan, Colin B Begg

Open access · greenAbstract readCommentComparative Study
In one paragraph

Article in Biometrics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.6field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Testing clonality of three and more tumors using their loss of heterozygosity profiles.Statistical applications in genetics and molecular biology · 2012
    Review
  6. Article
  7. Article
  8. Testing clonal relatedness of tumors using array comparative genomic hybridization: a statistical challenge.Clinical cancer research : an official journal of the American Association for Cancer Research · 2010
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Irina OstrovnayaDepartment of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. ostrovni@mskcc.org
Venkatraman E Seshan
Colin B Begg
Memorial Sloan Kettering Cancer Center · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Statistical Methods for Identifying Clonal TumorsR01CA124504 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI BEGG, COLIN B · 2009 to 2011
$1.1M
Epidemiologic Parameters of Rare Cancer Risk FactorsR01CA098438 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI BEGG, COLIN B · 2003 to 2005
$839k
NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA098438NCI NIH HHS R01 CA124504
6 · The paper itself

Abstract

summaryIn a recent article Begg et al. (2007, Biometrics 63, 522-530) proposed a statistical test to determine whether or not a diagnosed second primary tumor is biologically independent of the original primary tumor, by comparing patterns of allelic losses at candidate genetic loci. The proposed concordant mutations test is a conditional test, an adaptation of Fisher's exact test, that requires no knowledge of the marginal mutation probabilities. The test was shown to have generally good properties, but is susceptible to anticonservative bias if there is wide variation in mutation probabilities between loci, or if the individual mutation probabilities of the parental alleles for individual patients differ substantially from each other. In this article, a likelihood ratio test is derived in an effort to address these validity issues. This test requires prespecification of the marginal mutation probabilities at each locus, parameters for which some information will typically be available in the literature. In simulations this test is shown to be valid, but to be considerably less efficient than the concordant mutations test for sample sizes (numbers of informative loci) typical of this problem. Much of the efficiency deficit can be recovered, however, by restricting the allelic imbalance parameter estimate to a prespecified range, assuming that this parameter is in the prespecified range.

Indexed as

BiometryClone CellsHumansLikelihood FunctionsLoss of HeterozygosityMutationNeoplasms, Second Primary

Identifiers

PMID18266893
PMCPMC2761024
OpenAlexW2155573999

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.