Literature DB >> 12663557

Group sequential methods and sample size savings in biomarker-disease association studies.

R Aplenc1, H Zhao, T R Rebbeck, K J Propert.   

Abstract

Molecular epidemiological association studies use valuable biosamples and incur costs. Statistical methods for early genotyping termination may conserve biosamples and costs. Group sequential methods (GSM) allow early termination of studies on the basis of interim comparisons. Simulation studies evaluated the application of GSM using data from a case-control study of GST genotypes and prostate cancer. Group sequential boundaries (GSB) were defined in the EAST-2000 software and were evaluated for study termination when early evidence suggested that the null hypothesis of no association between genotype and disease was unlikely to be rejected. Early termination of GSTM1 genotyping, which demonstrated no association with prostate cancer, occurred in >90% of the simulated studies. On average, 36.4% of biosamples were saved from unnecessary genotyping. In contrast, for GSTT1, which demonstrated a positive association, inappropriate termination occurred in only 6.6%. GSM may provide significant cost and sample savings in molecular epidemiology studies.

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Year:  2003        PMID: 12663557      PMCID: PMC1462487     

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  10 in total

1.  Sequential analysis of matched dichotomous data from prospective case-control studies.

Authors:  I Van der Tweel; P A van Noord
Journal:  Stat Med       Date:  2000-12-30       Impact factor: 2.373

Review 2.  A unified theory for sequential clinical trials.

Authors:  J Whitehead
Journal:  Stat Med       Date:  1999 Sep 15-30       Impact factor: 2.373

3.  A simple algorithm for designing group sequential clinical trials.

Authors:  D A Schoenfeld
Journal:  Biometrics       Date:  2001-09       Impact factor: 2.571

4.  Glutathione S-transferase-mu (GSTM1) and -theta (GSTT1) genotypes in the etiology of prostate cancer.

Authors:  T R Rebbeck; A H Walker; J M Jaffe; D L White; A J Wein; S B Malkowicz
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  1999-04       Impact factor: 4.254

5.  Efficient use of biological banks for biochemical epidemiology: exploratory hypothesis testing by means of a sequential t-test.

Authors:  R Kaaks; I van der Tweel; P A van Noord; E Riboli
Journal:  Epidemiology       Date:  1994-07       Impact factor: 4.822

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Authors:  S J Pocock
Journal:  Biometrics       Date:  1982-03       Impact factor: 2.571

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Authors:  B S Pasternak; R E Shore
Journal:  J Chronic Dis       Date:  1980

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Authors:  R T O'Neill; C Anello
Journal:  Am J Epidemiol       Date:  1978-11       Impact factor: 4.897

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Journal:  Biometrics       Date:  1979-09       Impact factor: 2.571

10.  Two-stage designs for gene-disease association studies.

Authors:  Jaya M Satagopan; David A Verbel; E S Venkatraman; Kenneth E Offit; Colin B Begg
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

  10 in total
  1 in total

1.  A network of dopaminergic gene variations implicated as risk factors for schizophrenia.

Authors:  Michael E Talkowski; George Kirov; Mikhil Bamne; Lyudmila Georgieva; Gonzalo Torres; Hader Mansour; Kodavali V Chowdari; Vihra Milanova; Joel Wood; Lora McClain; Konasale Prasad; Brian Shirts; Jianping Zhang; Michael C O'Donovan; Michael J Owen; Bernie Devlin; Vishwajit L Nimgaonkar
Journal:  Hum Mol Genet       Date:  2007-11-27       Impact factor: 6.150

  1 in total

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