Robust statistical methods for analysis of biomarkers measured with batch/experiment-specific errors

Statistics in Medicine

Volumn 29, Issue 3, 2010, Pages 361-370

  Long, Qi ^a,b   Flanders, W Dana ^a,b   Fedirko, Veronika ^a,b   Bostick, Roberd M ^a,b  

^a EMORY UNIVERSITY   (United States)

^b EMORY UNIVERSITY   (United States)

Author keywords

Batch effect; Batch experiment specific error; Measurement error; ROC analysis; Surrogate variable

Indexed keywords

BIOLOGICAL MARKER; PROTEIN BAX; TRANSFORMING GROWTH FACTOR ALPHA;

ACCURACY; ARTICLE; CANCER RISK; CASE CONTROL STUDY; COLORECTAL TUMOR; COMPUTER PROGRAM; CONTROLLED STUDY; MEASUREMENT ERROR; OUTCOME VARIABLE; PREDICTION; SAMPLE SIZE; SIMULATION; STATISTICAL ANALYSIS;

BIAS (EPIDEMIOLOGY); BIOLOGICAL MARKERS; CASE-CONTROL STUDIES; COLORECTAL NEOPLASMS; COMPUTER SIMULATION; HUMANS; MATHEMATICAL COMPUTING; MODELS, STATISTICAL; PILOT PROJECTS; RESEARCH DESIGN;

EID: 74749107322     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3796     Document Type: Article

References (24)

1. Blanck HM, Bowman BA, Cooper GR, Myers GL, Miller DT. Laboratory issues: use of nutritional biomarkers. The Journal of Nutrition 2003; 133(Suppl 3):888S-894S.
2. Rundle AG, Vineis P, Ahsan H. Design options for molecular epidemiology research within cohort studies. Cancer Epidemiology Biomarkers and Prevention 2005; 14:1899-1907.
3. Tworoger SS, Yasui Y, Chang L, Stanczyk FZ, McTiernan A. Specimen allocation in longitudinal biomarker studies: controlling subject-specific effects by design. Cancer Epidemiology Biomarkers and Prevention 2004; 13:1257-1260.
4. Wang Y, Jacobs EJ, McCullough ML, Rodriguez C, Thun MJ, Calle EE, Flanders WD. Comparing methods for accounting for seasonal variability in a biomarker when only a single sample is available: insights from simulations based on serum 25-hydroxyvitamin D. American Journal of Epidemiology 2009; 170:88-94.
5. Ye H, Yu T, Temam S, Ziober BL, Wang J, Schwartz JL, Mao L, Wong DT, Zhou X. Transcriptomic dissection of tongue squamous cell carcinoma. BMC Genomics 2008; 9:69.
6. Yu T, Ye H, Chen Z, Ziober BL, Zhou X. Dimension reduction and mixed-effects model for microarray meta-analysis of cancer. Frontiers in Bioscience 2008; 13:2714-2720.
7. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement Error in Nonlinear Models: A Modern Perspective (2nd edn). Chapman & Hall, CRC: New York, 2006.
8. Stefanski LA, Carroll RJ. Conditional scores and optimal scores in generalized linear measurement error models. Biometrika 1987; 74:703-716.
9. Nakaruma T. Corrected score functions for errors-in-variables models: methodology and application to generalized linear models. Biometrika 1990; 77:127-137.
10. Stefanski LA. Unbiased estimation of a nonlinear function of a normal mean with application to measurement error models. Communications in Statistics - Theory and Methods 1989; 18:4335-4358.
11. Stefanski LA, Buzas JS. Instrumental variable estimation in binary regression measurement error models. Journal of the American Statistical Association 1995; 90:541-550.
12. Cook JR, Stefanski LA. Simulation-extrapolation estimation in parametric measurement error models. Journal of the American Statistical Association 1994; 89:1314-1328.
13. Bamber D. The area above the ordinal dominance gragh and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology 1975; 12:387-415.
14. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press: Oxford, U.K., 2003.
15. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44:837-845.
16. Obuchowski NA. Nonparametric analysis of clustered ROC curve data. Biometrics 1997; 53:567-578.
17. Beam CA. Analysis of clustered data in receiver operating characteristic studies. Statistical Methods in Medical Research 1998; 7:324-336.
18. Prentice RL, Pyke R. Logistic disease incidence models and case-control studies. Biometrika 1979; 66:403-411.
19. Ahearn T, Dash C, Bostick R. Associations of calcium and vitamin D with E-cadherin and β-catenin expression in normal-appearing rectal tissue; markers of adenomatous polyp II (MAPII) case-control study. Proceedings of the AACR 2008; 49:133.
20. Daniel CR, Bostick RM, Flanders WD, Long Q, Fedirko V, Sidelnikov E, Seabrook ME. TGF-α expression as a potential biomarker of risk within the normal-appearing colorectal mucosa of patients with and without incident sporadic adenoma. Cancer Epidemiology Biomarkers and Prevention 2009; 18(1):65-73.
21. Fedirko V, Bostick RM, Flanders WD, Long Q, Shaukat A, Rutherford RE, Daniel CR, Cohen V, Dash C, Woodard JJ. Effects of vitamin D and calcium supplementation on markers of apoptosis in normal colon mucosa: a randomized, double-blind, placebo-controlled clinical trial. Cancer Prevention Research 2009; 2(3):213-223.
22. Maritz JS, Jarrett RG. A note on estimating the variance of the sample median. Journal of the American Statistical Association 1978; 73(361):194-196.
23. Sukhatme S, Beam CA. Stratification in nonparametric ROC studies. Biometrics 1994; 50:149-163.
24. Hoeffding W. A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics 1978; 19:293-325.