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Published in: Trials 1/2015

Open Access 01-12-2015 | Methodology

An internal pilot design for prospective cancer screening trials with unknown disease prevalence

Authors: John T. Brinton, Brandy M. Ringham, Deborah H. Glueck

Published in: Trials | Issue 1/2015

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Abstract

Background

For studies that compare the diagnostic accuracy of two screening tests, the sample size depends on the prevalence of disease in the study population, and on the variance of the outcome. Both parameters may be unknown during the design stage, which makes finding an accurate sample size difficult.

Methods

To solve this problem, we propose adapting an internal pilot design. In this adapted design, researchers will accrue some percentage of the planned sample size, then estimate both the disease prevalence and the variances of the screening tests. The updated estimates of the disease prevalence and variance are used to conduct a more accurate power and sample size calculation.

Results

We demonstrate that in large samples, the adapted internal pilot design produces no Type I inflation. For small samples (N less than 50), we introduce a novel adjustment of the critical value to control the Type I error rate. We apply the method to two proposed prospective cancer screening studies: 1) a small oral cancer screening study in individuals with Fanconi anemia and 2) a large oral cancer screening trial.

Conclusion

Conducting an internal pilot study without adjusting the critical value can cause Type I error rate inflation in small samples, but not in large samples. An internal pilot approach usually achieves goal power and, for most studies with sample size greater than 50, requires no Type I error correction. Further, we have provided a flexible and accurate approach to bound Type I error below a goal level for studies with small sample size.
Literature
1.
go back to reference Lingen MW. Efficacy of oral cancer screening adjunctive techniques. Bethesda (MD): National Institute of Dental and Craniofacial Research, National Institutes of Health, US Department of Health and Human Services (NIH Project Number: 1RC2DE020779-01); 2009. Lingen MW. Efficacy of oral cancer screening adjunctive techniques. Bethesda (MD): National Institute of Dental and Craniofacial Research, National Institutes of Health, US Department of Health and Human Services (NIH Project Number: 1RC2DE020779-01); 2009.
2.
go back to reference Berg W, Zhang Z, Lehrer D, Jong R, Pisano E, Barr R, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA. 2012;307(13):1394–404.CrossRefPubMedPubMedCentral Berg W, Zhang Z, Lehrer D, Jong R, Pisano E, Barr R, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA. 2012;307(13):1394–404.CrossRefPubMedPubMedCentral
3.
go back to reference Lewin JM, Hendrick RE, D’Orsi CJ, Isaacs PK, Moss LJ, Karellas A, et al. Comparison of full-field digital mammography with screen-film mammography for cancer detection: results of 4,945 paired examinations. Radiology. 2001;218(3):873–80.CrossRefPubMed Lewin JM, Hendrick RE, D’Orsi CJ, Isaacs PK, Moss LJ, Karellas A, et al. Comparison of full-field digital mammography with screen-film mammography for cancer detection: results of 4,945 paired examinations. Radiology. 2001;218(3):873–80.CrossRefPubMed
4.
go back to reference Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med. 2005;353(17):1773–83.CrossRefPubMed Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med. 2005;353(17):1773–83.CrossRefPubMed
5.
go back to reference Lim K, Moles DR, Downer MC, Speight PM. Opportunistic screening for oral cancer and precancer in general dental practice: results of a demonstration study. Br Dent J. 2003;194(9):497–502. discussion 493.CrossRefPubMed Lim K, Moles DR, Downer MC, Speight PM. Opportunistic screening for oral cancer and precancer in general dental practice: results of a demonstration study. Br Dent J. 2003;194(9):497–502. discussion 493.CrossRefPubMed
6.
go back to reference Field EA, Morrison T, Darling AE, Parr TA, Zakrzewska JM. Oral mucosal screening as an integral part of routine dental care. Br Dent J. 1995;179(7):262–6.CrossRefPubMed Field EA, Morrison T, Darling AE, Parr TA, Zakrzewska JM. Oral mucosal screening as an integral part of routine dental care. Br Dent J. 1995;179(7):262–6.CrossRefPubMed
7.
go back to reference Stein C. A two-sample test for a linear hypothesis whose power is independent of the variance. Ann Math Stat. 1945;16(3):243–58.CrossRef Stein C. A two-sample test for a linear hypothesis whose power is independent of the variance. Ann Math Stat. 1945;16(3):243–58.CrossRef
8.
go back to reference Wittes J, Brittain E. The role of internal pilot studies in increasing the efficiency of clinical trials. Stat Med. 1990;9(1–2):65–71. discussion −2.CrossRefPubMed Wittes J, Brittain E. The role of internal pilot studies in increasing the efficiency of clinical trials. Stat Med. 1990;9(1–2):65–71. discussion −2.CrossRefPubMed
9.
go back to reference Coffey CS, Muller KE. Exact test size and power of a Gaussian error linear model for an internal pilot study. Stat Med. 1999;18(10):1199–214.CrossRefPubMed Coffey CS, Muller KE. Exact test size and power of a Gaussian error linear model for an internal pilot study. Stat Med. 1999;18(10):1199–214.CrossRefPubMed
10.
go back to reference Friede T, Kieser M. Sample size recalculation in internal pilot study designs: a review. Biom J. 2006;48(4):537–55.CrossRefPubMed Friede T, Kieser M. Sample size recalculation in internal pilot study designs: a review. Biom J. 2006;48(4):537–55.CrossRefPubMed
11.
go back to reference Wu C, Liu A, Yu KF. An adaptive approach to designing comparative diagnostic accuracy studies. J Biopharm Stat. 2008;18(1):116–25.CrossRefPubMed Wu C, Liu A, Yu KF. An adaptive approach to designing comparative diagnostic accuracy studies. J Biopharm Stat. 2008;18(1):116–25.CrossRefPubMed
12.
go back to reference Coffey CS, Muller KE. Controlling test size while gaining the benefits of an internal pilot design. Biometrics. 2001;57(2):625–31.CrossRefPubMed Coffey CS, Muller KE. Controlling test size while gaining the benefits of an internal pilot design. Biometrics. 2001;57(2):625–31.CrossRefPubMed
14.
go back to reference Wittes J, Schabenberger O, Zucker D, Brittain E, Proschan M. Internal pilot studies I: Type I error rate of the naive t-test. Stat Med. 1999;18(24):3481–91.CrossRefPubMed Wittes J, Schabenberger O, Zucker D, Brittain E, Proschan M. Internal pilot studies I: Type I error rate of the naive t-test. Stat Med. 1999;18(24):3481–91.CrossRefPubMed
15.
go back to reference Zucker DM, Wittes JT, Schabenberger O, Brittain E. Internal pilot studies II: comparison of various procedures. Stat Med. 1999;18(24):3493–509.CrossRefPubMed Zucker DM, Wittes JT, Schabenberger O, Brittain E. Internal pilot studies II: comparison of various procedures. Stat Med. 1999;18(24):3493–509.CrossRefPubMed
16.
go back to reference Miller F. Variance estimation in clinical studies with interim sample size reestimation. Biometrics. 2005;61(2):355–61.CrossRefPubMed Miller F. Variance estimation in clinical studies with interim sample size reestimation. Biometrics. 2005;61(2):355–61.CrossRefPubMed
19.
go back to reference Coffey CS, Kairalla JA, Muller KE. Practical methods for bounding Type I error rate with an internal pilot design. Commun Stat Theory Methods. 2007;36(11):2143–57.CrossRef Coffey CS, Kairalla JA, Muller KE. Practical methods for bounding Type I error rate with an internal pilot design. Commun Stat Theory Methods. 2007;36(11):2143–57.CrossRef
20.
go back to reference Pepe MS. The statistical evaluation of medical tests for classification and prediction. New York, NY: Oxford University Press; 2003. Pepe MS. The statistical evaluation of medical tests for classification and prediction. New York, NY: Oxford University Press; 2003.
21.
go back to reference Demler OV, Pencina MJ, D’Agostino RB. Equivalence of improvement in area under ROC curve and linear discriminant analysis coefficient under assumption of normality. Stat Med. 2011;30(12):1410–8.PubMed Demler OV, Pencina MJ, D’Agostino RB. Equivalence of improvement in area under ROC curve and linear discriminant analysis coefficient under assumption of normality. Stat Med. 2011;30(12):1410–8.PubMed
22.
go back to reference Muller KE, Stewart PW. Linear model theory: univariate, multivariate, and mixed models. New York: Wiley-Interscience; 2006.CrossRef Muller KE, Stewart PW. Linear model theory: univariate, multivariate, and mixed models. New York: Wiley-Interscience; 2006.CrossRef
23.
go back to reference Muller KE, LaVange LM, Ramey SL, Ramey CT. Power calculations for general linear multivariate models including repeated measures applications. J Am Stat Assoc. 1992;87(420):1209–26.CrossRefPubMedPubMedCentral Muller KE, LaVange LM, Ramey SL, Ramey CT. Power calculations for general linear multivariate models including repeated measures applications. J Am Stat Assoc. 1992;87(420):1209–26.CrossRefPubMedPubMedCentral
24.
go back to reference Kairalla JA, Coffey CS, Muller KE. GLUMIP 2.0: SAS/IML software for planning internal pilots. J Stat Softw. 2008;28(7):1–32.CrossRef Kairalla JA, Coffey CS, Muller KE. GLUMIP 2.0: SAS/IML software for planning internal pilots. J Stat Softw. 2008;28(7):1–32.CrossRef
25.
go back to reference Inc. SI. SAS/STAT® 9.3 User’s Guide. SAS Institute Inc., Cary, NC. 2011. Inc. SI. SAS/STAT® 9.3 User’s Guide. SAS Institute Inc., Cary, NC. 2011.
26.
go back to reference Johnson NL, Kotz S, Balakrishnan N. Continuous univariate distributions, vol. 1. New York: Wiley-Interscience; 1994. Johnson NL, Kotz S, Balakrishnan N. Continuous univariate distributions, vol. 1. New York: Wiley-Interscience; 1994.
27.
go back to reference Johnson NL, Kotz S, Balakrishnan N. Continuous univariate distributions, vol. 2. New York: Wiley-Interscience; 1995. Johnson NL, Kotz S, Balakrishnan N. Continuous univariate distributions, vol. 2. New York: Wiley-Interscience; 1995.
28.
go back to reference Thisted RA. Elements of statistical computing: NUMERICAL COMPUTATION. Ipswich, Suffolk: Chapman and Hall/CRC; 1988. Thisted RA. Elements of statistical computing: NUMERICAL COMPUTATION. Ipswich, Suffolk: Chapman and Hall/CRC; 1988.
29.
go back to reference Poh CF, MacAulay CE, Zhang L, Rosin MP. Tracing the “at-risk” oral mucosa field with autofluorescence: steps toward clinical impact. Cancer Prev Res. 2009;2(5):401–4.CrossRef Poh CF, MacAulay CE, Zhang L, Rosin MP. Tracing the “at-risk” oral mucosa field with autofluorescence: steps toward clinical impact. Cancer Prev Res. 2009;2(5):401–4.CrossRef
30.
go back to reference Wong DT. Oral cancer biomarker study. 2012. Wong DT. Oral cancer biomarker study. 2012.
31.
go back to reference Scheckenbach K, Wagenmann M, Freund M, Schipper J, Hanenberg H. Squamous cell carcinomas of the head and neck in Fanconi anemia: risk, prevention, therapy, and the need for guidelines. Klin Padiatr. 2012;224(3):132–8.CrossRefPubMed Scheckenbach K, Wagenmann M, Freund M, Schipper J, Hanenberg H. Squamous cell carcinomas of the head and neck in Fanconi anemia: risk, prevention, therapy, and the need for guidelines. Klin Padiatr. 2012;224(3):132–8.CrossRefPubMed
32.
go back to reference Rosenberg PS, Socie G, Alter BP, Gluckman E. Risk of head and neck squamous cell cancer and death in patients with Fanconi anemia who did and did not receive transplants. Blood. 2005;105(1):67–73.CrossRefPubMed Rosenberg PS, Socie G, Alter BP, Gluckman E. Risk of head and neck squamous cell cancer and death in patients with Fanconi anemia who did and did not receive transplants. Blood. 2005;105(1):67–73.CrossRefPubMed
33.
go back to reference Elashoff D, Zhou H, Reiss J, Wang J, Xiao H, Henson B, et al. Prevalidation of salivary biomarkers for oral cancer detection. Cancer Epidemiol Biomarkers Prev. 2012;21(4):664–72.CrossRefPubMedPubMedCentral Elashoff D, Zhou H, Reiss J, Wang J, Xiao H, Henson B, et al. Prevalidation of salivary biomarkers for oral cancer detection. Cancer Epidemiol Biomarkers Prev. 2012;21(4):664–72.CrossRefPubMedPubMedCentral
34.
go back to reference Hu S, Arellano M, Boontheung P, Wang J, Zhou H, Jiang J, et al. Salivary proteomics for oral cancer biomarker discovery. Clin Cancer Res. 2008;14(19):6246–52.CrossRefPubMedPubMedCentral Hu S, Arellano M, Boontheung P, Wang J, Zhou H, Jiang J, et al. Salivary proteomics for oral cancer biomarker discovery. Clin Cancer Res. 2008;14(19):6246–52.CrossRefPubMedPubMedCentral
35.
go back to reference Arellano-Garcia M, Hu S, Wang J, Henson B, Zhou H, Chia D, et al. Multiplexed immunobead-based assay for detection of oral cancer protein biomarkers in saliva. Oral Dis. 2008;14(8):705–12.CrossRefPubMedPubMedCentral Arellano-Garcia M, Hu S, Wang J, Henson B, Zhou H, Chia D, et al. Multiplexed immunobead-based assay for detection of oral cancer protein biomarkers in saliva. Oral Dis. 2008;14(8):705–12.CrossRefPubMedPubMedCentral
Metadata
Title
An internal pilot design for prospective cancer screening trials with unknown disease prevalence
Authors
John T. Brinton
Brandy M. Ringham
Deborah H. Glueck
Publication date
01-12-2015
Publisher
BioMed Central
Published in
Trials / Issue 1/2015
Electronic ISSN: 1745-6215
DOI
https://doi.org/10.1186/s13063-015-0951-3

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