Curriculum Vitae for Stuart G. Baker, ScD
Mathematical Statistician
US Mail Address
Biometry Research Group, DCP
National Cancer Institute
Executive Plaza North, Suite 3131
6130 Executive Boulevard, MSC 7354
Bethesda, MD 20892-7354 |
Shipping Address
Biometry Research Group, DCP
National Cancer Institute
6130 Executive Boulevard, Suite 3131
Rockville, MD 20852 |
Phone: (301) 496-7708
Fax: (301) 402-0816
E-mail: sb16i@nih.gov |
| Date and Place of Birth: | February 18, 1958; Washington, DC |
| Citizenship: | United States |
| Education: |
| 1980 |
AB (Applied Mathematics), Magna cum laude
Harvard College, Cambridge, MA |
| 1982 | MS (Biostatistics)
Harvard School of Public Health, Cambridge, MA |
| 1984 | ScD (Biostatistics)
Harvard School of Public Health, Cambridge, MA |
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Brief Chronology of Employment:
| 1984-1985 | Postdoctoral Research Fellow
Department of Biostatistics
Harvard School of Public Health
Boston, MA |
| 1985-1988 | Staff Fellow
Biometry Branch
National Cancer Institute
Bethesda, MD |
| 1988-1992 | Senior Staff Fellow
Biometry Branch
National Cancer Institute
Bethesda, MD |
| 1992-1997 | Mathematical Statistician GS-13
Biometry Branch
National Cancer Institute
Bethesda, MD |
| 1997-2005 | Mathematical Statistician GS-14
Biometry Branch
National Cancer Institute
Bethesda, MD |
| 2005-present | Mathematical Statistician GS-15
Biometry Branch
National Cancer Institute
Bethesda, MD |
Awards:
| 1993, 1995, 1996, 1998, 2010 | Performance Award
U.S. Department of Health and Human Services |
| 2004 | First recipient of the Distinguished Alum Award from the
Harvard School of Public Health
Department of Biotatistics |
| 2006 | Fellow
American Statistical Association |
| 2007 | Elected Member
International Statistical Institute |
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Special Appointments or Projects
| 1990-1994 | Editorial Board, Medical Decision Making |
| 1994-2005 | Editorial Board, Statistics in Medicine |
| 1995-1998 | Statistical Consultant to the
Society for Obstetrics Anesthesia and Perinatalogy |
| 1998 | Selection Committee
Lefkopolou Lecture, Harvard Biostatistics |
| 2000-2008 | Editorial Board, Disease Markers |
| 2001-Present | Editorial Board, Journal of the National Cancer Institute |
| 2004-2008 | Editorial Board, Biometrics |
| 2006-Present | Statistical Advisory Group, Trials |
| 2006-2007 | Guest Editor, Statistical Methods in Medical Research
See Statistical Methods in Medical Research 2009;18:115. |
| 2007, 2009 | Selection Committee
Harvard Biostat Distinguished Alum Award |
| 2008 | Workshop Co-organizer
Paradoxes in Carcinogenesis |
| 2009-Present | Editorial Board, Cancer Biomarkers |
Teaching/Mentoring
| 1995-1997 | Thesis Consultant:
Mark Moran, University of South Carolina |
| 2000-2001 | Thesis Consultant:
Lori Dodd, University of Washington, Seattle |
| 2002 | Thesis Consultant:
Jonathan Rabinowitz, Bar Ilan University, Israel |
| 1997-2000, 2002-2005 | Lecturer:
Cancer Prevention and Control Academic Course |
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First Recipient of the Distinguish Alum Award from the Department of Biostatistics at the Harvard School of Public Health, June 2004
Excerpts from the July 2005 issue of the Harvard Biostatistics newsletter, Biostat Connections
- …the Distinguished Alum Award was initiated in 2003 by the faculty of the Biostatistics Department to "recognize an individual in government, industry, or academia, who by virtue of applications to support of research, methodology and theory, significant organizational responsibility, and teaching has impacted the theory and practice of statistical science.
- …Stuart was not selected for this award solely because of his methodological research productivity. What is most impressive about Stuart's work is that it bridges the two research communities of clinical and epidemiological researchers and biostatisticians. His letter of nomination stated, "For medical investigators simple formulas have great appeal. (To quote Albert Einstein, "Everything should be made as simple as possible—but not simpler"). Much of Dr. Baker's research has involved creating novel approaches and formulating them as crisply as possible. For analyses that require complicated methodology, medical investigators desire clear assumptions with statistics that are easy to interpret. These (characteristics) are the hallmark of many of Dr. Baker's more mathematical papers." Stuart has contributed to many research areas including: novel designs and analytic methods to reduce bias in studies involving historical controls or non-randomized screening studies; methods for analysis of nonignorable missing data; and most recently to the analysis of surrogate endpoints and genetics data.
- …Students were inspired by the discussion with this former graduate from our program who has managed his career as a statistician so well. Stuart's humility, humor and poise contributed a memorable Department of Biostatistics day.
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Reviewer of Papers and Proposals From the Following Sources
Methodology Journals:
American Journal of Epidemiology, American Statistician, Biometrics, Biometrical Journal, Bioinformatics, Biometrika, BMC Bioinformatics, Controlled Clinical Trials, Computational Statistics and Data Analysis , Epidemiology, Health Services and Outcomes Research Methodology, Journal of Computational Statistics and Data Analysis, Journal of Clinical Epidemiology, Journal of Epidemiology and Biostatistics, Journal of the American Statistical Association, Journal of Biopharmaceutical Statistics, Journal of the Royal Statistical Society Series A, Journal of the Royal Statistical Society Series B, Journal of the Royal Statistical Society, Series C, Journal of Statistical Planning and Inference, Mathematical and Computer Modelling, Medical Decision Making, Psychometrics, Statistics in Medicine, Statistical Methods in Medical Research, Statistica Neerlandica, Statistica Sinica, Technometrics
Biomedical Journals:
Anesthesiology, Annals of Internal Medicine, Archives of Physical Medicine and Rehabilitation, Breast Cancer Research, British Medical Journal, Cancer Journal, Epidemiology, Gastroenterology, Genetics in Medicine, International Journal of Cancer, Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology, Journal of Clinical Oncology, Journal of the National Cancer Institute, Journal of Urology, Molecular Genetics and Genomics, PLoS Genetics, Preventive Medicine
Research Proposals:
Various research proposals submitted to the National Cancer Institute (particularly the Early Detection Research Network); surrogate endpoint evaluation for the Food and Drug Administration; United States-Israel Binational Science Foundation
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Bioinformatics
Baker and Kramer (BMC Bioinformatics, 2006) proposed a method to identify genes most likely to contribute to good classification. Baker (BMC Bioinformatics, 2010) proposed the Swirls-and-Ripples method for parsimonious prediction and gene selection using gene expression microarrays.
Baker SG, Kramer BS. Identifying genes that contribute most to good classification in microarrays 
. BMC Bioinformatics 2006;7:407.
Baker SG, Kramer BS. Using microarrays to study the microenvironment in tumor biology: the crucial role of statistics. Seminars in Cancer Biology 2008;18:305-310.
Baker SG. Simple and flexible classification via Swirls-and-Ripples . BMC Bioinformatics 2010;11:452.
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Biomarker and Diagnostic Testing Studies
Baker et al. (BMC Med Res Methodol, 2002) discussed the design and analysis of cancer biomarker studies using stored specimens. Baker (J Natl Cancer Inst, 2009) proposed a biomarker development pipeline leading from biomarker identification to cancer screening evaluation.
Baker SG. Evaluating a new test using a reference test with estimated sensitivity and specificity. Communications in Statistics 1991;20:2739-2752.
Baker SG. Evaluating multiple diagnostic tests with partial verification. Biometrics 1995;51:330-337.
Baker SG, Connor RJ, Kessler LG. The partial testing design: a less costly way to test equivalence for sensitivity and specificity. Stat Med 1998;17:2219-2232.
Baker SG. Identifying combinations of cancer biomarkers for further study as triggers of early intervention. Biometrics 2000;56:1082-1087.
Baker SG, Tockman MS. Evaluating serial observations of precancerous lesions for further study as a trigger for early intervention. Stat Med 2002;21:2383-2390.
Baker SG, Kramer BS, Srivastava S. Markers for early detection of cancer: Statistical issues for nested case-control studies . BMC Med Res Methodol 2002;2:4.
Baker SG. The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. J Natl Cancer Inst 2003;95(7):511-515.
Baker SG. Response to letter "Re: The central role of the Receiver Operating Characteristic (ROC) curves in evaluating tests for the early detection of cancer". J Natl Cancer Inst 2005;97:234-235.
Baker SG, Kramer BS, Prorok PC. Development tracks for cancer prevention markers. Dis Markers 2004;20(2):97-102.
Baker SG, Kramer BS. Biomarkers, surrogate endpoints, and early detection imaging tests: reducing confusion. International Chinese Statistical Association Bulletin, January 2004.
Baker SG, Kramer BS. Why biotechnologists should care about biostatistics. Asia-Pacific Biotech News 2006(30 November);10(22):1275-1278.
Baker SG, Kramer BS, McIntosh M, Patterson BH, Shyr Y, Skates S. Evaluating markers for the early detection of cancer: overview of study designs and methods of analysis. Clinical Trials 2006;3:43-56.
Baker SG, Kramer BS. Peirce, Youden, and Receiver Operating Characteristic Curves. The American Statistician 2007;November:343-346.
Baker SG. Improving the biomarker pipeline to develop and evaluate cancer screening tests . J Natl Cancer Inst 2009;101(16):1116-1119.
Baker SG, Kramer BS. Evaluating biomarkers for cancer screening . Pharma IQ 31/08/2/2010, article ID: 3077.
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Cancer Screening Evaluation
Baker and Chu (J Am Stat Assoc, 1990) introduced a novel approach to evaluating cancer screening in which older subjects were controls for younger subjects. This approach was the topic of invited talks at the German Society for Medical Informatics, Statistics, and Epidemiology in Bochum, Germany in 1995 and at the INSERM Workshop, Paris France in 1997. Baker, et al. (BMC Med Res Methodol, 2003) simplified the aforementioned approach. Baker et al. (BMC Med Res Methodol, 2002) and Baker and Kramer (J Med Screening, 2008) developed a simple approach to adjust for dilution in randomized cancer screening trials with follow-up after the last screening.
Baker SG. Innovations in screening: evaluating periodic screening without using data from a control group. In: Engstrom PF, Anderson P, Mortenson L (eds). Advances in Cancer Control VI. New York: Alan R. Liss, Inc., 1989:15-21.
Baker SG, Chu KC. Evaluating screening for the early detection and treatment of cancer without using a randomized control group. J Am Stat Assoc 1990;85:321-327.
Prorok PC, Connor RJ, Baker SG. Statistical considerations in cancer screening programs. In: Smith JA (ed). The Urologic Clinics of North America. Early Detection and Treatment of Localized Carcinoma of the Prostate. Philadelphia: WB Saunders, 1990;17:699-708.
Baker SG, Connor RJ, Prorok PC. Recent developments in cancer screening modeling. In: Miller AB, Chamberlain J, Day NE, Hakama M, Prorok PC (eds). Cancer Screening. Cambridge: Cambridge University Press,1991;404-418.
Prorok PC, Byar DP, Smart CR, Baker SG, Connor RJ. Evaluation of screening for prostate, lung and colorectal cancers: the PLC trial. In: Miller AB, Chamberlain J, Day NE, Hakama M, Prorok PC (eds). Cancer Screening. Cambridge: Cambridge University Press, 1991;300-320.
Baker SG. Evaluating the age to begin periodic breast cancer screening using data from a few regularly scheduled screens. Biometrics 1998;54:1569-1578.
Baker SG. Evaluating periodic cancer screening without a randomized control group: a simplified design and analysis. In: Duffy SW, Hill C, Esteve J (eds). Quantitative Methods for the Evaluation of Cancer Screening. London: Edward Arnold Limited, 2001; pp 34-41.
Baker SG, Pinsky P. A proposed design and analysis for comparing digital and analog mammography: special ROC methods for cancer screening. J Am Stat Assoc 2001;96:421-428.
Baker SG, Kramer BS, Prorok PC. Statistical issues in randomized trials of cancer screening . BMC Med Res Methodol 2002;2:11.
Baker SG, Erwin D, Kramer BS, Prorok PC. Using observational data to estimate an upper bound on the reduction in cancer mortality due to periodic screening . BMC Med Res Methodol 2003;3:4.
Baker SG, Erwin D, Kramer BS. Estimating the cumulative risk of false positive cancer screenings . BMC Med Res Methodol 2003;3:11.
Baker SG, Kramer BS, Prorok PC. Comparing breast cancer mortality rates before-and-after a change in availability of screening in different regions: extension of the paired availability design . BMC Med Res Methodol2004;4(1):12.
Baker SG. Screening and breast cancer. Letter to the editor. N Engl J Med 2006;354:767-768.
Baker SG, Kramer BS, Prorok PC. Early reporting for cancer screening trials. J Med Screen 2008;15(3):122-129.
Baker SG, Kramer BS. Estimating the cumulative risk of a false-positive under a regimen involving various types of cancer screening tests. J Med Screen 2008;15:18-22.
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Carcinogenesis: Paradoxes and Theories
Baker and Kramer (BMC Cancer, 2007) summarized paradoxes in carcinogenesis. This paper was followed by my co-organizing an NCI workshop in 2008 on paradoxes in early-stage carcinogenesis. Baker et al. (BMC Cancer, 2009) developed a mathematical model of morphostat diffusion that induces cancer. Baker et al. (J Clin Oncol, 2010) proposed the concept of paradigm instability related to two theories of early-stage carcinogenesis.
Baker SG, Kramer BS. Paradoxes in carcinogenesis: new opportunities for research directions . BMC Cancer 2007;7:151. [Highly accessed]
Baker SG, Soto AM, Sonnenschein C, Cappuccio A, Potter JD, Kramer BS. Plausibility of stromal initiation of epithelial cancers without a mutation in the epithelium: a computer simulation of morphostats . BMC Cancer 2009;9:89. [Highly accessed]
Baker SG, Cappuccio A, Potter JD. Research on early-stage carcinogenesis: are we approaching paradigm instability? J Clin Oncol 2010;28:3215-3218.
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Categorical Data Analysis
Baker (The Statistician, 1994) generalized methods in the literature to develop the MP transformation, which simplifies maximum likelihood estimation when multinomial cell probabilities involve summations of various terms in the denominator. Applications of the MP transformation (listed elsewhere) include the analysis of haplotype data and the evaluation of diagnostic tests.
DerSimonian R, Baker SG. Two-process models for discrete-time serial categorical response. Stat Med 1988;7:965-974.
Baker SG. A simple EM algorithm for capture-recapture data with categorical covariates (with discussion). Biometrics 1990;46:1193-1197.
Baker SG, Freedman LS, Parmar MKB. Using replicate observations in observer agreement studies with binary assessments. Biometrics 1991;47:1327-1338.
Freedman, LS, Parmar MKB, Baker SG. The design of observer agreement studies with binary observations. Stat Med 1993;12:165-179.
Baker SG. The multinomial-Poisson transformation. The Statistician 1994;43:495-504.
Baker SG. Multinomial-Poisson transformation. The Encyclopedia of Statistical Science, Update Volume 2. New York: John Wiley and Sons, 1998;416-418.
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Causal Inference
Baker and Lindeman (Stat Med, 1994) developed a likelihood-based potential outcomes model to adjust for all-or-none compliance when estimating efficacy in before-and-after studies. This paper was the subject of an invited talk in 1997 at the German Center for Cancer Research, Heidelberg, Germany. Baker et al. (Biostaistics, 2001) found that the paired availability design gave similar results as a meta-analysis of randomized trials in one application. The paired availability design was discussed by Edmund Gehan in "Nonrandomized trials", Encyclopedia of Biostatistics, 4, 3039-3042. The paired availability design was included as entry in a book by B.S. Everitt, Medical Statistics from A to Z. A Guide for Clinicians and Medical Students, Second Edition, 2006. The causal model in the paired availability design was independently proposed by Angrist, Imbens, and Rubin (J Am Stat Assoc, 1996).
Baker SG, Lindeman KS. The paired availability design: a proposal for evaluating epidural analgesia during labor. Stat Med 1994;13:2269-2278.
Baker SG. Compliance, all-or-none. In: Kotz S, Read CR, Banks DL (eds). The Encyclopedia of Statistical Science, Update Volume 1. New York: John Wiley and Sons, 1997;134-138.
Baker SG. The paired availability design: an update. In: Abel U, Koch A (eds). Nonrandomized Comparative Clinical Studies. Dusseldorf: Medinform-Verlag, 1998;79-84.
Baker SG. Analysis of survival data from a randomized trial with all-or-none compliance: estimating the cost-effectiveness of a cancer screening program. J Am Stat Assoc 1998;93:929-934.
Baker SG, Lindeman KS. Randomized and nonrandomized studies. Statistical considerations (editorial). Anesthesiology 2000;92:928-30.
Baker SG. Analyzing a randomized cancer prevention trial with a missing binary outcome, an auxiliary variable, and all-or-none compliance. J Am Stat Assoc 2000;95:43-50.
Baker SG, Lindeman KL. Rethinking historical controls. Biostatistics 2001,2:383-396.
Frangakis C, Baker SG. Compliance adjusted double-sampling designs for comparative research: estimation and optimal planning. Biometrics 2001;57:899-908.
Baker SG, Lindeman KL, Kramer BS. The paired availability design for historical controls . BMC Med Res Methodol 2001;1:9.
Baker SG, Kramer BS. Simple maximum likelihood estimates of efficacy in randomized trials and before-and-after studies, with implications for meta-analysis. Stat Methods Med Res 2005;14:1-19. correction 2005;14:349.
Baker SG. Counterfactuals and the paired availability design . Posted comment for Causal inference based on counterfactuals by M Höfler. BMC Med Res Methodol 2005;5:28.
Baker SG, Kramer BS, Lindeman KS. The paired availability design: if you can't randomize, perhaps this applies. Chance 2006;19:57-60.
Baker SG, Frangakis C, Lindeman KS. Estimating efficacy in a proposed randomized trial with initial and later noncompliance. J R Statist Soc C 2007;56:211-221.
Baker SG. Letter to the editor on causal inference for vaccines. J Am Stat Assoc 2007;102:394.
Baker SG. Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach . Biometrics 2011;67(1):319-323; discussion 323-325. (DOI: 10.1111/j.1541-0420.2010.01451_1.x)
Baker SG, Lindeman KS, Kramer BS. Clarifying the role of principal stratification in the paired availability design. Int J Biostat 2011;7(1):Article 25.
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Consulting
The newsletter articles were written when I was the statistical consultant to the Society of Obstetric Anesthesia and Perinatology (SOAP).
Stern RS, Weinstein MC, Baker SG. Risk reduction for nonmelanoma skin cancer with childhood sunscreen use. Arch Dermatol 1986;122:537-545.
Lindeman KS, Baker SG, Hirshman CA. Interaction between halothane and thenonadrenergic, noncholinergic inhibitory system in porcinetrachealis muscle. Anesthesiology 1994;81:641-648.
Pizov R, Brown RH, Weiss YS, Baranov D, Hennes H, Baker SG, Hirshman CA. Wheezing during induction of general anesthesia in patients with and without asthma: a randomized blinded trial. Anesthesiology 1995;82:1111-1116.
Baker SG. Searching for statistical significance; a misapplication of power. SOAP Newsletter. Summer 1996.
Baker SG. The correct and incorrect use of meta-analysis. SOAP Newsletter. Winter 1997.
Baker SG. The correct and incorrect use of logistic regression. SOAP Newsletter. Winter 1998.
Baker SG. Confidence intervals. SOAP Newsletter. Spring 1998.
Baker SG. Sample size is more than number crunching. SOAP Newsletter. Winter 2000.
Baker SG. Surrogate endpoints: illusion and reality. SOAP Newsletter. Fall 2000.
Roth MJ, Liu SF, Dawsey SM, Zhou B, Copeland C, Wang GQ, Solomon D, Baker SG, Giffen CA, Taylor PR. Cytologic detection of esophageal squamous cell carcinoma and precursor lesions using balloon and sponge samplers in asymptomatic adults in Linxian, China. Cancer 1997;80(11):2047-2059.
Chu KC, Baker SG, Tarone RE. A method for identifying rapid changes in U.S. cancer mortality trends. Cancer 1999;86:157-169.
Croswell JM, Kramer BS, Kreimer AR, Prorok PC, Xu J-L,Baker SG, Fagerstrom R, Riley TL, Clapp JD, Berg CD, Gohagan JK, Andriole GL, Chia D, Church TR, Crawford ED, Fouad MN, Gelmann EP, Lamerato L, Reding DJ, Schoen RE. Cumulative incidence of false-positive results in repeated multimodal cancer screening. Annals of Family Medicine 2009;7(3):212-222.
Croswell JM, Baker SG, Marcus PM, Clapp JD, Kramer BS. Cumulative incidence of false-positive tests in lung cancer screening: a randomized, controlled trial. Annals of Internal Medicine 2010;152(8):505-512.
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Graphical Methods
Baker and Kramer (J Women's Health & Gender-Based Med, 2001) re-invented a simple plot to illustrate Simpson's paradox. Howard Wainer called this plot a "BK-Plot' (Chance, 2002) and discussed it in a chapter on Simpson's paradox in his book, A Trout in the Milk and Other Visual Adventures. Reviewing Wainer's book Linda Pickel wrote, "Every student should at least be assigned the chapter on Simpson's paradox" (American Statistician, 2006, 203). Baker and Kramer (BMC Med Res Methodol, 2003) proposed plots to explain the transitivity fallacy.
Baker SG, Kramer BS. Good for women, good for men, bad for people: Simpson's paradox and the importance of sex-specific analysis in observational studies. Journal of Women's Health & Gender-Based Medicine 2001;10:867- 872.
Baker SG, Kramer BS. The transitive fallacy for randomized trials: If A bests B and B bests C in separate trials, is A better than C? BMC Med Res Methodol 2002;2(1):13.
Baker SG, Kramer BS. Randomized trials, generalizability, and meta-analysis: Graphical insights for binary outcomes . BMC Med Res Methodol 2003;3:10.
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Missing Data Adjustments
Baker and Laird (J Am Stat Assoc, 1988) proposed a non-ignorable missing-data model for categorical data. Baker (Stat Med, 1994) developed a powerful computational algorithm for fitting missing categorical data that was applied in subsequent papers. Baker et al. (Biostatistics, 2006) proposed a likelihood-based adjustment for missing outcomes in a randomized trial that involved propensity scores.
Baker SG, Laird NM. Regression analysis for categorical variables with outcome subject to nonignorable nonresponse. J Am Stat Assoc 1988;83:62-69.
Baker SG, Rosenberger WF, DerSimonian R. Closed-form estimates for missing counts in two-way contingency tables. Stat Med 1992;11:643-657.
Baker SG. A simple method for computing the observed information matrix when using the EM algorithm with categorical data. Journal of Computational and Graphical Statistics 1992;1:63-76.
Baker SG. Composite linear models for incomplete multinomial data. Stat Med 1994;13:609-622. Software at http://library.wolfram.com/infocenter/MathSource/706/.
Baker SG. Marginal regression for repeated binary data with outcome subject to non-ignorable non-response. Biometrics 1995;51:1042-1052.
Baker SG. The analysis of categorical case-control data subject to nonignorable nonresponse. Biometrics 1996,52:362-369.
Baker SG, Ko C, Graubard B. A sensitivity analysis for nonrandomly missing categorical data arising from a national health disability survey. Biostatistics 2003;1:41-56.
Baker SG, Freedman LS. A simple method for analyzing data from a randomized trial with a missing binary outcome . BMC Med Res Methodol 2003;3:8.
Baker SG, Fitzmaurice GM, Freedman LS, Kramer BS. Simple adjustments for randomized trials with nonrandomly missing or censored outcomes arising from informative covariates . Biostatistics 2006 Jan;7(1)29-40.
Baker SG, Darke AK, Pinsky P, Parnes HL, Kramer BS. Transparency and reproducibility in data analysis: the Prostate Cancer Prevention Trial . Biostatistics2010;11(3):413-418.
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Personalized Medicine and Risk Prediction Evaluation
Baker et al. (J R Statist Soc A, 2009) and Baker (J Natl Cancer Inst, 2009) proposed relative utility curves to evaluate the addition of a biomarker to a risk prediction model.
Baker SG, Freedman LS. Potential impact of genetic testing on cancer prevention trials, using breast cancer as an example. J Natl Cancer Inst 1995;87:1137-1144.
Baker SG, Kramer BS. Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial. J R Statist Soc C (Applied Statistics) 2005;54(5):941-954.
Baker SG, Cook NR, Vickers A, Kramer BS. Using relative utility curves to evaluate risk prediction. J R Statist Soc A 2009;172:729-748.
Baker SG. Putting risk prediction in perspective: relative utility curves . J Natl Cancer Inst 2009;101:1538-1542. doi: 10.1093/jnci/djp353
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Randomized Trials: Design and Analysis
Baker et al. (BMC Med Res Methodol, 2004) discussed the fallacy of enrolling only high-risk subjects into a cancer prevention trial when the goal is to draw conclusions about an average-risk population.
Baker SG, Heidenberger K. Choosing sample sizes to maximize expected health benefits subject to a constraint on total trial costs. Med Decis Making 1989;9:14-25.
The COMMIT Research Group. Community Intervention Trial for Smoking Cessation (COMMIT): I. cohort results from a four-year community intervention. Am J Public Health 1995;85(2):183-192.
Baker SG, Kramer BS, Corle D. The fallacy of enrolling only high-risk subjects in cancer prevention trials: is there a "free lunch?" BMC Med Res Methodol 2004(Oct 4);4:24.
Vickers AJ, Kramer BS, Baker SG. Selecting patients for randomized trials: a systematic approach. Trials 2006;7:30.
Baker SG, Kramer BS. Randomized trials for the real world: making as few and as reasonable assumptions as possible. Stat Meth Med Res 2008;3:243-252.
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Surrogate Endpoint Evaluation
Baker (Biostatistics, 2006) developed a simple approach to using surrogate endpoints to estimate the effect of intervention on true endpoint. Follow-up included organizing a session on surrogate endpoints at International Biometrics Conference, Montreal in 2006 and guest editing an issue of Stat Meth Med Res on surrogate endpoints (issue 5, 2008; see 2009; 18: p 115).
Baker SG, Kramer BS. A perfect correlate does not a surrogate make . BMC Med Res Methodol 2003;3:16.
Baker SG, Izmirlian G, Kipnis V. Resolving paradoxes involving surrogate endpoints. J R Statist Soc A 2005;168(4):753-762.
Baker SG. A simple meta-analytic approach for using a binary surrogate endpoint to predict the effect of intervention on true endpoint . Biostatistics 2006 Jan;7(1)58-70.
Baker SG. Surrogate endpoints: wishful thinking or reality? (Editorial). J Natl Cancer Inst 2006;98(8):502-503.
Baker SG. Two simple approaches for validating a binary surrogate endpoint using data from multiple trials. Stat Methods Med Res 2008;17(5):505-514.
Baker SG, Kramer BS. Surrogate endpoints. Encyclopedia of Cancer. New York: Springer-Verlag, 2008 10.1007/978-3-540-47648-1_5602.
Baker SG, Sargent DJ, Buyse M, Burzykowski T. Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time . Biometrics 2011 Aug 13.
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Survival Analysis
Baker et al. (Biometrics, 1993) developed a method to combine survival data from a random sample of subjects followed after preliminary removal from a study and from the remaining subjects not followed after preliminary removal from the study.
Feuer EJ, Kessler LG, Baker SG, Triolo HE, Green DT. The impact of breakthrough clinical trials on survival in population based tumor registries. J Clin Epidemiol 1991;44:141-153.
Feuer EJ, Hankey BF, Gaynor JJ, Wesley MN, Baker SG, Meyer JS. Graphical representation of survival curves associated with a binary non-reversible time dependent covariate. Stat Med 1992;11:455-474.
Baker SG, Wax Y, Patterson BH. Regression analysis of grouped survival data: informative censoring and double sampling. Biometrics 1993;49:379-389.
Wax Y, Baker SG, Patterson BH. A score test for non-informative censoring using doubly sampled grouped survival data. Appl Stat 1993;42(1):159-72.
Baker SG. Regression analysis of grouped survival data with incomplete covariates: nonignorable missing-data and censoring mechanisms. Biometrics 1994;50:821-826.
Baker SG. Discussion of doubling sampling with survival analysis. Biometrics 2001;57:348-350.
Baker SG. Cure model. Encyclopedia of Statistical Science. New York: John Wiley and Sons, 2004. DOI: 10.1002/0471667196.ess4040.
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Twin Studies and Genetics
Baker et al. (Biometrics, 2005) developed a novel approach to estimating genetic and environmental components of cancer from data on identical and fraternal twins. This paper was the topic of an invited talk at the Karolinska Insititute, Stockholm, Sweden in 2003. Baker and Kaprio (Br Med J, 2006) argued that the search for common cancer susceptibility genes faces important methodological and practical challenges for cancer prevention given the small chance that such genetic variants exist, and the difficulty and expense of demonstrating substantial clinical benefit if they do exist. The paper was the subject of an article in the London Times.
Baker SG, Lichtenstein P, Kaprio J, Holms N. Genetic susceptibility to prostate, breast and colorectal cancer among Nordic twins. Biometrics 2005;61:55-63.
Baker SG. A simple loglinear model for haplotype effects in a case-control study involving two unphased genotypes . Statistical Applications in Genetics and Molecular Biology 2005;4(1):Article14.
Baker SG, Kaprio J. Common susceptibility genes for cancer: search for the end of the rainbow . Br Med J 2006;332:1150-1152.
Baker SG, Kaprio J. Response to letter from Professor Lubinski regarding "Common susceptibility genes for cancer: search for the end of the rainbow" . Br Med J 2006;Rapid Response.
Baker SG, Kaprio J. Response to letter by Professor Luzzatto regarding "Common susceptibility genes for cancer: search for the end of the rainbow". Br Med J 2006;Rapid Response.
Baker SG, Kaprio J. Genes, cancer, and twins--the statistics behind the headlines. Chance 2007;20,:59-62.
Baker SG. Letter to the editor on "Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium" . J Natl Cancer Inst 2007;99(6)487-489.
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This page was last updated November 23, 2010
