DEPARTMENT OF BIOSTATISTICS
Johns Hopkins School of Public Health

 

Selected articles citing work developed in preparation for and during the project  
of "Statistical Methods for Partially Controlled Studies"
(PI: CE Frangakis, Co-PI: DB Rubin; funding agency: National Eye Institute, NIH)
 

  1. Aalen, O.O. (2004).  Discussion on causality Scandinavian Journal of Statistics , 31, 198-201.
     
  2. Ajdacic-Gross V, Bopp M, Sansossio R, et al.. (2005). Diversity and change in suicide seasonality over 125 years. Journal of Epidemiology and Community Health 59, 967--972
     
  3. An H, Little R (2005). Semiparametric estimation of treatment effect in a pretest-posttest study with missing data - Comment. Statistical Science 20, 282--301.
     
  4. Angrist, JD. (2004). American education research changes track. Oxford review of Economic Policy 20, p. 198--212.
     
  5. Angrist J, Bettinger, E, and Kremer, M. (2005). Long term consequences of secondary school vouchers: evidence from administrative records in Colombia. American Economic Review forthcoming
     
  6. Ansolabehere, S (2002). Comment on ``School Choice in NY City: A Bayesian Analysis of an Imperfect  Randomized Experiment '', by J Barnard, CE Frangakis, J Hill, and DB Rubin.  Case Studies in Bayesian Statistics, V 5, Gatsonis et al. (eds) New York: Springer-Verlag, 69-72.
     
  7. Arjas, E. (2004).  Reply to discussion on causality Scandinavian Journal of Statistics , 31, 193-196.
     
  8. Baker SG (2006). A simple meta-analytic approach for using a binary surrogate endpoint to predict the effect of intervention on true endpoint. Statistics in Medicine 24, 3773--3787
     
  9. Baker, SG (2001). Comment on ``Addressing an idiosyncrasy in estimating survival curves using double-sampling in the presence of self-selected right censoring '', by CE Frangakis and DB Rubin. Biometrics, 57, 348-350.
     
  10. Baker, SG (2000). Analyzing a randomized cancer prevention trial with a missing binary outcome, an auxiliary variable, and all-or-none compliance. Journal of the American Statistical Association, 95, 43-50 (Sec. 5).
     
  11. Baker, SG (1998). Analysis of survival data from a randomized trial with all-or-none compliance: estimating the cost-effectiveness of a cancer screening program. Journal of the American Statistical Association, 93, 929--934. (Sec. 5).
     
  12. Baker SG and Kramer BS (2002). A perfect correlate does not a surrogate make. BMC Medical Research Methodology 3, p. 16.
     
  13. Berck R and Xu H (2003). Comment on  "Barnard, J, Frangakis, CE, Hill, J, and Rubin, DB. A principal stratification approach to broken randomized experiments: a case study of School Choice Vouchers in New York City", Journal of the American Statistical Association 98, 318-320.
     
  14. Berger, VW (2002). Valid adjustment of randomized comparisons for binary covariates.  Biometrical Journal 46, p. 589--594.
     
  15. Berger, VW and Weinstein S (2004). Ensuring the comparability of comparison groups: is randomization enough? Controlled Clinical Trials 25, 515--524
     
  16. Bjorksten KS, Bjerregaard P, Kripke DF (2005). Suicides in the midnight sun - a study of seasonality in suicides in West Greenland. Psychiatry Research 133, 205--213
     
  17. Buddin R and Zimmer R (2004). The political dynamics of school choice: Negotiating contested rerrain.Journal of Policy Analysis and Management 23, 929--932.
     
  18. Campbell DE, West MR, and Peterson PE (2005). Participation in a national, means-tested school voucher program. Journal of Policy Analysis and Management 24, 523--541.
     
  19. Campbell, G, Yue, L, Penello, G, Barrick M. (2003). Encyclopedia of Biopharmaceutical Statistics  ISBN: 0-8247-4263-X.
     
  20. Chen, E, Bloomberg, GR, Fisher, EB, Strunk, R. (2002). Predictors of Repeat Hospitalizations in Children with Asthma: The Role of Psychosocial and Socio-Environmental Factors.  Health Psychology, in press
     
  21. Cheng J and Small D. (2005). Bounds of causal effects in three arm trials with non compliance. Journal of the Royal Statistical Society; Series B,,  (forthcoming).
     
  22. Cox, DR.  and Berrington A. (2002). Discussion of ``Clustered encouragement design with individual noncompliance: Bayesian inference and application to Advance Directive Forms", by CE Frangakis, DB Rubin, and XH Zhou. Biostatistics,  3, 165--167.
     
  23. Cox, DR and Wermuth (2004).  Causality: a statistical view International Statistical Review, 72, 285--305.
     
  24. Corcoran P, Reilly M, Salim A, et al. (2005). Temporal variation in Irish suicide rates. Suicide and Life Thretening Behavior 34, 429--438
     
  25. Corhonen, P. (2000). Accelerated failure time models for nonignorable noncompliance in randomized trials. PhD Thesis, Department of Nutrition, National Public Health Institute, University of Helsinki.
     
  26. Davis GE Lowell, WE. (2004). Chaotic solar cycles modulate the incidence and severity of mental illness. Medical Hypotheses 62, 207--214.
     
  27. Deisenhammer EA (2004). Weather and suicide: the present state of knowledge on the association of meteorological factors with suicidal behaviour. Acta Psychiatrica Scandinavica. 108, 455--459
     
  28. Des Jarlais, DC and Braine N (2004). Editorial: Assessing syringe exchange programs.Addiction 99, p. 1081--1082.
     
  29. De Stavola, BL, Nitsch D, Silva, ID et al. (2005). Statistical issues in life course epidemiology. American Journal of Epidemiology. 163, 84--96.
     
  30. Dunn G and Goetghebeur E (2005). Analysing compliance in clinical trials Statistical Methods in Medical Research,  14, 325--326.
     
  31. Dunn G, Maracy M, Dowrick C, Ayuso-Mateos JL, Dalgard OS, Page H, Lehtinen V, Casey P, Wilkinson C, Vazquez-Barquero JL, Wilkinson G; ODIN group. (2003). Estimating psychological treatment effects from a randomised controlled trial with both non-compliance and loss to follow-up. British Journal of Psychiatry,  183, 323--331.
     
  32. Dunn G, Maracy M, and Tomenson B (2005). Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: the role of instrumental variable methods. Statistical Methods in Medical Research,  14, 369--395.
     
  33. Farewell, V. T.,Lawless, J. F., Gladman, D. D., and Murray, B. U. (2003). Tracing studies and analysis of the effect of loss to follow-up on mortality estimation from patient registry data. Journal of the Royal Statistical Society, C,  52, 445.
     
  34. Fischer T, Johnsen SP, Pedersen L, et al. (2005). Seasonal variation in hospitalization and case fatality of subarachnoid hemorrhage - A nationwide Danish study on 9,367 patients. Neuroepidemiology 24, 32--37
     
  35. Gilbert, P. B., Bosch, R. J., and M. G. Hudgens (2003). Sensitivity analysis for the assessment of causal vaccine effects on viral load in AIDS vaccine trials. Biometrics, 59, 531-541.
     
  36. Goetghebeur E and Vansteelandt S. (2002).  Discussion of ``Clustered encouragement design with individual noncompliance: Bayesian inference and application to Advance Directive Forms", by CE Frangakis, DB Rubin, and XH Zhou. Biostatistics,  3,  169--171.
     
  37. Gottfredson DC, Kearley BW, Najaka SS, et al. (2005). The Baltimore City Drug Treatment Court - 3-year self-report outcome study. Evaluation Review29, 42--46.
     
  38. Goetghebeur E and Vansteelandt S (2005). Structural mean models for compliance analysis in randomized clinical trials and the impact of errors on measures of exposure. Statistical Methods in Medical Research,  14,397--415.
     
  39. Greene T, Daugirdas J, Depner T, et al. (2005). Association of achieved dialysis dose with mortality in the Hemodialysis Study: An example of "dose-targeting bias" . Journal of the Americal Society of Nephrology,  16,3371--3380.
     
  40. Greenland S. (2004). Multiple-bias modelling for analysis of observational data. Journal of the Royal Statistical Society: Series A, ,  V168,  267--306.
     
  41. Greevy R, Silber J. H., Cnaan, A., and Rosenbaum, P. R. (2004). Randomization Inference with imperfect compliance in ACE-Inhibitor after anthracyline randomized trial. Journal of the American Statistical Association,  99,  7--15.
     
  42. Harrison A, Senserrick T, and Tingvall, C (2000). [Swedish National Road Administration]. Development and trial of a method to investigate the acceptability of seatbelt reminder systems. Accident Research Center. Report No. 170. ISBN 0 7326 1469 4.
     
  43. Harkanen, T, Knekt, P, Virtala, E et al. (2005). A case study in comparing therapies involving informative drop-out, non-ignorable non-compliance and repeated measurements. Statistics in Medicine 24, 3773--3783
     
  44. Hen X, Liu MZ, Zhang A (2005). A note on postrandomization adjustment of covariates. Drug Information Journal 39, 373--383.
     
  45. Hernan MA, Robins, JM, Rodriguez LAG. (2005). Discussion on "Statistical issues arising in the Women's Health Initiative". Biometrics 61, 922--930
     
  46. Hill, J. L. (2000). Applications of Innovative Statistical Methodology for the Social Sciences (part 3). Ph. D. Thesis, Department of Statistics, Harvard University.
     
  47. Hill., JL, Waldfogel, J, and Brooks-Gunn, J (2002). Differential effects of high quality child care. Journal of Policy Analysis and Management 21, p. 601--627.
     
  48. Hogan, J and Daniels, M (2002). A hierarchical modelling approach to analysing longitudinal data with drop-out and non-compliance, with application to an equivalence trial in paediatric acquired immune deficiency syndrome. Journal of the Royal Statistical Society: Series C (Applied Statistics) 51, p. 1.
     
  49. Hogan JW, Roy, J, and Korkontzelou, C. (2004). Tutorial in biostatistics - Handling drop-out in longitudinal studies. Statistics in Medicine 23, 1455--1497.
     
  50. Hollis, S. (2002). A graphical sensitivity analysis for clinical trials with non-ignorable missing binary outcome. Statistics in Medicine 21, 3823--3834
     
  51. Howell, WG (2004). Dynamic selection effects in means-tested, urban school voucher programs. Journal of Policy Analysis and Management 23, p. 225--250.
     
  52. Huba et al. (2003). Modeling HIV Risk in Highly Vulnerable Youth. Structural Equation Modeling, 10, 583--608.
     
  53. Hudgens, MG, and Gilbert, PB, and Self, SG. (2004). Endpoints in vaccine trials. Statistical Methods in Medical Research. 13, 89--114
     
  54. Hudgens MG and Halloran ME (2005). Causal vaccine effects on binary post-infection outcomes. Journal of the American Statistical Association (forthcoming)
     
  55. Hudgens, MG, Hoering, A, and Self, SG. (2003). On the analysis of viral load endpoints in HIV vaccine trials. Statistics in Medicine. 22, 2281--2298
     
  56. Jessen, Ved Geert (2003). Saeson for selvmordsadfaerd: myter og resultater. Suicidologi, 8, 14-22.
     
  57. Jo, B. (2002).  Statistical power in randomized intervention studies with noncompliance.  Psychological Methods, 178--193.
     
  58. Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Forthcoming in Journal of Educationan and Behavioral Statistics.
     
  59. Joffee, MM, Ten Have TR, Brensinger C. (2003). The compliance score as a regressor in randomized trials. Biostatistics 4, 327--340
     
  60. Jonsson, EN and Sheiner, LB. (2002). More efficient clinical trials through use of scientific model-based statistical tests. Clinical Pharmacology and Therapeutics 72, 603--614
     
  61. Junker, B and Gitelman A. I (2002). Comment on ``School Choice in NY City: A Bayesian Analysis of an Imperfect Randomized Experiment '', by J Barnard, CE Frangakis, J Hill, and DB Rubin.  Case Studies in Bayesian Statistics, V, Gatsonis et al. (eds) New York: Springer-Verlag, 73-91.
     
  62. King G and Zeng L. (2006). The dangers of extreme counterfactuals. Political Analysis. forthcoming.
     
  63. Krueger, A, and Zhu, P (2003). Comment on  "Barnard, J, Frangakis, CE, Hill, J, and Rubin, DB. A principal stratification approach to broken randomized experiments: a case study of School Choice Vouchers in New York City", Journal of the American Statistical Association 98, 314-318.
     
  64. Langagergaard, V, Norgard, B., Mellemkjaer, L., Pedersen, L., Rothman, K. J., and Sorensen, H. T.(2004). Seasonal variation in month of birth and diagnosis in children and adlosescents with hodgkin disease and non-hodgkin lymphoma. Journal of Pediatric Hematology/Oncology,  25(3):534--538.
     
  65. Lauritzen, S (2004).  Graphical models for surrogates Scandinavian Journal of Statistics , V 31.
     
  66. Levy, DE, O'Malley, AJ, and Normand, SL (2003).  Covariate adjustment in clinical trials with non-ignorable missing data and non-compliance. Statistics in Medicine , 23, 2319--2339.
     
  67. Liddell C, Rae G, Brown TRM, et al. (2004). Giving patients an audiotape of their GP consultation: a randomised controlled trial. British Journal of Medical Practice 54, 667--672
     
  68. Lie SA, Engesaeter LB, Havelin LI, Gjessing HK, Vollset SE. (2004). Dependency issues in survival analyses of 55,782 primary hip replacements from 47,355 patients. Statistics in Medicine,  233227--3240.
     
  69. Loeys, T., and Goetghebeur, E. (2003). A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all or nothing compliance. Biometrics,  59100.
     
  70. Loeys T, Goetghebeur E and A. Vandebosch A. (2005). Causal Proportional Hazards Models and Time-constant Exposure in Randomized Clinical Trials. Lifetime Data Analysis,  11, 435--449.
     
  71. Matsui, S. (2004). Applications of a parametric model for informative censoring. Biometrics 60, 704--714
     
  72. Matsui, S. (2004). Analysis of times to repeated events in two-arm randomized trials with noncompliance and dependent censoring. Biometrics 60, 965--976
     
  73. Matsuyama, Y. (2002). Correcting for non-compliance of repeated binary outcomes in randomized clinical trials: randomized analysis approach. Statistics in Medicine 21, 675--687
     
  74. Mealli F, Imbens GW, Ferro S, Biggeri A. (2004). Analyzing a randomized trial on breast self-examination with noncompliance and missing outcomes.Biostatistics 5, 207--222.
     
  75. Mercatanti, A. (2004). Analyzing a randomized experiment with imperfect compliance and ignorable conditions for missing data: theoretical and computational issues. Computational Statistics and Data Analysis 46, 493--509
     
  76. McIntosh, MW (1999). Instrumental variables when evaluating screening trials: estimating the benefit of detecting cancer by screening. Statistics in Medicine, 18, 2775--2794 (Sec. 5.2).
     
  77. Muthen, Bengt O. (2002).  Beyond SEM: Generalized latent variable modelling. Behaviormetrika, 21, 81—117.
     
  78. Muthen, B, Booil, J, and Brown, H (2003). Comment on  "Barnard, J, Frangakis, CE, Hill, J, and Rubin, DB. A principal stratification approach to broken randomized experiments: a case study of  School Choice Vouchers in New York City", Journal of the American Statistical Association 98, 311--314.
     
  79. Nishimura M, Terao T, Soeda S, et al. (2004). Suicide and occupation: further supportive evidence for their relevance. Progress in Neuropharmacology and Biological Psychiatry 28, 83--87
     
  80. Partonen T, Haukka J, Pirkola S (2004). Time patterns and seasonal mismatch in suicide. Acta Psychiatrica Scandinavica 109, 110-115.
     
  81. Partonen T, Haukka J, Nevanlinna H, et al. (2004). Analysis of the seasonal pattern in suicide Journal of Affective Disorders 81, 13-139.
     
  82. Peterson, PE and Howell, WG (2004). Efficiency, bias, and classification schemes: a response to Alan B. Krueger and Pei Zhu. American Behavioral Scientist 47, p. 699--719
     
  83. Postolache, TT, Oren DA. (2005). Circadian phase shifting, alerting, and antidepressant effects of bright light treatment. Clinics in Sports Medicine 24, 381
     
  84. Prentice RL, Pettinger M, Anderson GL (2005). Statistical issues arising in the Women's Health Initiative. Biometrics 61, 899-911
     
  85. Robins, J. M., Rotnitzky, A., and Bonetti, M. (2001). Comment on ``Addressing an idiosyncrasy in estimating survival curves using double-sampling in the presence of self-selected right censoring '', by CE Frangakis and DB Rubin. Biometrics, 57, 343-347.
     
  86. Committee on Scientific Principles for Education Research,  RJ Shavelson and LTowne, (Eds), National Research Council. Scientific Research in Education (2002).
     
  87. Saadat M, Bahaoddini A, Mohabatkar H, et al. (2005). High incidence of suicide by burning in Masjid-i-Sulaiman (southwest of Iran), a polluted area with natural sour gas leakage. Burns 30, 829--832
     
  88. Sashegyi AI, Brown KS, Farrell PJ. (2002). Application of a generalized random effects regression model for cluster-correlated longitudinal data to a school-based smoking prevention trial. American Journal of Epidemiology 152, 1192--1200
     
  89. Shmitz KH, Holtzman, J, Courneya, KS et al. (2005). Controlled physical activity trials in cancer survivors: A systematic review and meta-analysis. Cancer Epidemiology Biomarkers and Prevention. 14, 1588--1595.
     
  90. Schuh A (2004). Suicides peak in May and June. Is the decision to kill oneself dependent on the weather? MMW-Fortschritte Der Medizin 146, 614-615.
     
  91. Sheiner LB and Steiner JL. (2000). Pharmacokinetic/pharmacodynamic modeling in drug development. Annual Review of Pharmacology and Toxicology, 40, 67--95.
     
  92. Sheiner LB and Wakefield J. (1999). Population modelling in drug development. Statistical Methods in Medical Research,  8(3):183--193.
     
  93. Shepherd, BE, Gilbert, PB, Jemiai, Y, and Rotnitzky, A. (2006). Sensitivity Analyses Comparing Outcomes Only Existing in a Subset Selected Post-Randomization, Conditional on Covariates, with Application to HIV Vaccine Trials. Biometrics in press.
     
  94. Skriver, MV, Pedersen, L., Stang, P, Lund, L., Rothman, KJ. (2004). The month of birth does not affect the risk of hypospadias.  European Journal of Epidemiology, Forthcoming, 1232—1244.
     
  95. Ten Have T. R., Elliott M. R., Joffe, M., and Zanutto, E. (2004). Causal models for randomized physician encouragement trials in treating primary care.. Journal of the American Statistical Association,  99,  16--25.
     
  96. The Government of Quebec, Canada, Ministry of Health and Social Services. (1998). The intoxication of alcohol: consequences and determinants. (1998). Legal document: ISBN: 2-550-33716-6.
     
  97. Tonelli LH, Postolache TT (2005). Tumor necrosis factor alpha, interleukin-1 beta, interleukin-6 and major histocompatibility complex molecules in the normal brain and after peripheral immune challenge. Neurological Research. 27, 679--684.
     
  98. US Surgeon General's Report (2001).  Women and Smoking. Chapter 4.
     
  99. Yau, LH and Little, R J. (2001). Inference for the complier-average causal effect from longitudinal data subject to noncompliance and missing data, with application to a job training assessment for the unemployed.  Journal of the American Statistical Association, 14, 327—347.
     
  100. White, IR. (2005). Uses and limitations of randomization based efficacy estimators.  Statistical Methods in Medical Research. , 96, 1232—1244.
     
  101. Zhang, J (2002). Causal inference with principal stratification. Ph. D. Thesis, Department of Statistics, Harvard University.

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