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. An H, Little R (2005). Semiparametric estimation of treatment effect in a pretest-posttest study with missing data - Comment. Statistical Science 20, 282--301.
     
  3. Angrist, JD. (2004). American education research changes track. Oxford review of Economic Policy 20, p. 198--212.
     
  4. 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
     
  5. Arjas, E. (2004).  Reply to discussion on causality Scandinavian Journal of Statistics , 31, 193-196.
     
  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. 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.
     
  8. 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).
     
  9. 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).
     
  10. Baker SG and Kramer BS (2002). A perfect correlate does not a surrogate make. BMC Medical Research Methodology 3, p. 16.
     
  11. 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.
     
  12. Berger, VW (2002). Valid adjustment of randomized comparisons for binary covariates.  Biometrical Journal 46, p. 589--594.
     
  13. Berger, VW and Weinstein S (2004). Ensuring the comparability of comparison groups: is randomization enough? Controlled Clinical Trials 25, 515--524
     
  14. 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
     
  15. Campbell, G, Yue, L, Penello, G, Barrick M. (2003). Encyclopedia of Biopharmaceutical Statistics  ISBN: 0-8247-4263-X.
     
  16. 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
     
  17. 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).
     
  18. 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.
     
  19. Cox, DR and Wermuth (2004).  Causality: a statistical view International Statistical Review, 72, 285--305.
     
  20. 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.
     
  21. Davis GE Lowell, WE. (2004). Chaotic solar cycles modulate the incidence and severity of mental illness. Medical Hypotheses 62, 207--214.
     
  22. Des Jarlais, DC and Braine N (2004). Editorial: Assessing syringe exchange programs.Addiction 99, p. 1081--1082.
     
  23. Dunn G and Goetghebeur E (2005). Analysing compliance in clinical trials Statistical Methods in Medical Research,  14325--326.
     
  24. 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,  183323--331.
     
  25. 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,  14369--395.
     
  26. 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,  52445.
     
  27. 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.
     
  28. 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.
     
  29. 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.
     
  30. 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,  163371--3380.
     
  31. 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,  14397--415.
     
  32. Greenland S. (2004). Multiple-bias modelling for analysis of observational data. Journal of the Royal Statistical Society: Series A, ,  V168,  267--306.
     
  33. 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.
     
  34. 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.
     
  35. Hen X, Liu MZ, Zhang A (2005). A note on postrandomization adjustment of covariates. Drug Information Journal 39, 373--383.
     
  36. Hill, J. L. (2000). Applications of Innovative Statistical Methodology for the Social Sciences (part 3). Ph. D. Thesis, Department of Statistics, Harvard University.
     
  37. 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.
     
  38. 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.
     
  39. Hollis, S. (2002). A graphical sensitivity analysis for clinical trials with non-ignorable missing binary outcome. Statistics in Medicine 21, 3823--3834
     
  40. Howell, WG (2004). Dynamic selection effects in means-tested, urban school voucher programs. Journal of Policy Analysis and Management 23, p. 225--250.
     
  41. Hudgens, MG, and Gilbert, PB, and Self, SG. (2004). Endpoints in vaccine trials. Statistical Methods in Medical Research. 13, 89--114
     
  42. Huba et al. (2003). Modeling HIV Risk in Highly Vulnerable Youth. Structural Equation Modeling, 10, 583--608.
     
  43. Hudgens MG and Halloran ME (2005). Causal vaccine effects on binary post-infection outcomes. Journal of the American Statistical Association (forthcoming)
     
  44. 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
     
  45. Jessen, Ved Geert (2003). Saeson for selvmordsadfaerd: myter og resultater. Suicidologi, 8, 14-22.
     
  46. Jo, B. (2002).  Statistical power in randomized intervention studies with noncompliance.  Psychological Methods, 178--193.
     
  47. Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Forthcoming in Journal of Educationan and Behavioral Statistics.
     
  48. Joffee, MM, Ten Have TR, Brensinger C. (2003). The compliance score as a regressor in randomized trials. Biostatistics 4, 327--340
     
  49. 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
     
  50. King G and Zeng L. (2006). The dangers of extreme counterfactuals. Political Analysis. forthcoming.
     
  51. 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.
     
  52. 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.
     
  53. 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.
     
  54. Lauritzen, S (2004).  Graphical models for surrogates Scandinavian Journal of Statistics , V 31.
     
  55. 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.
     
  56. 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
     
  57. 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.
     
  58. 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.
     
  59. 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.
     
  60. Matsui, S. (2004). Applications of a parametric model for informative censoring. Biometrics 60, 704--714
     
  61. Matsui, S. (2004). Analysis of times to repeated events in two-arm randomized trials with noncompliance and dependent censoring. Biometrics 60, 965--976
     
  62. Matsuyama, Y. (2002). Correcting for non-compliance of repeated binary outcomes in randomized clinical trials: randomized analysis approach. Statistics in Medicine 21, 675--687
     
  63. 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.
     
  64. 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
     
  65. 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).
     
  66. Muthen, Bengt O. (2002).  Beyond SEM: Generalized latent variable modelling. Behaviormetrika, 21, 81—117.
     
  67. 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.
     
  68. Partonen T, Haukka J, Pirkola S (2004). Time patterns and seasonal mismatch in suicide. Acta Psychiatrica Scandinavica 109, 110-115.
     
  69. Partonen T, Haukka J, Nevanlinna H, et al. (2004). Analysis of the seasonal pattern in suicide Journal of Affective Disorders 81, 13-139.
     
  70. 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
     
  71. 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.
     
  72. Committee on Scientific Principles for Education Research,  RJ Shavelson and LTowne, (Eds), National Research Council. Scientific Research in Education (2002).
     
  73. 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
     
  74. 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.
     
  75. Sheiner LB and Steiner JL. (2000). Pharmacokinetic/pharmacodynamic modeling in drug development. Annual Review of Pharmacology and Toxicology, 40, 67--95.
     
  76. Sheiner LB and Wakefield J. (1999). Population modelling in drug development. Statistical Methods in Medical Research,  8(3):183--193.
     
  77. 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.
     
  78. 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.
     
  79. 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.
     
  80. 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.
     
  81. US Surgeon General's Report (2001).  Women and Smoking. Chapter 4.
     
  82. 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.
     
  83. White, IR. (2005). Uses and limitations of randomization based efficacy estimators.  Statistical Methods in Medical Research. , 96, 1232—1244.
     
  84. Zhang, J (2002). Causal inference with principal stratification. Ph. D. Thesis, Department of Statistics, Harvard University.

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