SEARCH

SEARCH BY CITATION

References

  • Amari, S. (1985) Differential-geometric methods in statistics. Lect. Notes Statist., 28.
  • Baker, S. G. and Laird, N. M. (1988) Regression models for categorical survey variables with outcome subject to nonignorable nonresponse. J. Am. Statist. Ass., 83, 6269.
  • Chambers, R. L. and Welsh, A. H. (1993) Log-linear models for survey data with non-ignorable non-response. J. R. Statist. Soc. B, 55, 157170.
  • Copas, J. (1999) What works?: selectivity models and meta-analysis. J. R. Statist. Soc. A, 162, 95109.
  • Copas, J. and Eguchi, S. (2001) Local sensitivity approximations for selectivity bias. J. R. Statist. Soc. B, 63, 871895.
  • Copas, J. B. and Jackson, D. (2004) A bound for publication bias based on the number of unpublished studies. Biometrics, 60, 146153.
  • Copas, J. B. and Li, H. G. (1997) Inference for non-random samples (with discussion). J. R. Statist. Soc. B, 59, 5595.
  • Copas, J. B. and Shi, J. Q. (2000a) Reanalysis of epidemiological evidence on lung cancer and passive smoking. Br. Med. J., 320, 417418.
  • Copas, J. B. and Shi, J. Q. (2000b) Meta-analysis, funnel plots and sensitivity analysis. Biostatistics, 1, 247262.
  • Copas, J. B. and Shi, J. Q. (2001) A sensitivity analysis for publication bias in systematic reviews. Statist. Meth. Med. Res., 10, 115.
  • Cox, D. R. (1972) Regression models and life-tables (with discussion). J. R. Statist. Soc. B, 34, 187220.
  • Crowder, M. (1994) Identifiability crises in competing risks. Int. Statist. Rev., 62, 379391.
  • Crowder, M. (2001) Classical Competing Risks. London: Chapman and Hall–CRC.
  • Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. B, 39, 138.
  • Department of Health (1998) Report of the Scientific Committee on Tobacco and Health (the SCOTH Report). London: Stationery Office.
  • DerSimonian, R. and Laird, N. (1986) Meta-analysis in clinical trials. Contr. Clin. Trials, 7, 177188.
  • Diggle, P. and Kenward, M. G. (1994) Informative drop-out in longitudinal data analysis (with discussion). Appl. Statist., 43, 4993.
  • Draper, D. (1995) Assessment and propagation of model uncertainty (with discussion). J. R. Statist. Soc. B, 57, 4597.
  • Duval, S. and Tweedie, R. (2000) A nonparametric ‘trim and fill’ method for accounting for publication bias in meta-analysis. J. Am. Statist. Ass., 95, 8998.
  • Egger, M., Smith, G. D., Schneider, M. and Minder, C. (1997) Bias in meta-analysis detected by a simple graphical test. Br. Med. J., 315, 629634.
  • Forster, J. J. and Smith, P. W. F. (1998) Model-based inference for categorical survey data subject to non-ignorable non-response. J. R. Statist. Soc. B, 60, 5770.
  • Givens, G. H., Smith, D. D. and Tweedie, R. L. (1997) Publication bias in meta-analysis: a Bayesian data augmentation approach to account for issues exemplified in the passive smoking debate. Statist. Sci., 12, 221250.
  • Goetghebeur, E. and Lapp, K. (1997) The effect of treatment compliance in a placebo-controlled trial: regression with unpaired data. Appl. Statist., 46, 351364.
  • Greenhouse, J. and Iyengar, S. (1994) Sensitivity analysis and diagnostics. In Handbook of Research Synthesis (eds H.Cooper and L.Hedges). New York: Sage.
  • Greenland, S. (2003) The impact of prior distributions for uncontrolled confounding and response bias: a case study of the relation of wire codes and magnetic fields to childhood leukemia. J. Am. Statist. Ass., 98, 4754.
  • Greenland, S. (2005) Multiple-bias modelling for analysis of observational data (with discussion). J. R. Statist. Soc. A, 168, 267306.
  • Gustafson, P. (2001) On measuring sensitivity to parametric model misspecification. J. R. Statist. Soc. B, 63, 8194.
  • Gustafson, P. (2002) On the simultaneous effects of model misspecification and errors in variables. Can. J. Statist., 30, 463474.
  • Hackshaw, A. K., Law, M. R. and Wald, N. J. (1997) The accumulated evidence on lung cancer and environmental tobacco smoke. Br. Med. J., 315, 980988.
  • Heckman, J. J. (1979) Sample selection bias as a specification error. Econometrica, 47, 153161.
  • Heckman, J. J. and Honore, B. E. (1989) The identifiability of the competing risks model. Biometrika, 76, 325330.
  • Hedges, L. V. (1984) Estimation of effect size under non random sampling: the effects of censoring studies yielding statistically insignificant mean differences. J. Educ. Statist., 9, 6185.
  • Heitjan, D. F. and Rubin, D. B. (1991) Ignorability and coarse data. Ann. Statist., 19, 22442253.
  • Holland, P. W. (1986) Statistics and causal inference. J. Am. Statist. Ass., 81, 945968.
  • Horton, N. J. and Fitzmaurice, G. M. (2002) Maximum likelihood estimation of bivariate logistic models for incomplete responses with indicators of ignorable and non-ignorable missingness. Appl. Statist., 51, 281295.
  • Jacobsen, M. and Keiding, N. (1995) Coarsening at random in general sample spaces and random censoring in continuous time. Ann. Statist., 23, 774786.
  • Lane, D. M. and Dunlap, W. P. (1978) Estimating effect size: bias resulting from the significance criterion in editorial decisions. Br. J. Math. Statist. Psychol., 31, 107112.
  • Lawless, J. F. (2003) Statistical Models and Methods for Lifetime Data, 2nd edn. New York: Wiley.
  • Little, R. J. A. (1985) A note about models for selectivity bias. Econometrica, 53, 14691474.
  • Little, R. J. A. (1995) Modelling the dropout mechanism in repeated-measures studies. J. Am. Statist. Ass., 90, 11121121.
  • Little, R. J. A. and Rubin, D. B. (2002) Statistical Analysis with Missing Data, 2nd edn. New York: Wiley.
  • Lu, G. and Copas, J. B. (2004) Missing at random, likelihood ignorability and model completeness. Ann. Statist., 32, 754765.
  • Moeschberger, M. L. and Klein, J. P. (1995) Statistical methods for dependent competing risks. Lifetime Data Anal., 1, 195204.
  • Nilsson, R. (2001) Environmental tobacco smoke revisited: the reliability of the data used for risk assessment. Risk Assessmnt, 21, 373375.
  • Park, T. and Brown, M. B. (1994) Models for categorical data with nonignorable nonresponse. J. Am. Statist. Ass., 89, 4452.
  • Pearl, J. (2000) Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.
  • Rosenbaum, P. R. (2002) Observational Studies, 2nd edn. New York: Springer.
  • Rosenbaum, P. R. (2004) Design sensitivity in observational studies. Biometrika, 91, 153164.
  • Royall, R. and Tsou, T.-S. (2003) Interpreting statistical evidence by using imperfect models: robust adjusted likelihood functions. J. R. Statist. Soc. B, 65, 391404.
  • Rubin, D. B. (1974) Estimating causal effects of treatments in randomized and non-randomized studies. J. Educ. Psychol., 66, 688701.
  • Rubin, D. B. (1977) Formalizing subjective notions about the effect of nonrespondents in sample surveys. J. Am. Statist. Ass., 72, 538543.
  • Schafer, J. L. (1997) Analysis of Incomplete Multivariate Data. London: Chapman and Hall–CRC.
  • Scharfstein, O. S., Daniels, M. J. and Robins, J. M. (2003) Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes. Biostatistics, 4, 495512.
  • Scharfstein, O. S. and Robins, J. M. (2002) Estimation of the failure time distribution in the presence of informative censoring. Biometrika, 89, 617634.
  • Scharfstein, O. S., Rotnitzky, A. and Robins, J. M. (1999) Adjusting for non-ignorable drop-out using semiparametric nonresponse models (with discussion). J. Am. Statist. Ass., 94, 10961146.
  • Sen, P. K. (1985) Locally optimal statistical tests. In Encyclopedia of Statistical Sciences (eds S.Kotz and N. L.Johnson), vol. 5, pp. 95100. New York: Wiley.
  • Shi, J. Q. and Copas, J. (2002) Publication bias and meta analysis for 2×2 tables: an average Markov chain Monte Carlo EM algorithm. J. R. Statist. Soc. B, 64, 221236.
  • Siannis, F., Copas, J. B. and Lu, G. (2005) Sensitivity analysis for informative censoring in parametric survival models. Biostatistics, 6, 7791.
  • Sutton, A. J., Abrams, K. R., Jones, D. R. and Sheldon, T. A. (2000a) Methods for Meta-analysis in Medical Research. Chichester: Wiley.
  • Sutton, A. J., Song, F., Gilbody, S. M. and Jones, D. R. (2000b) Modelling publication bias in meta-analysis: a review. Statist. Meth. Med. Res., 9, 421445.
  • Tang, G., Little, R. J. A. and Raghunathan, T. E. (2003) Analysis of multivariate missing data with nonignorable nonresponse. Biometrika, 90, 747764.
  • Tsiatis, A. (1972) A nonidentifiability aspect of the problem of competing risks. Proc. Natn. Acad. Sci. USA, 72, 2022.
  • White, H. (1982) Maximum likelihood estimation of misspecified models. Econometrica, 50, 125.
  • White, I. R. and Pocock, S. J. (1996) Statistical reporting of clinical trials with individual changes from allocated treatment. Statist. Med., 15, 249262.

References in the discussion

  • Aït-Sahalia, Y. (2002) Maximum likelihood estimation of discretely sampled diffusions: a closed-form approximation approach. Econometrica, 70, 223262.
  • An, H. (2004) Robust likelihood-based inference for multivariate data with missing values. Doctoral Dissertation. Department of Biostatistics, University of Michigan, Ann Arbor.
  • Baker, S. G. and Laird, N. M. (1988) Regression analysis for categorical variables with outcome subject to nonignorable nonresponse. J. Am. Statist. Ass., 83, 6269.
  • Balke, A. and Pearl, J. (1997) Bounds on treatment effects from studies with imperfect compliance. J. Am. Statist. Ass., 92, 11711177.
  • Barndorff-Nielsen, O. E. and Sørensen, M. (1994) A review of some aspects of asymptotic likelihood theory for stochastic processes. Int. Statist. Rev., 62, 133165.
  • Bates, R. A., Buck, R. J., Riccomagno, E. and Wynn, H. P. (1996) Experimental design and observation for large systems. J. R. Statist. Soc. B, 58, 7794.
  • Berk, R. A. (2004) Regression Analysis: a Constructive Critique. Newbury Park: Sage.
  • Bladt, M. and Sørensen, M. (2005) Statistical inference for discretely observed Markov jump processes. J. R. Statist. Soc. B, 67, 395410.
  • Box, G. E. P. (1979) Some problems of statistics and everyday life. J. Am. Statist. Ass., 74, 14.
  • Box, G. E. P. and Wilson, K. B. (1951) On the experimental attainment of optimum conditions (with discussion). J. R. Statist. Soc. B, 13, 145.
  • Burnham, K. P. and Anderson, D. R. (2002) Model Selection and Multi-model Inference, 2nd edn. New York: Springer.
  • Carroll, R. J., Ruppert, D. and Stefanski, L. A. (1995) Measurement Error in Nonlinear Models. London: Chapman and Hall.
  • Chatfield, C. (1995) Model uncertainty, data mining and statistical inference (with discussion). J. R. Statist. Soc. A, 158, 419466.
  • Chatfield, C. (2001) Time-series Forecasting. Boca Raton: Chapman and Hall–CRC.
  • Chesher, A. (1991) The effect of measurement error. Biometrika, 78, 451462.
  • Clarke, P. S. and Smith, P. W. F. (2003) Results on point estimation for log-linear models with non-ignorable non-response. Methodological Working Paper M03/23. Southampton Statistical Sciences Institute, University of Southampton, Southampton.
  • Clarke, P. S. and Smith, P. W. F. (2004) Interval estimation for log-linear models with one variable subject to non-ignorable non-response. J. R. Statist. Soc. B, 66, 357368.
  • Coombs, C. H. (1964) A Theory of Data. New York: Wiley.
  • Copas, J. and Eguchi, S. (2001) Local sensitivity approximations for selectivity bias. J. R. Statist. Soc. B, 63, 871895.
  • Copas, J. B. and Jackson, D. (2004) A bound for publication bias based on the number of unpublished studies. Biometrics, 60, 146153.
  • Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Statist. Soc. B, 39, 138.
  • Draper, D. (1995) Assessment and propagation of model uncertainty (with discussion). J. R. Statist. Soc. B, 57, 4597.
  • Draper, D., Gaver, D., Goel, P., Greenhouse, J., Hedges, L., Morris, C., Tucker, J. and Waternaux, C. (1993) Combining Information: Statistical Issues and Opportunities for Research. Alexandria: American Statistical Association.
  • Fedorov, V. V. (1972) Theory of Optimal Experiments. New York: Acadamic Press.
  • Forster, J. J. and Smith, P. W. F. (1998) Model-based inference for categorical survey data subject to non-ignorable non-response (with discussion). J. R. Statist. Soc. B, 60, 5770.
  • Fuller, W. A. (1987) Measurement Error Models. New York: Wiley.
  • Greenland, S. (2005) Multiple-bias modelling for analysis of observational data (with discussion). J. R. Statist. Soc. A, 168, 267306.
  • Greenland, S., Pearl, J. and Robins, J. M. (1999) Casual diagrams for epidemiologic research. Epidemiology, 10, 3748.
  • Gustafson, P. (2002) On the simultaneous effects of model misspecification and errors in variables. Can. J. Statist., 30, 463474.
  • Hackshaw, A. K., Law, M. R. and Wald, N. J. (1997) The accumulated evidence on lung cancer and environmental tobacco smoke. Br. Med. J., 315, 980988.
  • Heckman, J. and Vytlacil, E. (1999) Local instrumental variables and latent variable models for identifying and bounding treatment effects. Proc. Natn. Acad. Sci. USA, 96, 47304734.
  • Henrion, M. and Fischhoff, B. (1986) Assessing uncertainty in physical constants. Am. J. Phys., 54, 791798.
  • Herzberg, A. M. and Andrews, D. F. (1976) Some considerations in the optimal design of experiments in non-optimal situations. J. R. Statist. Soc. B, 38, 284289.
  • Horowitz, J. and Manski, C. (2000) Nonparametric analysis of randomized experiments with missing covariate and outcome data. J. Am. Statist. Ass., 95, 7784.
  • Jacod, J. and Mémin, J. (1976) Caractéristiques locales et condition de continuité absolue pour les semi-martingales. Z. Wahrsch. Ver. Geb., 35, 137.
  • Küchler, U. and Sørensen, M. (1997) Exponential Families of Stochastic Processes. New York: Springer.
  • Law, M. R., Morris, J. K. and Wald, N. J. (1997) Environmental tobacco smoke exposure and ischaemic heart disease: an evaluation of the evidence. Br. Med. J., 315, 973980.
  • Leeb, H. and Pötscher, B. M. (2003) The finite-sample distribution of post-model-selection estimators and uniform versus nonuniform approximations. Econometr. Theory, 19, 100142.
  • Lin, H., Scharfstein, D. O. and Rosenheck, R. A., (2004) Analysis of longitudinal data with irregular, outcome-dependent follow-up. J. R. Statist. Soc. B, 66, 791813.
  • Lipsitz, S. R., Fitzmaurice, G. M., Ibrahim, J. G., Gelber, R. and Lipshultz, S. (2002) Parameter estimation in longitudinal studies with outcome-dependent follow-up. Biometrics, 58, 621630.
  • Little, R. J. A. and An, H. (2004) Robust likelihood-based analysis of multivariate data with missing values. Statist. Sin., 14, 949968.
  • Little, R. J. A. and Rubin, D. B. (2002) Statistical Analysis with Missing Data, 2nd edn. New York: Wiley.
  • Little, R. J. A. and Wang, Y.-X. (1996) Pattern-mixture models for multivariate incomplete data with covariates. Biometrics, 52, 98111.
  • Longford, N. T. (2003) An alternative to model selection in ordinary regression. Statist. Comput., 13, 391406.
  • Madigan, D. and Raftery, A. E. (1994) Model selection and accounting for model uncertainty in graphical models using Occam's window. J. Am. Statist. Ass., 89, 15351546.
  • Manski, C. (1989) Anatomy of the selection problem. J. Hum. Res., 24, 343360.
  • Manski, C. (1990) Nonparametric bounds on treatment effects. Am. Econ. Rev. Pap. Proc., 80, 319323.
  • Manski, C. (1995) Identification Problems in the Social Sciences. Cambridge: Harvard University Press.
  • Manski, C. (2003) Partial Identification of Probability Distributions. New York: Springer.
  • Manski, C., Sandefur, G., McLanahan, S. and Powers, D. (1992) Alternative estimates of the effect of family structure during adolescence on high school graduation. J. Am. Statist. Ass., 87, 2537.
  • Moses, L. E. (1969) The National Halothane Study, part IV. Bethesda: National Institutes of Health.
  • Pötscher, B. M. (1991) Effects of model selection on inference. Econometr. Theory, 7, 163185.
  • Raftery, A. E. (1996) Approximate Bayes factors and accounting for model uncertainty in generalised linear models. Biometrika, 83, 251266.
  • Rosenbaum, P. R. (1995) Observational Studies. New York: Springer.
  • Rosenbaum, P. R. (2001) Replicating effects and biases. Am. Statistn, 55, 223227.
  • Rosenbaum, P. R. (2002) Observational Studies, 2nd edn. New York: Springer.
  • Royall, R. and Tsou, T.-S. (2003) Interpreting statistical evidence by using imperfect models: robust adjusted likelihood functions. J. R. Statist. Soc. B, 65, 391404.
  • Rubin, D. B. (1976) Inference and missing data (with discussion). Biometrika, 63, 581592.
  • Rubin, D. B. (1977) Formalizing subjective notions about the effect of nonrespondents in sample surveys. J. Am. Statist. Ass., 72, 538543.
  • Salzberg, A. J. (1999) Removable selection bias in quasi-experiments. Am. Statistn, 53, 103107.
  • Shadish, W. R., Cook, T. D. and Campbell, D. T. (2002) Experimental and Quasi-experimental Designs for Generalized Causal Inference. Boston: Houghton-Mifflin.
  • Skinner, C. J. and Humphreys, K. (1999) Weibull regression for lifetimes measured with error. Liftime Data Anal., 5, 2337.
  • Troxel, A., Ma, G. and Heitjan, D. F. (2004) An index of local sensitivity to nonignorability. Statist. Sin., 14, 12211237.
  • Verbeke, G., Molenberghs, G., Thijs, H., Lesaffre, E. and Kenward, M. G. (2001) Sensitivity analysis for non-random dropout: a local influence approach. Biometrics, 57, 714.