4. Adjusting for Time-Varying Confounding in Survival Analysis

In this paper we illustrate how directly including endogenous time-varying confounders in the model of the effect of an exposure on a response can lead to bias in discrete time survival analysis. An alternative to this method is Hernán, Brumback, and Robins' (1999) use of sample weights to adjust for endogenous time-varying confounding. We discuss when this method can be used to provide unbiased estimators, and we illustrate the method by addressing a substantive research question using existing survey data. We also critically examine the robustness of the weighting method to violations of the underlying assumptions via a simulation analysis.

[1]  J. Robins,et al.  G-Estimation of the Effect of Prophylaxis Therapy for Pneumocystis carinii Pneumonia on the Survival of AIDS Patients , 1992, Epidemiology.

[2]  J. Barber,et al.  Intergenerational Influences on the Entry into Parenthood: Mothers' Preferences for Family and Nonfamily Behavior , 2000 .

[3]  James M. Robins,et al.  Probabilistic evaluation of sequential plans from causal models with hidden variables , 1995, UAI.

[4]  Maia Berkane.  Latent Variable Modeling and Applications to Causality , 1997 .

[5]  M. Sobel,et al.  Identification Problems in the Social Sciences. , 1996 .

[6]  J. Heckman,et al.  Longitudinal Analysis of Labor Market Data: Alternative methods for evaluating the impact of interventions , 1985 .

[7]  S. Hoffman.  Teenage childbearing is not so bad after all...or is it? A review of the new literature. , 1998, Family planning perspectives.

[8]  J. Davis.  The Logic Of Causal Order , 1987 .

[9]  William G. Axinn,et al.  Innovations in life history calendar applications , 1999 .

[10]  James J. Heckman,et al.  Longitudinal Analysis of Labor Market Data , 1985 .

[11]  Kenneth A. Bollen,et al.  Structural Equations with Latent Variables , 1989 .

[12]  J. Robins,et al.  Adjusting for differential rates of prophylaxis therapy for PCP in high- versus low-dose AZT treatment arms in an AIDS randomized trial , 1994 .

[13]  J. Barber,et al.  Ideational influences on the transition to parenthood : Attitudes toward childbearing and competing alternatives , 2001 .

[14]  William G. Axinn,et al.  The Effects of Children's Schooling on Fertility Limitation , 1993 .

[15]  W. Mason.  Freedman is Right as Far as He Goes, but There is More, and It's Worse. Statisticians Could Help , 1991 .

[16]  O. D. Duncan.  Path Analysis: Sociological Examples , 1966, American Journal of Sociology.

[17]  M. Sobel,et al.  Causal Inference in Sociological Studies , 2004 .

[18]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[19]  J. Pearl,et al.  Causal diagrams for epidemiologic research. , 1999, Epidemiology.

[20]  S. Hofferth,et al.  The effects of early childbearing on schooling over time. , 2001, Family planning perspectives.

[21]  Christopher Winship,et al.  THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA , 1999 .

[22]  Andrew Abbott,et al.  The Causal Devolution , 1998 .

[23]  D. Freedman.  Statistical models and shoe leather , 1989 .

[24]  J. Robins.  A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. , 1987, Journal of chronic diseases.

[25]  J. Robins,et al.  Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.

[26]  D. Alwin,et al.  The life history calendar: a technique for collecting retrospective data. , 1988, Sociological methodology.

[27]  James M. Robins,et al.  Association, Causation, And Marginal Structural Models , 1999, Synthese.

[28]  James M. Robins,et al.  Marginal Structural Models versus Structural nested Models as Tools for Causal inference , 2000 .

[29]  J. Pearl.  Graphs, Causality, and Structural Equation Models , 1998 .

[30]  B. Singer,et al.  Causality in the Social Sciences , 1988 .

[31]  Michael E. Sobel,et al.  Causal Inference in the Social Sciences , 2000 .

[32]  J. Robins.  A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .

[33]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[34]  Daniel S. Nagin,et al.  3. Bounding Disagreements about Treatment Effects: A Case Study of Sentencing and Recidivism , 1998 .

[35]  Herbert L. Smith.  SPECIFICATION PROBLEMS IN EXPERIMENTAL AND NONEXPERIMENTAL SOCIAL RESEARCH , 1990 .

[36]  W G Axinn,et al.  7. The Neighborhood History Calendar: A Data Collection Method Designed for Dynamic Multilevel Modeling , 1997, Sociological methodology.

[37]  G. Bohrnstedt,et al.  Statistics for social data analysis , 1982 .

[38]  Hubert M. Blalock,et al.  ARE THERE REALLY ANY CONSTRUCTIVE ALTERNATIVES TO CAUSAL MODELING , 1991 .

[39]  William G. Axinn,et al.  The influence of school enrollment and accumulation on cohabitation and marriage in early adulthood. , 1995 .

[40]  D. Relles,et al.  Theory Testing in a World of Constrained Research Design , 1990 .

[41]  W. Axinn,et al.  Mass Education and Fertility Transition , 2001, American Sociological Review.

[42]  J. Robins,et al.  Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. , 2000, Epidemiology.

[43]  James M. Robins,et al.  Causal Inference from Complex Longitudinal Data , 1997 .

[44]  Stanley Lieberson,et al.  Making It Count: The Improvement of Social Research and Theory. , 1987 .

[45]  D. Berry,et al.  Statistical models in epidemiology, the environment, and clinical trials , 2000 .