Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Causal inference (2)
- Clinical trials (2)
- Survival data (2)
- Accelerated hazards model (1)
- Action onset (1)
-
- Body composition (1)
- Change point (1)
- Clinical trial (1)
- Cure model (1)
- Diagnostic (1)
- Double robustness (1)
- Efficient score (1)
- Functional limitation (1)
- Generalized estimating equations (1)
- Hazard functions (1)
- Instrumental variables (1)
- Inverse Probability of Treatment Weighted Estimator (IPTW) (1)
- Inverse Probability of Treatment causal inference (1)
- Latent variable (1)
- Locally efficient (1)
- Longitudinal outcomes (1)
- Longitudinal study (1)
- Marginal Structural Models (MSMs) (1)
- Marginal structural models (1)
- Mean response model (1)
- Mixture model (1)
- Nuisance parameters (1)
- Observational studies (1)
- Omnibus test (1)
- Physical activity (1)
Articles 1 - 6 of 6
Full-Text Articles in Statistical Models
Mean Response Models Of Repeated Measurements In Presence Of Varying Effectiveness Onset, Ying Qing Chen, Su-Chun Cheng
Mean Response Models Of Repeated Measurements In Presence Of Varying Effectiveness Onset, Ying Qing Chen, Su-Chun Cheng
U.C. Berkeley Division of Biostatistics Working Paper Series
Repeated measurements are often collected over time to evaluate treatment efficacy in clinical trials. Most of the statistical models of the repeated measurements have been focusing on their mean response as function of time. These models usually assume that the treatment has persistent effect of constant additivity or multiplicity on the mean response functions throughout the observation period of time. In reality, however, such assumption may be confounded by the potential existence of the so-called effectiveness action onset, although they are often unobserved or difficult to obtain. Instead of including nonparametric time-varying coefficients in the mean response models, we propose …
Comparison Of The Inverse Probability Of Treatment Weighted (Iptw) Estimator With A Naïve Estimator In The Analysis Of Longitudinal Data With Time-Dependent Confounding: A Simulation Study, Thaddeus Haight, Romain Neugebauer, Ira B. Tager, Mark J. Van Der Laan
Comparison Of The Inverse Probability Of Treatment Weighted (Iptw) Estimator With A Naïve Estimator In The Analysis Of Longitudinal Data With Time-Dependent Confounding: A Simulation Study, Thaddeus Haight, Romain Neugebauer, Ira B. Tager, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional regression, and an IPTW (Inverse Probability of Treatment Weighted) estimator, to true causal parameters for a given MSM (Marginal Structural Model). The study was extracted from a larger epidemiological study (Longitudinal Study of Effects of Physical Activity and Body Composition on Functional Limitation in the Elderly, by Tager et. al [accepted, Epidemiology, September 2003]), which examined the causal effects of physical activity and body composition on functional limitation. The simulation emulated the larger study in terms of the exposure and outcome variables of interest-- physical …
Measuring Treatment Effects Using Semiparametric Models, Zhuo Yu, Mark J. Van Der Laan
Measuring Treatment Effects Using Semiparametric Models, Zhuo Yu, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
In order to estimate the causal effect of treatments on an outcome of interest, one has to account for the effect of confounding factors which covary with the treatments and also contribute to the outcome of interest. In this paper, we use the semiparametric regression model to estimate the causal parameters. We assume the causal effect of the treatments can be described by the parametric component of the semiparametric regression model. Following the general methodology which was developed in van der Laan and Robins (2002) we give the orthogonal complement of the nuisance tangent space which identifies all the estimating …
Estimating Causal Parameters In Marginal Structural Models With Unmeasured Confounders Using Instrumental Variables, Tanya A. Henneman, Mark Johannes Van Der Laan, Alan E. Hubbard
Estimating Causal Parameters In Marginal Structural Models With Unmeasured Confounders Using Instrumental Variables, Tanya A. Henneman, Mark Johannes Van Der Laan, Alan E. Hubbard
U.C. Berkeley Division of Biostatistics Working Paper Series
For statisticians analyzing medical data, a significant problem in determining the causal effect of a treatment on a particular outcome of interest, is how to control for unmeasured confounders. Techniques using instrumental variables (IV) have been developed to estimate causal parameters in the presence of unmeasured confounders. In this paper we apply IV methods to both linear and non-linear marginal structural models. We study a specific class of generalized estimating equations that is appropriate to these data, and compare the performance of the resulting estimator to the standard IV method, a two-stage least squares procedure. Our results are applied to …
Mixture Hazards Models With Additive Random Effects Accounting For Treatment Effectiveness Lag Time, Ying Qing Chen, C. A. Rohde, M.-C. Wang
Mixture Hazards Models With Additive Random Effects Accounting For Treatment Effectiveness Lag Time, Ying Qing Chen, C. A. Rohde, M.-C. Wang
U.C. Berkeley Division of Biostatistics Working Paper Series
In many clinical trials to evaluate treatment efficacy, it is believed that there may exist latent treatment effectiveness lag times after which medical treatment procedure or chemical compound would be in full effect. In this article, semiparametric regression models are proposed and studied for estimating the treatment effect accounting for such latent lag times. The new models take advantage of the invariance property of the additive hazards model in marginalising over an additive latent variable; parameters in the models are thus easily estimated and interpreted, while the flexibility of not having to specify the baseline hazard function is preserved. Monte …
A Class Of Semiparametric Scale-Change Hazards Regression Models And Its Adequacy For Censored Survival Data, Ying Qing Chen
A Class Of Semiparametric Scale-Change Hazards Regression Models And Its Adequacy For Censored Survival Data, Ying Qing Chen
U.C. Berkeley Division of Biostatistics Working Paper Series
A class of semiparametric hazards regression models called the accelerated hazards models was introduced to identify the covariate effect characterized by the scale-change between hazard functions. In this article, we compare the accelerated hazards models with several other popular classes of regression models in statistical literature for censored survival data. We also propose and study some test statistics to assess the models' adequacy. Simulation studies are conducted to evaluate the performance of the test statistics. Actual clinical trials data are analyzed to demonstrate the proposed models and test statistics.