A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome
| Autor: | Kirsten J. Lum, Jason Roy, Michael J. Daniels |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Statistics and Probability Computer science Bayesian probability Marginal structural model HIV Infections 01 natural sciences 010104 statistics & probability 03 medical and health sciences Bayes' theorem Statistics Econometrics Humans Cognitive Dysfunction 0101 mathematics Antihypertensive Agents Causal model Models Statistical Bayes Theorem Articles General Medicine Survival Analysis Dirichlet process 030104 developmental biology Inverse probability Data Interpretation Statistical Causal inference Parametric model Statistics Probability and Uncertainty |
| Zdroj: | Biostatistics. 18:32-47 |
| ISSN: | 1468-4357 1465-4644 |
| DOI: | 10.1093/biostatistics/kxw029 |
| Popis: | Marginal structural models (MSMs) are a general class of causal models for specifying the average effect of treatment on an outcome. These models can accommodate discrete or continuous treatments, as well as treatment effect heterogeneity (causal effect modification). The literature on estimation of MSM parameters has been dominated by semiparametric estimation methods, such as inverse probability of treatment weighted (IPTW). Likelihood-based methods have received little development, probably in part due to the need to integrate out confounders from the likelihood and due to reluctance to make parametric modeling assumptions. In this article we develop a fully Bayesian MSM for continuous and survival outcomes. In particular, we take a Bayesian nonparametric (BNP) approach, using a combination of a dependent Dirichlet process and Gaussian process to model the observed data. The BNP approach, like semiparametric methods such as IPTW, does not require specifying a parametric outcome distribution. Moreover, by using a likelihood-based method, there are potential gains in efficiency over semiparametric methods. An additional advantage of taking a fully Bayesian approach is the ability to account for uncertainty in our (uncheckable) identifying assumption. To this end, we propose informative prior distributions that can be used to capture uncertainty about the identifying “no unmeasured confounders” assumption. Thus, posterior inference about the causal effect parameters can reflect the degree of uncertainty about this assumption. The performance of the methodology is evaluated in several simulation studies. The results show substantial efficiency gains over semiparametric methods, and very little efficiency loss over correctly specified maximum likelihood estimates. The method is also applied to data from a study on neurocognitive performance in HIV-infected women and a study of the comparative effectiveness of antihypertensive drug classes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|