Technical Reports Available: Statistical Methods for Health Policy and Management Research.
In Spring 2013 semester, five doctoral students in Health Policy and Management researched, wrote, presented, and reviewed their work and others on specific statistical methods for health policy and management research.
The final products are five technical reports, the first for the HPM department. The students became ‘classroom experts’ on these topics and developed research, writing, and teaching skills from their work for this class. The reports contain examples and categorical references (indicating material for beginners and those with some knowledge of the topic). The technical reports eventually will be placed on our department’s website. Until then, if you want an electronic version of a report, please contact Cindy L. Christiansen, Associate Professor HPM, cindyLc@bu.edu
Jake Morgan
Classification and Regression Tree Analysis
Technical Report #1; HPM, BUSPH
Executive Summary
Classification and Regression Tree Analysis, CART, is a simple yet powerful analytic tool that helps determine the most important” (based on explanatory power) variables in a particular dataset, and can help researchers craft a potent explanatory model. This technical report will outline a brief background of CART followed by practical applications and suggested implementation strategies in R. A short example will demonstrate the potential usefulness of CART in a research setting.
Meng-Yun Lin
Bayesian Statistics
Technical Report #2; ; HPM, BUSPH
Executive Summary
This report is a brief introduction of Bayesian statistics. The first section describes the basic concepts of Bayesian approach and how they are applied to statistical estimation and hypothesis testing. The next section presents the statistical modeling using Bayesian approach. It first explains the main components of Bayes model including prior, likelihood function, and posterior. Then, it introduces informative and non-informative Bayes models. The last section provides an example of fitting Bayesian logistic regression in SAS. It illustrates how to program Bayes model and how to check model convergence.
Kyung Min Lee
Marginal structural modeling in health services research
Technical Report #3; HPM, BUSPH
Executive Summary
Statistical inferences from observational studies are often subject to confounding caused by both observed and unobserved confounding variables. Conventional methods for controlling for confounding include applying statistical techniques such as stratification and multivariable regression analysis. In the presence of time-dependent confounders, however, such techniques may still lead to biased estimates. Marginal structural modeling (MSM) uses a multi-step estimation strategy to separate confounding control from the estimation of the parameters of interest, allowing the investigator to obtain unbiased estimates. Given that there are no unmeasured confounders and the probability of treatment is positive, the estimates of a marginal structural model can be interpreted as causal. This report serves as a starting point for researchers who wish to use MSM in their studies, providing an overview of the theory behind MSM and guidance for its implementation.
Marina Soley-Bori
Dealing with missing data: Key assumptions and methods for applied analysis
Technical Report #4; ; HPM, BUSPH
Executive Summary
This tech report presents the basic concepts and methods used to deal with missing data. After explaining the missing data mechanisms and the patterns of missingness, the main conventional methodologies are reviewed, including Listwise deletion, Imputation methods, Multiple Imputation, Maximum Likelihood and Bayesian methods. Advantages and limitations are specified so that the reader is able to identify the main trade-offs when using each method. The report also summarizes how to carry out Multiple Imputation and Maximum Likelihood using SAS and STATA.
Michal Horný
Bayesian Networks
Technical Report #5; HPM, BUSPH
Executive Summary
A Bayesian network is a representation of a joint probability distribution of a set of random variables with a possible mutual causal relationship. The network consists of nodes representing the random variables, edges between pairs of nodes representing the causal relationship of these nodes, and a conditional probability distribution in each of the nodes. The main objective of the method is to model the posterior conditional probability distribution of outcome (often causal) variable(s) after observing new evidence. Bayesian networks may be constructed either manually with knowledge of the underlying domain, or automatically from a large dataset by appropriate software.