Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data

Comment on ``Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data'' [arXiv:0804.2958]Comment: Published in at http://dx.doi.org/10.1214/07-STS227C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Controlling for individual heterogeneity in longitudinal models, with applications to student achievement

Longitudinal data tracking repeated measurements on individuals are highly valued for research because they offer controls for unmeasured individual heterogeneity that might otherwise bias results. Random effects or mixed models approaches, which treat individual heterogeneity as part of the model error term and use generalized least squares to estimate model parameters, are often criticized because correlation between unobserved individual effects and other model variables can lead to biased and inconsistent parameter estimates. Starting with an examination of the relationship between random effects and fixed effects estimators in the standard unobserved effects model, this article demonstrates through analysis and simulation that the mixed model approach has a ``bias compression'' property under a general model for individual heterogeneity that can mitigate bias due to uncontrolled differences among individuals. The general model is motivated by the complexities of longitudinal student achievement measures, but the results have broad applicability to longitudinal modeling.Comment: Published at http://dx.doi.org/10.1214/07-EJS057 in the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Missing data in value-added modeling of teacher effects

The increasing availability of longitudinal student achievement data has heightened interest among researchers, educators and policy makers in using these data to evaluate educational inputs, as well as for school and possibly teacher accountability. Researchers have developed elaborate "value-added models" of these longitudinal data to estimate the effects of educational inputs (e.g., teachers or schools) on student achievement while using prior achievement to adjust for nonrandom assignment of students to schools and classes. A challenge to such modeling efforts is the extensive numbers of students with incomplete records and the tendency for those students to be lower achieving. These conditions create the potential for results to be sensitive to violations of the assumption that data are missing at random, which is commonly used when estimating model parameters. The current study extends recent value-added modeling approaches for longitudinal student achievement data Lockwood et al. [J. Educ. Behav. Statist. 32 (2007) 125--150] to allow data to be missing not at random via random effects selection and pattern mixture models, and applies those methods to data from a large urban school district to estimate effects of elementary school mathematics teachers. We find that allowing the data to be missing not at random has little impact on estimated teacher effects. The robustness of estimated teacher effects to the missing data assumptions appears to result from both the relatively small impact of model specification on estimated student effects compared with the large variability in teacher effects and the downweighting of scores from students with incomplete data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS405 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Analysis of rolling group therapy data using conditionally autoregressive priors

Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community's ability to understand how the dynamics of rolling groups lead to client outcomes.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS434 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Relationship between Exposureto Class Size Reductionand Student Achievementin California

The CSR Research Consortium has been evaluating the implementation of the Class Size Reduction initiative in California since 1998. Initial reports documented the implementation of the program and its impact on the teacher workforce, the teaching of mathematics and Language Arts, parental involvement and student achievement. This study examines the relationship between student achievement and the number of years students have been exposed to CSR in grades K-3. The analysis was conducted at the grade level within schools using student achievement data collected in 1998-2001. Archival data collected by the state were used to establish CSR participation by grade for each school in the state. Most students had one of two patterns of exposure to CSR, which differed by only one year during grade K-3. The analysis found no strong association between achievement and exposure to CSR for these groups, after controlling for pre-existing differences in the groups

An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse treatment programs

Participants in longitudinal studies on the effects of drug treatment and criminal justice system interventions are at high risk for institutionalization (e.g., spending time in an environment where their freedom to use drugs, commit crimes, or engage in risky behavior may be circumscribed). Methods used for estimating treatment effects in the presence of institutionalization during follow-up can be highly sensitive to assumptions that are unlikely to be met in applications and thus likely to yield misleading inferences. In this paper we consider the use of principal stratification to control for institutionalization at follow-up. Principal stratification has been suggested for similar problems where outcomes are unobservable for samples of study participants because of dropout, death or other forms of censoring. The method identifies principal strata within which causal effects are well defined and potentially estimable. We extend the method of principal stratification to model institutionalization at follow-up and estimate the effect of residential substance abuse treatment versus outpatient services in a large scale study of adolescent substance abuse treatment programs. Additionally, we discuss practical issues in applying the principal stratification model to data. We show via simulation studies that the model can only recover true effects provided the data meet strenuous demands and that there must be caution taken when implementing principal stratification as a technique to control for post-treatment confounders such as institutionalization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS179 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Marijuana Use and High School Dropout: The Influence of Unobservables

In this study we reconsider the relationship between heavy and persistent marijuana use and high school dropout status using a unique prospective panel study of over 4500 7th grade students from South Dakota who are followed up through high school. Propensity score weighting is used to adjust for baseline differences that are found to exist before marijuana initiation occurs (7th grade). Weighted logistic regression incorporating these propensity score weights is then used to examine the extent to which time-varying factors, including substance use, also influence the likelihood of dropping out of school. We find a positive association between marijuana use and dropping out (OR=5.68), over half of which can be explained by prior differences in observational characteristics and behaviors. The remaining association (OR=2.31) is made statistically insignificant when measures of cigarette smoking are included in the analysis. Because no physiological justification can be provided for why cigarette smoking would reduce the cognitive effects of marijuana on schooling, we interpret this as evidence that the association is due to other factors. We then use the rich data to explore which constructs are driving this result, determining that it is time-varying parental and peer influences.

Lateral adhesion drives reintegration of misplaced cells into epithelial monolayers.

Cells in simple epithelia orient their mitotic spindles in the plane of the epithelium so that both daughter cells are born within the epithelial sheet. This is assumed to be important to maintain epithelial integrity and prevent hyperplasia, because misaligned divisions give rise to cells outside the epithelium. Here we test this assumption in three types of Drosophila epithelium; the cuboidal follicle epithelium, the columnar early embryonic ectoderm, and the pseudostratified neuroepithelium. Ectopic expression of Inscuteable in these tissues reorients mitotic spindles, resulting in one daughter cell being born outside the epithelial layer. Live imaging reveals that these misplaced cells reintegrate into the tissue. Reducing the levels of the lateral homophilic adhesion molecules Neuroglian or Fasciclin 2 disrupts reintegration, giving rise to extra-epithelial cells, whereas disruption of adherens junctions has no effect. Thus, the reinsertion of misplaced cells seems to be driven by lateral adhesion, which pulls cells born outside the epithelial layer back into it. Our findings reveal a robust mechanism that protects epithelia against the consequences of misoriented divisions.The authors are grateful to R. Nieuwburg, the St Johnston group, and other Gurdon Institute members for suggestions. We thank the Bloomington Stock Center, J. Knoblich, and the TRiP at Harvard Medical School (NIH/NIGMS R01-GM084947) for fly stocks. We thank N. Lowe for technical assistance. This work was supported by a Wellcome Trust Principal Fellowship to D.St.J. (080007), and by core support from the Wellcome Trust (092096) and Cancer Research UK (A14492). D.T.B. was supported by a Marie Curie Fellowship and the Wellcome Trust. H.E.L. was supported by a Herchel Smith Studentship.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ncb324