Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance

We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens-Rubin (1997) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model where we use time-invariant subject-specific compliance principal strata to summarize longtudinal trends of subject-specific time-varying compliance patterns. The principal strata are formed using Markov models that related current compliance behavior to compliance history. Treatment effects are estimated as intent-to -treat effects within the compliance principal strata

Casual Mediation Analyses with Structural Mean Models

We represent a linear structural mean model (SMM)approach for analyzing mediation of a randomized baseline intervention\u27s effect on a univariate follow-up outcome. Unlike standard mediation analyses, our approach does not assume that the mediating factor is randomly assigned to individuals (i.e., sequential ignorability). Hence, a comparison of the results of the proposed and standard approaches in with respect to mediation offers a sensitivity analyses of the sequential ignorability assumption. The G-estimation procedure for the proposed SMM represents an extension of the work on direct effects of randomized treatment effects for survival outcomes by Robins and Greenland (1994) (Section 5.0 and Appendix B) and on treatment non-adherence for continuous outcomes by TenHave et al. (2004). Simulations show good estimation and confidence interval performance under unmeasured confounding relative mediation approach. Sensitivity analyses of the sequential ignorability assumption comparing the results of the two approaches are presented in the context of two suicide/depression treatment studies

Imprecision and bias in orthodontic treatment results

Imprecision in treatment response has been defined as inconsistent unpredictable results from the same treatment. Bias has been defined as systematic failure to achieve defined treatment goals. Concepts of imprecision and bias are applied to the results of a study of soft-tissue response to Class II treatment with edgewise and Herbst appliances.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27413/1/0000448.pd

A multivariate approach to analyzing the relation between occlusion and craniofacial morphology

This study examined the association between occlusion and craniofacial morphology using univariate and multivariate statistical methods. Data were obtained from study casts and lateral cephalometric radiographs of 164 children in the early permanent dentition. The following multiple features of occlusion were assessed: molar relation, overjet, overbite, and anterior crowding. Angular skeletal measures assessed cranial base flexure, maxillary horizontal and vertical positions, mandibular horizontal and vertical positions, horizontal and vertical maxillary-mandibular relations, and positions of the incisors. The relation between the Occlusal Index, which is a malocclusion severity index, and skeletal morphology was also investigated. Associations were examined by use of linear correlation, stepwise multiple regression, and canonical correlation analyses. Individually and in combination, occlusal features were poorly associated with individual skeletal measures (r2 [les] 0.35). The strongest association occurred between a linear combination of occlusal features and a linear combination of skeletal measures (R2 = 0.66, p = 0.0001). A malocclusion severity index did not aid in the identification of craniofacial morphology. The results suggested that combinations of certain occlusal characteristics may be associated with specific skeletal types; however, a generalized statement of this concept could not be supported.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27993/1/0000426.pd

Longitudinal Nested Compliance Class Model in the Presence of Time-Varying Noncompliance

This article discusses a nested latent class model for analyzing longitudinal randomized trials when subjects do not always adhere to the treatment to which they are randomized. In the Prevention of Suicide in Primary Care Elderly: Collaborative Trial (PROSPECT) study, subjects were randomized to either the control treatment, where they received standard care, or to the intervention, where they received standard care in addition to meeting with depression health specialists. The health specialists educate patients, their families, and physicians about depression and monitor their treatment. Those randomized to the control treatment have no access to the health specialists; however, those randomized to the intervention could choose not to meet with the health specialists, hence, receiving only the standard care. Subjects participated in the study for two years where depression severity and adherence to meeting with health specialists were measured at each follow-up. The outcome of interest is the effect of meeting with the health specialists on depression severity. Traditional intention-to-treat and as-treated analyses may produce biased causal effect estimates in the presence of subject noncompliance. Utilizing a nested latent class model that uses subject-specific and time-invariant superclasses allows us to summarize longitudinal trends of compliance patterns, and estimate the effect of the intervention using intent-to-treat contrasts within principal strata corresponding to longitudinal compliance behavior patterns. Analyses show that subjects with more severe depression are more likely to adhere to treatment randomization, and those that are compliant and meet with health specialists benefit from the meetings and show improvement in depression. Simulation results show that our estimation procedure produces reasonable parameter estimates under correct model assumptions