The Use of Mixed Models for the Analysis of Mediated Data with Time-Dependent Predictors

Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longitudinal data with time-dependent predictors and mediators. We simulated mediated longitudinal data from an SEM and specified delayed effects of the predictor. A variety of model specifications were assessed, and the LMMs and SEMs were evaluated with respect to bias, coverage probability, power, and Type I error. Models evaluated in the simulation were also applied to data from an observational cohort of HIV-infected individuals. We found that when carefully constructed, the LMM adequately models mediated exposure effects that change over time in the presence of mediation, even when the data arise from an SEM

Non-linear mixed models in the analysis of mediated longitudinal data with binary outcomes

<p>Abstract</p> <p>Background</p> <p>Structural equation models (SEMs) provide a general framework for analyzing mediated longitudinal data. However when interest is in the total effect (i.e. direct plus indirect) of a predictor on the binary outcome, alternative statistical techniques such as non-linear mixed models (NLMM) may be preferable, particularly if specific causal pathways are not hypothesized or specialized SEM software is not readily available. The purpose of this paper is to evaluate the performance of the NLMM in a setting where the SEM is presumed optimal.</p> <p>Methods</p> <p>We performed a simulation study to assess the performance of NLMMs relative to SEMs with respect to bias, coverage probability, and power in the analysis of mediated binary longitudinal outcomes. Both logistic and probit models were evaluated. Models were also applied to data from a longitudinal study assessing the impact of alcohol consumption on HIV disease progression.</p> <p>Results</p> <p>For the logistic model, the NLMM adequately estimated the total effect of a repeated predictor on the repeated binary outcome and were similar to the SEM across a variety of scenarios evaluating sample size, effect size, and distributions of direct vs. indirect effects. For the probit model, the NLMM adequately estimated the total effect of the repeated predictor, however, the probit SEM overestimated effects.</p> <p>Conclusions</p> <p>Both logistic and probit NLMMs performed well relative to corresponding SEMs with respect to bias, coverage probability and power. In addition, in the probit setting, the NLMM may produce better estimates of the total effect than the probit SEM, which appeared to overestimate effects.</p

Treating Homeless Opioid Dependent Patients with Buprenorphine in an Office-Based Setting

CONTEXT Although office-based opioid treatment with buprenorphine (OBOT-B) has been successfully implemented in primary care settings in the US, its use has not been reported in homeless patients. OBJECTIVE To characterize the feasibility of OBOT-B in homeless relative to housed patients. DESIGN A retrospective record review examining treatment failure, drug use, utilization of substance abuse treatment services, and intensity of clinical support by a nurse care manager (NCM) among homeless and housed patients in an OBOT-B program between August 2003 and October 2004. Treatment failure was defined as elopement before completing medication induction, discharge after medication induction due to ongoing drug use with concurrent nonadherence with intensified treatment, or discharge due to disruptive behavior. RESULTS Of 44 homeless and 41 housed patients enrolled over 12 months, homeless patients were more likely to be older, nonwhite, unemployed, infected with HIV and hepatitis C, and report a psychiatric illness. Homeless patients had fewer social supports and more chronic substance abuse histories with a 3- to 6-fold greater number of years of drug use, number of detoxification attempts and percentage with a history of methadone maintenance treatment. The proportion of subjects with treatment failure for the homeless (21%) and housed (22%) did not differ (P=.94). At 12 months, both groups had similar proportions with illicit opioid use [Odds ratio (OR), 0.9 (95% CI, 0.5–1.7) P=.8], utilization of counseling (homeless, 46%; housed, 49%; P=.95), and participation in mutual-help groups (homeless, 25%; housed, 29%; P=.96). At 12 months, 36% of the homeless group was no longer homeless. During the first month of treatment, homeless patients required more clinical support from the NCM than housed patients. CONCLUSIONS Despite homeless opioid dependent patients' social instability, greater comorbidities, and more chronic drug use, office-based opioid treatment with buprenorphine was effectively implemented in this population comparable to outcomes in housed patients with respect to treatment failure, illicit opioid use, and utilization of substance abuse treatment

Performance of mixed effects models in the analysis of mediated longitudinal data

Backgroun: Linear mixed effects models (LMMs) are a common approach for analyzing longitudinal data in a variety of settings. Although LMMs may be applied to complex data structures, such as settings where mediators are present, it is unclear whether they perform well relative to methods for mediational analyses such as structural equation models (SEMs), which have obvious appeal in such settings. For some researchers, SEMs may be more difficult than LMMs to implement, e.g. due to lack of training in the methodology or the need for specialized SEM software. It therefore is of interest to evaluate whether the LMM performs sufficiently in a scenario particularly suitable for SEMs. We focus on evaluation of the total effect (i.e. direct and indirect) of an exposure on an outcome of interest when a mediating factor is present. Our aim is to explore whether the LMM performs as well as the SEM in a setting that is conducive to using the SEM.Methods We simulated mediated longitudinal data from an SEM where a binary, main independent variable has both direct and indirect effects on a continuous outcome. We conducted analyses with both the LMM and SEM to evaluate the performance of the LMM in a setting where the SEM is expected to be preferable. Models were evaluated with respect to bias, coverage probability and power. Sample size, effect size and error distribution of the simulated data were varied. Results: Both models performed well in a range of settings. Marginal increases in power estimates were observed for the SEM, although generally there were no major differences in performance. Power for both models was good with a sample of size of 250 and a small to medium effect size. Bias did not substantially increase for either model when data were generated from distributions that were both skewed and kurtotic. Conclusions: In settings where the goal is to evaluate the overall effects, the LMM excluding mediating variables appears to have good performance with respect to power, bias and coverage probability relative to the SEM. The major benefit of SEMs is that it simultaneously and efficiently models both the direct and indirect effects of the mediation process

Micelles of Different Morphologies - Advantages of Worm-like Filomicelles of PEO-PCL in Paclitaxel Delivery

Worm-like and spherical micelles are both prepared here from the same amphiphilic diblock copolymer, poly(ethylene oxide)-b-poly (ε-caprolactone) (PEO [5 kDa]-PCL [6.5 kDa]) in order to compare loading and delivery of hydrophobic drugs. Worm-like micelles of this degradable copolymer are nanometers in cross-section and spontaneously assemble to stable lengths of microns, resembling filoviruses in some respects and thus suggesting the moniker filomicelles . The highly flexible worm-like micelles can also be sonicated to generate kinetically stable spherical micelles composed of the same copolymer. The fission process exploits the finding that the PCL cores are fluid, rather than glassy or crystalline, and core-loading of the hydrophobic anticancer drug delivery, paclitaxel (TAX) shows that the worm-like micelles load and solubilize twice as much drug as spherical micelles. In cytotoxicity tests that compare to the clinically prevalent solubilizer, Cremophor® EL, both micellar carriers are far less toxic, and both types of TAX-loaded micelles also show 5-fold greater anticancer activity on A549 human lung cancer cells. PEO-PCL based worm-like filomicelles appear to be promising pharmaceutical nanocarriers with improved solubilization efficiency and comparable stability to spherical micelles, as well as better safety and efficacy in vitro compared to the prevalent Cremophor® EL TAX formulation

Implications of Cannabis Use and Heavy Alcohol Use on HIV Drug Risk Behaviors in Russian Heroin Users

Cannabis and heavy alcohol use potentially increase HIV transmission by increasing risky drug behaviors. We studied 404 subjects entering treatment for heroin dependence, in St. Petersburg, Russia. We used the HIV Risk Assessment Battery (RAB) drug subscale to measure risky drug behavior. Although all heavy alcohol users had risky drug behaviors, their drug RAB scores did not differ from non-heavy alcohol users in unadjusted or adjusted analyses. Cannabis use was significantly associated with drug RAB scores in unadjusted analyses (mean difference 1.7 points) and analyses adjusted for age, sex, and employment (mean difference 1.3 points). When also adjusting for stimulant use, the impact of cannabis use was attenuated and no longer statistically significant (mean difference 1.1 points). Because of the central role of risky drug behaviors in the Russian HIV epidemic, it is important to understand how the use of multiple substances, including cannabis and alcohol, impacts risky drug behaviors