Random effects models are commonly used to analyze longitudinal categorical data. Marginalized random effects models are a class of models that permit direct estimation of marginal mean parameters and characterize serial correlation for longitudinal categorical data via random effects (Heagerty, 1999). Marginally specified logistic-normal models for longitudinal binary data. Biometrics
55, 688–698; Lee and Daniels, 2008. Marginalized models for longitudinal ordinal data with application to quality of life studies. Statistics in Medicine
27, 4359–4380). In this paper, we propose a Kronecker product (KP) covariance structure to capture the correlation between processes at a given time and the correlation within a process over time (serial correlation) for bivariate longitudinal ordinal data. For the latter, we consider a more general class of models than standard (first-order) autoregressive correlation models, by re-parameterizing the correlation matrix using partial autocorrelations (Daniels and Pourahmadi, 2009). Modeling covariance matrices via partial autocorrelations. Journal of Multivariate Analysis
100, 2352–2363). We assess the reasonableness of the KP structure with a score test. A maximum marginal likelihood estimation method is proposed utilizing a quasi-Newton algorithm with quasi-Monte Carlo integration of the random effects. We examine the effects of demographic factors on metabolic syndrome and C-reactive protein using the proposed models.
Kronecker product; Metabolic syndrome; Partial autocorrelation
Estimation of the covariance structure for irregular sparse longitudinal data has been studied by many authors in recent years but typically using fully parametric specifications. In addition, when data are collected from several groups over time, it is known that assuming the same or completely different covariance matrices over groups can lead to loss of efficiency and/or bias. Nonparametric approaches have been proposed for estimating the covariance matrix for regular univariate longitudinal data by sharing information across the groups under study. For the irregular case, with longitudinal measurements that are bivariate or multivariate, modeling becomes more difficult. In this article, to model bivariate sparse longitudinal data from several groups, we propose a flexible covariance structure via a novel matrix stick-breaking process for the residual covariance structure and a Dirichlet process mixture of normals for the random effects. Simulation studies are performed to investigate the effectiveness of the proposed approach over more traditional approaches. We also analyze a subset of Framingham Heart Study data to examine how the blood pressure trajectories and covariance structures differ for the patients from different BMI groups (high, medium and low) at baseline.
Covariance matrix; DIC; Dirichlet process mixture of normals; MCMC
The purpose of this study was to provide normative data on fall prevalence in US hospitals by unit type and to determine the 27-month secular trend in falls prior to the implementation of the Centers for Medicare and Medicaid Service (CMS) rule which does not reimburse hospitals for care related to injury resulting from hospital falls.
We used data from the National Database of Nursing Quality Indicators (NDNQI) collected between July 1, 2006 and September 30, 2008 to estimate prevalence and secular trends of falls occurring in adult medical, medical-surgical and surgical nursing units. More than 88 million patient days (pd) of observation were contributed from 6,100 medical, surgical, and medical-surgical nursing units in 1,263 hospitals across the United States.
A total of 315,817 falls occurred (rate=3.56 falls/1,000 pd) during the study period, of which 82,332 (26.1%) resulted in an injury (rate=0.93/1,000 pd). Both total fall and injurious fall rates were highest in medical units (fall rate=4.03/1,000 pd; injurious fall rate=1.08/1,000 pd) and lowest in surgery units (fall rate=2.76/1,000 pd; injurious fall rate=0.67/1,000 pd). Falls (0.4% decrease/quarter, p<0.0001) and injurious falls (1% decrease per quarter, p<0.0001) both decreased over the 27-month study.
In this large sample, fall and injurious fall prevalence varied by nursing unit type in US hospitals. Over the 27 month study, there was a small, but statistically significant, decrease in falls (p<0.0001) and injurious falls (p<0.0001).
Accidental falls; epidemiology; hospital units; injuries/epidemiology; databases
Skeletal muscles of children with Duchenne muscular dystrophy (DMD) have enhanced susceptibility to damage and progressive lipid infiltration, which contribute to an increase in magnetic resonance proton transverse relaxation time (T2). Therefore, examining T2 changes in individual muscles may be useful for monitoring disease progression in DMD. In this study we utilized mean T2, percent elevated pixels, and T2 heterogeneity to assess changes in composition of dystrophic muscles. In addition, we used fat saturation (fatsat) to distinguish T2 changes due to edema and inflammation from fat infiltration in muscles.
Thirty subjects with DMD and 15 age-matched controls underwent T2-weighted imaging of their lower leg using 3-T MR system. T2 maps were developed and four lower leg muscles were manually traced (soleus, medial gastrocnemius, peroneal and tibialis anterior). Mean T2 of the traced regions of interest (ROI), width of T2 histograms, and percent-elevated pixels were calculated.
We found that even in young children with DMD, muscles had elevated mean T2, were more heterogeneous, and had a greater percent-elevated pixels in the lower leg muscles than controls. T2 measures decreased with fat saturation, but were still higher (p<0.05) in dystrophic muscles than controls. Further, T2 measures showed positive correlations with timed functional tests (r=0.23–0.79).
The elevated T2 measures with and without fat saturation in all ages of DMD examined (5–15 years) compared to unaffected controls indicate that the dystrophic muscles have increased regions of damage, edema, and fat infiltration. This study shows that T2 mapping provides multiple approaches that can be effectively utilized to characterize muscle tissue in children with DMD even in the early stages of the disease. Therefore, T2 mapping may prove clinically useful in monitoring muscle changes due to disease process or therapeutic interventions in DMD.
Duchenne muscular dystrophy; skeletal muscle; MRI; proton transverse relaxation time; T2 mapping; heterogeneity; biomarker
In a typical case-control study, exposure information is collected at a single time-point for the cases and controls. However, case-control studies are often embedded in existing cohort studies containing a wealth of longitudinal exposure history on the participants. Recent medical studies have indicated that incorporating past exposure history, or a constructed summary measure of cumulative exposure derived from the past exposure history, when available, may lead to more precise and clinically meaningful estimates of the disease risk. In this paper, we propose a flexible Bayesian semiparametric approach to model the longitudinal exposure profiles of the cases and controls and then use measures of cumulative exposure based on a weighted integral of this trajectory in the final disease risk model. The estimation is done via a joint likelihood. In the construction of the cumulative exposure summary, we introduce an influence function, a smooth function of time to characterize the association pattern of the exposure profile on the disease status with different time windows potentially having differential influence/weights. This enables us to analyze how the present disease status of a subject is influenced by his/her past exposure history conditional on the current ones. The joint likelihood formulation allows us to properly account for uncertainties associated with both stages of the estimation process in an integrated manner. Analysis is carried out in a hierarchical Bayesian framework using Reversible jump Markov chain Monte Carlo (RJMCMC) algorithms. The proposed methodology is motivated by, and applied to a case-control study of prostate cancer where longitudinal biomarker information is available for the cases and controls.
Adaptive knot selection; Exposure trajectory; Influence function; Odds ratio; Regression spline; Risk score diagnostics; Semiparametric modeling
We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest strategies for eliciting sensitivity parameters and conduct simulations to assess violations to the assumptions. This approach is used to assess mediation in a recent weight management clinical trial.
Contusion spinal cord injury (SCI) animal models are used to study loss of muscle function and mass. However, parallels to the human condition typically have been confounded by spontaneous recovery observed within the first few post-injury weeks, partly because of free cage activity. We implemented a new rat model combining SCI with cast immobilization (IMM) to more closely reproduce the unloading conditions experienced by SCI patients. Magnetic resonance imaging was used to monitor hindlimb muscles' cross-sectional area (CSA) after SCI, IMM alone, SCI combined with IMM (SCI+IMM), and in controls (CTR) over a period of 21 days. Soleus muscle tetanic force was measured in situ on day 21, and hindlimb muscles were harvested for histology. IMM alone produced a decrease in triceps surae CSA to 63.9±4.9% of baseline values within 14 days. In SCI, CSA decreased to 75.0±10.5% after 7 days, and recovered to 77.9±10.7% by day 21. SCI+IMM showed the greatest amount of atrophy (56.9±9.9% on day 21). In all groups, muscle mass and soleus tetanic force decreased in parallel, such that specific force was maintained. Extensor digitorum longus (EDL) and soleus fiber size decreased in all groups, particularly in SCI+IMM. We observed a significant degree of asymmetry in muscle CSA in SCI but not IMM. This effect increased between day 7 and 21 in SCI, but also in SCI+IMM, suggesting a minor dependence on muscle activity. SCI+IMM offers a clinically relevant model of SCI to investigate the mechanistic basis for skeletal muscle adaptations after SCI and develop therapeutic approaches.
atrophy; immobilization; magnetic resonance imaging; SCI; skeletal muscle
We explore the use of a posterior predictive loss criterion for model selection for incomplete longitudinal data. We begin by identifying a property that most model selection criteria for incomplete data should consider. We then show that a straightforward extension of the Gelfand and Ghosh (1998) criterion to incomplete data has two problems. First, it introduces an extra term (in addition to the goodness of fit and penalty terms) that compromises the criterion. Second, it does not satisfy the aforementioned property. We propose an alternative and explore its properties via simulations and on a real dataset and compare it to the deviance information criterion (DIC). In general, the DIC outperforms the posterior predictive criterion, but the latter criterion appears to work well overall and is very easy to compute unlike the DIC in certain classes of models for missing data.
DIC; Bayes Factor; Longitudinal data; MCMC; Model Selection
In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance. Standard methods include specifying a single covariance matrix for all groups or independently estimating the covariance matrix for each group without regard to the others, but when these model assumptions are incorrect, these techniques can lead to biased mean effects or loss of efficiency, respectively. Thus, it is desirable to develop methods to simultaneously estimate the covariance matrix for each group that will borrow strength across groups in a way that is ultimately informed by the data. In addition, for several groups with covariance matrices of even medium dimension, it is difficult to manually select a single best parametric model among the huge number of possibilities given by incorporating structural zeros and/or commonality of individual parameters across groups. In this paper we develop a family of nonparametric priors using the matrix stick-breaking process of Dunson et al. (2008) that seeks to accomplish this task by parameterizing the covariance matrices in terms of the parameters of their modified Cholesky decomposition (Pourahmadi, 1999). We establish some theoretic properties of these priors, examine their effectiveness via a simulation study, and illustrate the priors using data from a longitudinal clinical trial.
Bayesian nonparametric inference; Cholesky decomposition; matrix stick-breaking process; simultaneous covariance estimation; sparsity
We explore a Bayesian approach to selection of variables that represent fixed and random effects in modeling of longitudinal binary outcomes with missing data caused by dropouts. We show via analytic results for a simple example that nonignorable missing data lead to biased parameter estimates. This bias results in selection of wrong effects asymptotically, which we can confirm via simulations for more complex settings. By jointly modeling the longitudinal binary data with the dropout process that possibly leads to nonignorable missing data, we are able to correct the bias in estimation and selection. Mixture priors with a point mass at zero are used to facilitate variable selection. We illustrate the proposed approach using a clinical trial for acute ischemic stroke.
Bayesian variable selection; Bias; Dropout; Missing data; Model selection
Although lung transplantation is an accepted therapy for end-stage disease, recipient outcomes continue to be hindered by early primary graft dysfunction (PGD) as well as late rejection and bronchiolitis obliterans syndrome (BOS). We have previously shown that the pro-inflammatory cytokine response following transplantation correlates with the severity of PGD. We hypothesized that lung-transplant recipients with an increased inflammatory response immediately following surgery would also have a greater incidence of unfavorable long-term outcomes including rejection, BOS and ultimately death.
A retrospective study of lung-transplant recipients (n = 19) for whom serial blood sampling of cytokines was performed for 24 h following transplantation between March 2002 and June 2003 at a single institution. Long-term follow-up was examined for rejection, BOS and survival.
Thirteen single and six bilateral lung recipients were examined. Eleven (58%) developed BOS and eight (42%) did not. Subgroup analysis revealed an association between elevated IL-6 concentrations 4 h after reperfusion of the allograft and development of BOS (P = 0.068). The correlation between IL-6 and survival time was found to be significant (corr = −0.46, P = 0.047), indicating that higher IL-6 response had shorter survival following transplantation.
An elevation in interleukin (IL)-6 concentration immediately following lung transplantation is associated with a trend towards development of bronchiolitis obliterans, rejection and significantly decreased survival time. Further studies are warranted to confirm the correlation between the immediate inflammatory response, PGD and BOS. Identification of patients at risk for BOS based on the cytokine response after surgery may allow for early intervention.
Transplantation; Lung transplantation; Lung other; Inflammation
Rural counties in the U.S. have higher rates of obesity, sedentary lifestyle, and associated chronic diseases than non-rural areas, yet the management of obesity in rural communities has received little attention from researchers.
To compare 2 extended-care programs for weight management with an education control group.
Design, Setting, and Participants
234 obese women from rural communities who completed an initial 6-month weight-loss program were randomized to extended-care, delivered via telephone counseling or face-to-face sessions, or to an education control group. Cooperative Extension Service offices in six medically underserved rural counties served as venues for the trial. The study was conducted from June 2003 to May 2007.
The extended-care programs entailed problem-solving counseling delivered in 26 biweekly sessions. Control group participants received 26 biweekly newsletters containing weight-control advice.
Main Outcome Measure
Change in weight from randomization.
Mean weight at study entry was 96.4 kg. Mean weight loss during the initial 6-month intervention was 10.0 kg. One year after randomization, participants in the telephone and face-to-face conditions regained less weight (means ± SE = 1.3 ± 0.7 and 1.2 ± 0.6 kg, respectively) than those in the education control group (3.7 ± 0.6 kg; Ps = 0.02 and 0.03). The beneficial effects of extended-care counseling were mediated by greater adherence to behavioral weight-management strategies, and cost analyses indicated that telephone counseling was less expensive than face-to-face intervention.
Extended care delivered either by telephone or face-to-face sessions improved the one-year maintenance of lost weight compared to education alone. Telephone counseling constitutes an effective and cost-efficient option for long-term weight management. Delivering lifestyle interventions via the existing infrastructure of the Cooperative Extension Service represents a viable means of research translation into rural communities with limited access to preventive health services.
ClinicalTrials.gov number, NCT00201006.
A major challenge following successful weight loss is continuing the behaviors required for long-term weight maintenance. This challenge may be exacerbated in rural areas with limited local support resources.
This study describes and compares program costs and cost-effectiveness for 12-month extended care lifestyle maintenance programs following an initial 6-month weight loss program.
A 1-year prospective controlled randomized clinical trial.
The study included 215 female participants age 50 or older from rural areas who completed an initial 6-month lifestyle program for weight loss. The study was conducted from June 1, 2003, to May 31, 2007.
The intervention was delivered through local Cooperative Extension Service offices in rural Florida. Participants were randomly-assigned to a 12-month extended care program using either individual telephone counseling (n=67), group face-to-face counseling (n=74), or a mail/control group (n=74).
Main Outcome Measures
Program delivery costs, weight loss, and self-reported health status were directly assessed through questionnaires and program activity logs. Costs were estimated across a range of enrollment sizes to allow inferences beyond the study sample.
Statistical Analyses Performed
Non-parametric and parametric tests of differences across groups for program outcomes were combined with direct program cost estimates and expected value calculations to determine which scales of operation favored alternative formats for lifestyle maintenance.
Median weight regain during the intervention year was 1.7 kg for participants in the face-to-face format, 2.1 kg for the telephone format, and 3.1 kg for the mail/control format. For a typical group size of 13 participants, the face-to-face format had higher fixed costs, which translated into higher overall program costs ($420 per participant) when compared to individual telephone counseling ($268 per participant) and control ($226 per participant) programs. While the net weight lost after the 12-month maintenance program was higher for the face-to-face and telephone programs compared to the control group, the average cost per expected kilogram of weight lost was higher for the face-to-face program ($47/kg) compared to the other two programs (approximately $33/kg for telephone and control).
Both the scale of operations and local demand for programs are important considerations in selecting a delivery format for lifestyle maintenance. In this study, the telephone format had a lower cost, but similar outcomes compared to the face-to-face format.
Obesity; cost-effectiveness; randomized trial; rural health
A key issue in the treatment of obesity in older adults is whether the health benefits of weight loss outweigh the potential risks with respect to musculoskeletal injury.
To compare change in weight, improvements in metabolic risk factors, and reported musculoskeletal adverse events in middle-aged (50–59 years) and older (65–74 years), obese women.
Materials and methods
Participants completed an initial 6-month lifestyle intervention for weight loss, comprised of weekly group sessions, followed by 12 months of extended care with biweekly contacts. Weight and fasting blood samples were assessed at baseline, month 6, and month 18; data regarding adverse events were collected throughout the duration of the study.
Both middle-aged (n = 162) and older (n = 56) women achieved significant weight reductions from baseline to month 6 (10.1 ± 0.68 kg and 9.3 ± 0.76 kg, respectively) and maintained a large proportion of their losses at month 18 (7.6 ± 0.87 kg and 7.6 ± 1.3 kg, respectively); there were no significant differences between the two groups with respect to weight change. Older women further experienced significant reductions in systolic blood pressure, HbA1c, and C-reactive protein from baseline to month 6 and maintained these improvements at month 18. Despite potential safety concerns, we found that older women were no more likely to experience musculoskeletal adverse events during the intervention as compared with their middle-aged counterparts.
These results suggest that older, obese women can experience significant health benefits from lifestyle treatment for obesity, including weight loss and improvements in disease risk factors. Further investigation of the impact of weight loss on additional health-related parameters and risks (eg, body composition, muscular strength, physical functioning, and injuries) in older adults is needed.
lifestyle intervention; adverse events; metabolic risk factors
Bed alarm systems intended to prevent hospital falls have not been formally evaluated.
To investigate whether an intervention aimed at increasing bed alarm use decreases hospital falls and related events.
Pair-matched, cluster randomized trial over 18 months. Nursing units were allocated by computer-generated randomization on the basis of baseline fall rates. Patients and outcome assessors were blinded to unit assignment; outcome assessors may have become unblinded. (ClinicalTrials.gov registration number: NCT00183053)
16 nursing units in an urban community hospital.
27 672 inpatients in general medical, surgical, and specialty units.
Education, training, and technical support to promote use of a standard bed alarm system (intervention units); bed alarms available but not formally promoted or supported (control units).
Pre–post difference in change in falls per 1000 patient-days (primary end point); number of patients who fell, fall-related injuries, and number of patients restrained (secondary end points).
Prevalence of alarm use was 64.41 days per 1000 patient-days on intervention units and 1.79 days per 1000 patient-days on control units (P = 0.004). There was no difference in change in fall rates per 1000 patient-days (risk ratio, 1.09 [95% CI, 0.85 to 1.53]; difference, 0.41 [CI, −1.05 to 2.47], which corresponds to a greater difference in falls in control vs. intervention units) or in the number of patients who fell, injurious fall rates, or the number of patients physically restrained on intervention units compared with control units.
The study was conducted at a single site and was slightly underpowered compared with the initial design.
An intervention designed to increase bed alarm use in an urban hospital increased alarm use but had no statistically or clinically significant effect on fall-related events or physical restraint use.
Primary Funding Source
National Institute on Aging.
Pattern mixture modeling is a popular approach for handling incomplete longitudinal data. Such models are not identifiable by construction. Identifying restrictions are one approach to mixture model identification (Little, 1995; Little and Wang, 1996; Thijs et al., 2002; Kenward et al., 2003; Daniels and Hogan, 2008) and are a natural starting point for missing not at random sensitivity analysis (Thijs et al., 2002; Daniels and Hogan, 2008). However, when the pattern specific models are multivariate normal, identifying restrictions corresponding to missing at random may not exist. Furthermore, identification strategies can be problematic in models with covariates (e.g. baseline covariates with time-invariant coefficients). In this paper, we explore conditions necessary for identifying restrictions that result in missing at random (MAR) to exist under a multivariate normality assumption and strategies for identifying sensitivity parameters for sensitivity analysis or for a fully Bayesian analysis with informative priors. In addition, we propose alternative modeling and sensitivity analysis strategies under a less restrictive assumption for the distribution of the observed response data. We adopt the deviance information criterion for model comparison and perform a simulation study to evaluate the performances of the different modeling approaches. We also apply the methods to a longitudinal clinical trial. Problems caused by baseline covariates with time-invariant coefficients are investigated and an alternative identifying restriction based on residuals is proposed as a solution.
Missing at random; Non-future dependence; Deviance information criterion
Acute lung injury (ALI) is a condition characterized by acute onset of severe hypoxemia and bilateral pulmonary infiltrates. ALI patients typically require mechanical ventilation in an intensive care unit. Low tidal volume ventilation (LTVV), a time-varying dynamic treatment regime, has been recommended as an effective ventilation strategy. This recommendation was based on the results of the ARMA study, a randomized clinical trial designed to compare low vs. high tidal volume strategies (The Acute Respiratory Distress Syndrome Network, 2000) . After publication of the trial, some critics focused on the high non-adherence rates in the LTVV arm suggesting that non-adherence occurred because treating physicians felt that deviating from the prescribed regime would improve patient outcomes. In this paper, we seek to address this controversy by estimating the survival distribution in the counterfactual setting where all patients assigned to LTVV followed the regime. Inference is based on a fully Bayesian implementation of Robins’ (1986) G-computation formula. In addition to re-analyzing data from the ARMA trial, we also apply our methodology to data from a subsequent trial (ALVEOLI), which implemented the LTVV regime in both of its study arms and also suffered from non-adherence.
Bayesian inference; Causal inference; Dynamic treatment regime; G-computation formula
Missing phenotype data can be a major hurdle to mapping quantitative trait loci (QTL). Though in many cases experiments may be designed to minimize the occurrence of missing data, it is often unavoidable in practice; thus, statistical methods to account for missing data are needed. In this paper we describe an approach for conjoining multiple imputation and QTL mapping. Methods are applied to map genes associated with increased breathing effort in mice after lung inflammation due to allergen challenge in developing lines of the Collaborative Cross, a new mouse genetics resource. Missing data poses a particular challenge in this study because the desired phenotype summary to be mapped is a function of incompletely observed dose-response curves. Comparison of the multiple imputation approach to two naive approaches for handling missing data suggest that these simpler methods may yield poor results: ignoring missing data through a complete case analysis may lead to incorrect conclusions, while using a last observation carried forward procedure, which does not account for uncertainty in the imputed values, may lead to anti-conservative inference. The proposed approach is widely applicable to other studies with missing phenotype data.
multiple imputation; missing data; quantitative trait loci
The primary purpose of the present set of studies was to provide a direct comparison of the effects of the angiotensin-converting enzyme inhibitor enalapril and the angiotensin receptor blocker losartan on body composition, physical performance, and muscle quality when administered late in life to aged rats. Overall, enalapril treatment consistently attenuated age-related increases in adiposity relative to both placebo and losartan. The maximal effect was achieved after 3 months of treatment (between 24 and 27 months of age), at a dose of 40 mg/kg and was observed in the absence of any changes in physical activity, body temperature, or food intake. In addition, the reduction in fat mass was not due to changes in pathology given that enalapril attenuated age-related increases in tumor development relative to placebo- and losartan-treated animals. Both enalapril and losartan attenuated age-related decreases in grip strength, suggesting that changes in body composition appear dissociated from improvements in physical function and may reflect a differential impact of enalapril and losartan on muscle quality. To link changes in adiposity to improvements in skeletal muscle quality, we performed gene array analyses to generate hypotheses regarding cell signaling pathways altered with enalapril treatment. Based on these results, our primary follow-up pathway was mitochondria-mediated apoptosis of myocytes. Relative to losartan- and placebo-treated rats, only enalapril decreased DNA fragmentation and caspase-dependent apoptotic signaling. These data suggest that attenuation of the severity of skeletal muscle apoptosis promoted by enalapril may represent a distinct mechanism through which this compound improves muscle strength/quality.
Age-related adiposity; Body composition; Sarcopenia; Renin–angiotensin system; Physical function; Muscle quality
Obese older adults are particularly susceptible to sarcopenia and have a higher prevalence of disability than their peers of normal weight. Interventions to improve body composition in late life are crucial to maintaining independence. The main mechanisms underlying sarcopenia have not been determined conclusively, but chronic inflammation, apoptosis, and impaired mitochondrial function are believed to play important roles. It has yet to be determined whether impaired cellular quality control mechanisms contribute to this process. The objective of this study was to assess the effects of a 6-month weight loss program combined with moderate-intensity exercise on the cellular quality control mechanisms autophagy and ubiquitin-proteasome, as well as on inflammation, apoptosis, and mitochondrial function, in the skeletal muscle of older obese women. The intervention resulted in significant weight loss (8.0 ± 3.9 % vs. 0.4 ± 3.1% of baseline weight, p = 0.002) and improvements in walking speed (reduced time to walk 400 meters, − 20.4 ± 16% vs. − 2.5 ± 12%, p = 0.03). In the intervention group, we observed a three-fold increase in messenger RNA (mRNA) levels of the autophagy regulators LC3B, Atg7, and lysosome-associated membrane protein-2 (LAMP-2) compared to controls. Changes in mRNA levels of FoxO3A and its targets MuRF1, MAFBx, and BNIP3 were on average seven-fold higher in the intervention group compared to controls, but these differences were not statistically significant. Tumor necrosis factor-α (TNF-α) mRNA levels were elevated after the intervention, but we did not detect significant changes in the downstream apoptosis markers caspase 8 and 3. Mitochondrial biogenesis markers (PGC1α and TFAm) were increased by the intervention, but this was not accompanied by significant changes in mitochondrial complex content and activity. In conclusion, although exploratory in nature, this study is among the first to report the stimulation of cellular quality control mechanisms elicited by a weight loss and exercise program in older obese women.
In longitudinal clinical trials, when outcome variables at later time points are only defined for patients who survive to those times, the evaluation of the causal effect of treatment is complicated. In this paper, we describe an approach that can be used to obtain the causal effect of three treatment arms with ordinal outcomes in the presence of death using a principal stratification approach. We introduce a set of flexible assumptions to identify the causal effect and implement a sensitivity analysis for non-identifiable assumptions which we parameterize parsimoniously. Methods are illustrated on quality of life data from a recent colorectal cancer clinical trial.
Principal stratification; QOL; Ordinal data; Sensitivity analysis
Obesity and a sedentary lifestyle are associated with physical impairments and biologic changes in older adults. Weight loss combined with exercise may reduce inflammation and improve physical functioning in overweight, sedentary, older adults. This study tested whether a weight loss program combined with moderate exercise could improve physical function in obese, older adult women.
Participants (N = 34) were generally healthy, obese, older adult women (age range 55–79 years) with mild to moderate physical impairments (ie, functional limitations). Participants were randomly assigned to one of two groups for 24 weeks: (i) weight loss plus exercise (WL+E; n = 17; mean age = 63.7 years [4.5]) or (ii) educational control (n = 17; mean age = 63.7 [6.7]). In the WL+E group, participants attended a group-based weight management session plus three supervised exercise sessions within their community each week. During exercise sessions, participants engaged in brisk walking and lower-body resistance training of moderate intensity. Participants in the educational control group attended monthly health education lectures on topics relevant to older adults. Outcomes were: (i) body weight, (ii) walking speed (assessed by 400-meter walk test), (iii) the Short Physical Performance Battery (SPPB), and (iv) knee extension isokinetic strength.
Participants randomized to the WL+E group lost significantly more weight than participants in the educational control group (5.95 [0.992] vs 0.23 [0.99] kg; P < 0.01). Additionally, the walking speed of participants in the WL+E group significantly increased compared with that of the control group (reduction in time on the 400-meter walk test = 44 seconds; P < 0.05). Scores on the SPPB improved in both the intervention and educational control groups from pre- to post-test (P < 0.05), with significant differences between groups (P = 0.02). Knee extension strength was maintained in both groups.
Our findings suggest that a lifestyle-based weight loss program consisting of moderate caloric restriction plus moderate exercise can produce significant weight loss and improve physical function while maintaining muscle strength in obese, older adult women with mild to moderate physical impairments.
obesity; weight loss; physical function; oxidative stress; inflammation; walking speed
In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often co-circulate with influenza and cause influenza-like illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among individuals. We propose a Bayesian competing-risk model for multiple co-circulating pathogens for inference on transmissibility and intervention efficacies under the assumption that missingness in the biological confirmation of the pathogen is ignorable. Simulation studies indicate a reasonable performance of the proposed model even if the number of potential pathogens is misspecified. They also show that a moderate amount of missing laboratory test results has only a small impact on inference about key parameters in the setting of close contact groups. Using the proposed model, we found that a non-pharmaceutical intervention is marginally protective against transmission of influenza A in a study conducted in elementary schools.
Missing data; MCMC; Infectious disease; Competing risks; Intervention efficacy