MIXNO: a computer program for mixed-effects nominal logistic regression

MIXNO provides maximum marginal likelihood estimates for mixed-effects nominal logistic regression analysis. These models can be used for analysis of correlated nominal response data, for example, data arising from a clustered or longitudinal design. For such data, the mixed-effects model assumes that data within clusters or sub jects are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from nesting of the data. MIXNO uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated along with the (fixed) effects of explanatory variables. Examples illustrating usage and features of MIXNO are provided

MIXNO: a computer program for mixed-effects nominal logistic regression

MIXNO provides maximum marginal likelihood estimates for mixed-effects nominal logistic regression analysis. These models can be used for analysis of correlated nominal response data, for example, data arising from a clustered or longitudinal design. For such data, the mixed-effects model assumes that data within clusters or sub jects are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from nesting of the data. MIXNO uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated along with the (fixed) effects of explanatory variables. Examples illustrating usage and features of MIXNO are provided.

A Clinical Prediction Score to Guide Referral of Elderly Dialysis Patients for Kidney Transplant Evaluation.

IntroductionDialysis patients aged ≥70 years derive improved life expectancy through kidney transplantation compared to their waitlisted counterparts, but guidelines are not clear about how to identify appropriate transplantation candidates. We developed a clinical prediction score to identify elderly dialysis patients with expected 5-year survival appropriate for kidney transplantation (>5 years).MethodsIncident dialysis patients in 2006-2009 aged ≥70 were identified from the United States Renal Data System database and divided into derivation and validation cohorts. Using the derivation cohort, candidate variables with a significant crude association with 5-year all-cause mortality were included in a multivariable logistic regression model to generate a scoring system. The scoring system was tested in the validation cohort and a cohort of elderly transplant recipients.ResultsCharacteristics most predictive of 5-year mortality included age >80, body mass index (BMI) <18, the presence of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), immobility, and being institutionalized. Factors associated with increased 5-year survival were non-white race, a primary cause of end stage renal disease (ESRD) other than diabetes, employment within 6 months of dialysis initiation, and dialysis start via arteriovenous fistula (AVF). 5-year mortality was 47% for the lowest risk score group (3.6% of the validation cohort) and >90% for the highest risk cohort (42% of the validation cohort).ConclusionThis clinical prediction score could be useful for physicians to identify potentially suitable candidates for kidney transplantation

Affect, Interpersonal Behaviour and Interpersonal Perception During Open-Label, Uncontrolled Paroxetine Treatment of People with Social Anxiety Disorder: A Pilot Study

Background: Laboratory-based research with community samples has suggested changes in affective, behavioural and cognitive processes as possible explanations for the effects of serotonergic medications. Examining the effects of serotonergic medications using an ecological momentary measure (such as event-contingent recording) in the daily lives of people with social anxiety disorder would contribute to establishing the effects of these medications on affect, behaviour and one form of cognition: perception of others’ behaviour. Methods: The present study assessed changes in affect, interpersonal behaviour and perception of others’ behaviour in adults with social anxiety disorder using ecological momentary assessment at baseline and over 4 months of a single-arm, uncontrolled, open-label trial of treatment with the selective serotonin reuptake inhibitor paroxetine. Results: Anxiety and concurrent depressive symptoms decreased. Participants also reported increased positive and decreased negative affect; increased agreeable and decreased quarrelsome behaviour; increased dominant and decreased submissive behaviour; and increased perception that others behaved agreeably toward them. Moreover, participants demonstrated reduced intraindividual variability in affect, interpersonal behaviour and perception of others’ behaviour. Limitations: Limitations included the lack of a placebo group, the inability to identify the temporal order of changes and the restricted assessment of extreme behaviour. Conclusion: The results of the present study demonstrate changes during pharmacotherapy in the manifestation of affect, interpersonal behaviour and interpersonal perception in the daily lives of people with social anxiety disorder. Given the importance of interpersonal processes to social anxiety disorder, these results may guide future research seeking to clarify mechanisms of action for serotonergic medications

Kinematic Foot Types in Youth with Equinovarus Secondary to Hemiplegia

Background Elevated kinematic variability of the foot and ankle segments exists during gait among individuals with equinovarus secondary to hemiplegic cerebral palsy (CP). Clinicians have previously addressed such variability by developing classification schemes to identify subgroups of individuals based on their kinematics. Objective To identify kinematic subgroups among youth with equinovarus secondary to CP using 3-dimensional multi-segment foot and ankle kinematics during locomotion as inputs for principal component analysis (PCA), and K-means cluster analysis. Methods In a single assessment session, multi-segment foot and ankle kinematics using the Milwaukee Foot Model (MFM) were collected in 24 children/adolescents with equinovarus and 20 typically developing children/adolescents. Results PCA was used as a data reduction technique on 40 variables. K-means cluster analysis was performed on the first six principal components (PCs) which accounted for 92% of the variance of the dataset. The PCs described the location and plane of involvement in the foot and ankle. Five distinct kinematic subgroups were identified using K-means clustering. Participants with equinovarus presented with variable involvement ranging from primary hindfoot or forefoot deviations to deformtiy that included both segments in multiple planes. Conclusion This study provides further evidence of the variability in foot characteristics associated with equinovarus secondary to hemiplegic CP. These findings would not have been detected using a single segment foot model. The identification of multiple kinematic subgroups with unique foot and ankle characteristics has the potential to improve treatment since similar patients within a subgroup are likely to benefit from the same intervention(s)

Financial Motivation Undermines Maintenance in an Intensive Diet and Activity Intervention

Financial incentives are widely used in health behavior interventions. However, self-determination theory posits that emphasizing financial incentives can have negative consequences if experienced as controlling. Feeling controlled into performing a behavior tends to reduce enjoyment and undermine maintenance after financial contingencies are removed (the undermining effect). We assessed participants' context-specific financial motivation to participate in the Make Better Choices trial—a trial testing four different strategies for improving four health risk behaviors: low fruit and vegetable intake, high saturated fat intake, low physical activity, and high sedentary screen time. The primary outcome was overall healthy lifestyle change; weight loss was a secondary outcome. Financial incentives were contingent upon meeting behavior goals for 3 weeks and became contingent upon merely providing data during the 4.5-month maintenance period. Financial motivation for participation was assessed at baseline using a 7-item scale (α = .97). Across conditions, a main effect of financial motivation predicted a steeper rate of weight regained during the maintenance period, t(165) = 2.15, P = .04. Furthermore, financial motivation and gender interacted significantly in predicting maintenance of healthy diet and activity changes, t(160) = 2.42, P = .016, such that financial motivation had a more deleterious influence among men. Implications for practice and future research on incentivized lifestyle and weight interventions are discussed

A Practical Guide to Calculating Cohen’s f2, a Measure of Local Effect Size, from PROC MIXED

Reporting effect sizes in scientific articles is increasingly widespread and encouraged by journals; however, choosing an effect size for analyses such as mixed-effects regression modeling and hierarchical linear modeling can be difficult. One relatively uncommon, but very informative, standardized measure of effect size is Cohen’s f2, which allows an evaluation of local effect size, i.e., one variable’s effect size within the context of a multivariate regression model. Unfortunately, this measure is often not readily accessible from commonly used software for repeated-measures or hierarchical data analysis. In this guide, we illustrate how to extract Cohen’s f2 for two variables within a mixed-effects regression model using PROC MIXED in SAS® software. Two examples of calculating Cohen’s f2 for different research questions are shown, using data from a longitudinal cohort study of smoking development in adolescents. This tutorial is designed to facilitate the calculation and reporting of effect sizes for single variables within mixed-effects multiple regression models, and is relevant for analyses of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies

Applying Mixed-Effects Location Scale Modeling to Examine Within-Person Variability in Physical Activity Self-Efficacy

Abstract: Background: Physical activity self-efficacy is conceptualized as a construct that is changeable and responsive to contextual factors. The current study applied mixed-effects location scale modeling to examine within-person variability in physical activity self-efficacy among middle-aged and older adults (N = 14 adults, mean age = 59.4 years) who were attempting behavior change. Methods: An electronic diary was used to record self-reported self-efficacy and physical activity via Ecological Momentary Assessment (EMA) twice a day (2:00 pm and 9:00 pm). Data from weeks 1-6 were analyzed using a Mixed-Effects Location Scale Model in SAS PROC NLMIXED. Results: Participants differed from each other in the degree to which physical activity self-efficacy varied from day to day (p = .03). Within-person variation in self-efficacy was negatively related to levels of brisk walking each week (p = .002), and decreased over time (p = .03). Conclusions: Preliminary results suggest that fluctuations in self-efficacy may be as important for predicting short-term behavior as the overall or mean level of self-efficacy