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In health research, count outcomes are fairly common and often these counts have a large number of zeros. In order to adjust for these extra zero counts, various modifications of the Poisson regression model have been proposed. Lambert (Lambert, D., Technometrics 34, 1-14, 1992) described a zero-inflated Poisson (ZIP) model that is based on a mixture of a binary distribution (πi ) degenerated at zero with a Poisson distribution (λi ). Depending on the relationship between πi and λi , she described two variants: a ZIP and a ZIP (τ ) model. In this paper, we extend these models for the case of clustered data (e.g., patients observed within hospitals) and describe random-effects ZIP and ZIP (τ ) models. These models are appropriate for the analysis of clustered extra-zero Poisson count data. The distribution of the random effects is assumed to be normal and a maximum marginal likelihood estimation method is used to estimate the model parameters. We applied these models to data from patients who underwent colon operations from 123 Veterans Affairs Medical Centers in the National VA Surgical Quality Improvement Program.

The authors evaluated the efficacy of fluoxetine hydrochloride (Prozac; Eli Lilly and Company, Indianapolis, IN) as an adjunct to behavioral treatment for smoking cessation. Sixteen sites randomized 989 smokers to 3 dose conditions: 10 weeks of placebo, 30 mg, or 60 mg fluoxetine per day. Smokers received 9 sessions of individualized cognitive-behavioral therapy, and biologically verified 7-day self-reported abstinence follow-ups were conducted at 1, 3, and 6 months posttreatment. Analyses assuming missing data counted as smoking observed no treatment difference in outcomes. Pattern-mixture analysis that estimates treatment effects in the presence of missing data observed enhanced quit rates associated with both the 60-mg and 30-mg doses. Results support a modest, short-term effect of fluoxetine on smoking cessation and consideration of alternative models for handling missing data.

Objectives To investigate the relationships of biological, behavioral, familial, and environmental characteristics with siblings´ physical activity (PA) levels as well as the intrapair resemblance in PA. Methods The sample comprises 834 (390 females) biological siblings [brother-brother (BB), sister-sister (SS), brother-sister (BS)] aged 9 to 20 years. Total PA index (TPAI) was estimated by questionnaire. Information on potential behavioral, familial, and environmental correlates was obtained by self-report; body mass index (BMI), biological maturation, and physical fitness were measured. Multilevel models were used to analyze siblings´ clustered data, and sibling resemblance was estimated with the intraclass correlation (ρ). Results On average, younger sibs, those more physically fit, and those with more parental support had greater TPAI. Further, BB pairs had higher TPAI levels than SS or BS pairs, but also had greater within-pair variance. When adjusted for all covariates, SS pairs demonstrated greater resemblance in TPAI (ρ = 0.53, 95%CI = 0.38–0.68) than BS (ρ = 0.26, 95%CI = 0.14–0.43) or BB pairs (ρ = 0.18, 95%CI = 0.06–0.44). Conclusions Age, physical fitness, and parental support were the best predictors of TPAI levels. A moderate level of resemblance in TPAI was observed in SS pairs, while lower resemblance was found for BS and BB pairs. These findings may be due to differences in the roles of shared genetic factors, familial, and environmental characteristics across different sibling types.

e18095 Background: A cancer diagnosis in adolescents and young adults (AYA) comes at a complex developmental interval that often coincides with risky behaviors, such as substance use. Modifiable risk behaviors are an important area of research to minimize morbidity for AYA. Our goal was to examine substance use, affect, and other health indicators in a diverse AYA cancer patient sample. Methods: We identified patients between the ages of 18-39 years and 6 or more months from date of cancer diagnosis. Measures included demographic, general health and substance use variables, the Center for Epidemiologic Studies Depression Scale (CES-D), and the State-Trait Anxiety Inventory (STAI-T). Linear trend analyses were performed to examine group differences. Backward-selection ordinal regression analyses identified specific factors associated with risky health behaviors. Results: Participants (60% male; Mage = 27; 70% White; 14% Black) included 100 patients with primary diagnoses of lymphoma (N = 45); leukemia (N = 37); sarcoma (N = 10); other (N = 8). Median time from cancer diagnosis was 2 years (range 6 months - 20 years). Past year alcohol, cigarette, and marijuana use was reported in 80% ,15%, and 33% of participants, respectively. We classified three subgroups based on past year alcohol and smoking, including non-smoker/non-drinker (n = 20), non-smoker/drinker (n = 65), and smoker/drinker (n = 15). Linear trend analyses indicated that the groups did not differ on time since diagnosis, but the smoker/drinker group initiated drinking younger ( p< 0.01), were more likely to use marijuana in the past year ( p< 0.01), and had increased depressive symptoms (CES-D; p< 0.01), anxiety (STAI-T; p< 0.05), body pain ( p< 0.05), and poorer sleep quality ( p< 0.05). Backward model selection treating outcome categories as ordinal indicated that frequent marijuana use and poor sleep quality were associated with being a past year smoker/drinker. Conclusions: A sizeable portion of AYA cancer patients engage in drinking and smoking, which is associated with negative affect and poorer self-reported health. Targeted interventions should be considered to reduce risky behaviors in this population.

This study investigates biological, behavioural and sociodemographic correlates of intra-pair similarities, and estimates sibling resemblance in health-related physical fitness (PF). The sample comprises 1101 biological siblings (525 females) aged 9–20 years. PF components and markers were: morphological [waist circumference (WC) and %body fat (%BF)], muscular [handgrip strength (GS) and standing long jump (SLJ)], motor [50-yard dash (50YD) and shuttle run (SR)], and cardiorespiratory (1-mile run). Biological maturation was assessed; physical activity (PA), TV viewing and socioeconomic status (SES) information was obtained. On average, older and more mature subjects are better performers in all PF components; PA was negatively associated with SR, while SES was negatively associated with SLJ and SR. A pattern was observed in the intraclass correlations (ρ) wherein same sex siblings demonstrate greater resemblance for most PF components (sister-sister: 0.35≤ ρ≤0.55; brother-brother: (0.25≤ρ≤0.60) than brother-sister pairs (BS) (0≤ρ≤0.15), except for %BF (ρBB>ρSS>ρBS), and the 1-mile run (ρSS>ρBS>ρBB). In conclusion, behavioural and sociodemographic correlates play different roles in siblings PF expression. Further, a significant familial PF resemblance was observed with different trends in different sibling types, probably due to variations in shared genetic factors and sociodemographic conditions.

This study aimed to jointly analyse moderate-to-vigorous physical activity (MVPA) and sedentariness, and their correlates, in children within their school contexts, using a multivariate multilevel approach. The sample comprises 499 Portuguese children (284 girls) from 23 schools. MVPA and sedentary time were estimated by accelerometer. A set of predictor variables from both child and school levels was tested. Overall, schools explained a small amount of the total variance in both MVPA (5.6%) and sedentariness (3.2%), and a correlation coefficient of − 0.45 (p < 0.05) was found between MVPA and sedentariness at the child level. Number of siblings and socioeconomic status (SES) were significantly associated with both sedentariness (SES: β = 2.372 ± 1.183; siblings: β = − 8.127 ± 2.759) and MPVA (SES: β = − 1.535 ± 0.421; siblings: β = 2.822 ± 0.977), but with opposite signs. Body Mass Index (BMI) (β = − 4.804 ± 1.898) and sex (male) (β = 21.561 ± 3.496) were only associated with MVPA. None of the school correlates were statistically significant in their joint effects to simultaneously explain sedentariness and MVPA. These results suggest that although MVPA and sedentariness may be different constructs, they are correlated and this should be taken into account when designing strategies to reduce children’s sedentariness and increase their MVPA. In addition, the small effect of the school context on this relationship highlights the important roles of child and family characteristics.

Despite the highly replicated relationship between depression and nicotine dependence, little is known about this association across both time and levels of lifetime smoking exposure. In the present study, we evaluate if symptoms of depression are associated with emerging nicotine dependence after accounting for smoking exposure and whether this relationship varies from adolescence to young adulthood and across increasing levels of smoking.The sample was drawn from the Social and Emotional Contexts of Adolescent Smoking Patterns Study which measured smoking, nicotine dependence and depression over 6 assessment waves spanning 6years. Analyses were based on repeated assessment of 941 participants reporting any smoking 30days prior to individual assessment waves. Mixed-effects regression models were estimated to examine potential time and smoking exposure varying effects in the association between depression and nicotine dependence.Inter-individual differences in mean levels of depression and within subject changes in depression from adolescence to young adulthood were each significantly associated with nicotine dependence symptoms over and above lifetime smoking exposure. This association was consistent across both time and increasing levels of lifetime smoking.Depression is a consistent risk factor for nicotine dependence over and above exposure to cigarettes and this association can be demonstrated from the earliest experiences with smoking in adolescents through the establishment of more regular smoking patterns across the transition to young adulthood.Depression remains a prominent risk factor for nicotine dependence, and youth with depression symptoms represent an important subgroup in need of targeted smoking intervention.

A bivariate mixed-effects location-scale model is proposed for estimation of means, variances, and covariances of two continuous outcomes measured concurrently in time and repeatedly over subjects. Modeling the two outcomes jointly allows examination of BS and WS association between the outcomes and whether the associations are related to covariates. The variance-covariance matrices of the BS and WS effects are modeled in terms of covariates, explaining BS and WS heterogeneity. The proposed model relaxes assumptions on the homogeneity of the within-subject (WS) and between-subject (BS) variances. Furthermore, the WS variance models are extended by including random scale effects. Data from a natural history study on adolescent smoking are used for illustration. 461 students, from 9th and 10th grades, reported on their mood at random prompts during seven consecutive days. This resulted in 14,105 prompts with an average of 30 responses per student. The two outcomes considered were a subject's positive affect and a measure of how tired and bored they were feeling. Results showed that the WS association of the outcomes was negative and significantly associated with several covariates. The BS and WS variances were heterogeneous for both outcomes, and the variance of the random scale effects were significantly different from zero.

Medicaid data contain International Classification of Diseases, Clinical Modification (ICD-9-CM) codes indicating maltreatment, yet there is a little information on how valid these codes are for the purposes of identifying maltreatment from health, as opposed to child welfare, data. This study assessed the validity of Medicaid codes in identifying maltreatment. Participants ( n = 2,136) in the first National Survey of Child and Adolescent Well-Being were linked to their Medicaid claims obtained from 36 states. Caseworker determinations of maltreatment were compared with eight sets of ICD-9-CM codes. Of the 1,921 children identified by caseworkers as being maltreated, 15.2% had any relevant ICD-9-CM code in any of their Medicaid files across 4 years of observation. Maltreated boys and those of African American race had lower odds of displaying a maltreatment code. Using only Medicaid claims to identify maltreated children creates validity problems. Medicaid data linkage with other types of administrative data is required to better identify maltreated children.

This paper discusses statistical models for multilevel ordinal data that may be more appropriate for prevention outcomes than are models that assume continuous measurement and normality. Prevention outcomes often have distributions that make them inappropriate for many popular statistical models that assume normality, and are more appropriately considered ordinal outcomes. Despite this, the modeling of ordinal outcomes is often not well understood. This article discusses ways to analyze multilevel ordinal outcomes that are clustered or longitudinal, including the proportional odds regression model for ordinal outcomes, which assumes that the covariate effects are the same across the levels of the ordinal outcome. The article will cover how to test this assumption and what to do if it is violated. It will also discuss application of these models using computer software programs.

The threshold of change model (TCM) is a statistical technique for analyzing ordered stages of change variables. TCM focuses on the thresholds that separate the ordered stages, and the effects of explanatory variables are evaluated in terms of raising or lowering the thresholds. TCM also allows the explanatory variables to exert differential influence on each threshold. In this paper, we use TCM to analyze the data from a clinical trial that compared assertive community treatment (ACT) with standard case management (SCM) for patients with co-occurring severe mental illness and substance use disorder. Endpoint data (36-month follow up) were used for this analysis. The response variable is the recoded Substance Abuse Treatment Scale with three ordered levels (engagement/persuasion, active treatment, and recovery/relapse prevention), and hence two thresholds. The explanatory variables are gender and group (ACT vs. SCM). The results indicate that gender exerts constant and significant effects on both thresholds. The group effect is somewhat mixed: ACT lowers the first threshold (active treatment), but raises the second threshold (recovery/relapse prevention).

Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.

Stage models are prominent in research describing health behavior change. Since stage models often propose that different factors have varying influences on membership in the different stage, statistical methods that can estimate the thresholds that separate the stages and the relative value of variables in influencing these thresholds are useful. This article describes use of a "thresholds of change" model for analyzing the thresholds separating stages and specifically for examining the effects of explanatory variables on these thresholds using a generalization of an ordinal logistic (or probit) regression model. Data from a skin cancer prevention study (N=3,185) in which participants were grouped into three stages for sunscreen use (precontemplation, contemplation, and action) are used to illustrate the Thresholds of Change Model. For this example, two thresholds exist: a contemplation (between precontemplation and contemplation) and an action threshold (between contemplation and action). Variables examined include gender, skin type, perceived susceptibility to sunburn, worry about skin cancer, and sun protection self-efficacy. We examine models that assume that the effects of these variables are the same across thresholds, and then allow the effects of these variables to vary across thresholds. Results indicate that perceived susceptibility has an equal effect on both thresholds, but that worry and self-efficacy have differential effects: worry exerts a greater influence on the contemplation threshold, whereas self-efficacy has a significantly stronger effect on the action threshold. Gender also has a stronger effect on the action threshold; males were less likely to be classified in the action stage than females. This analytic approach has broad applications to many types of stage data.

Background Hospital-acquired pressure ulcers (HAPUs) are costly to treat and can result in Medicare reimbursement penalties. Statistical models can identify patients at greatest HAPU risk and improve prevention. Objectives To use electronic health record (EHR) data to predict HAPUs among hospitalized patients. Methods EHR data were obtained from an academic medical center that included hospitalized patients with at least 1 skin examination between 2011–2014. These data contained encounter-level demographic variables, diagnoses, prescription drugs and provider orders. HAPUs were defined by stages III, IV or unstageable pressure ulcers not present-on-admission as a secondary diagnosis, and excluded diagnosis of paraplegia/quadriplegia. Random forests and k-means clustering were applied to reduce the dimensionality of the large dataset. A 2-level mixed-effects logistic regression of patient-encounters evaluated associations between covariates and HAPU incidence (Equation 1). Results The approach produced a sample population of 23,054 patients with 1,549 HAPUs. The mixed-effects model predicted HAPUs with exceptional (99%) accuracy for a rare event (table 1). The greatest odds ratio (OR) of HAPU incidence was among patients diagnosed with spinal cord injury (ICD-9 907.2: OR=247.4; P

In this article we describe a model for multilevel ordinal response data that allows for non- proportional odds for a subset of explanatory variables. As applied to stages of change data, which are commonly encountered in health promotion research, this model is termed the multilevel thresholds of change model since it focuses on modeling the K-I thresholds that delineate membership in the K ordered stages. Explanatory variables can have the same effect across thresholds (i.e., proportional odds) or varying effects across thresholds (i.e., non- proportional odds). In addition to the explanatory variables of the model, random effects are included to account for the multilevel structure of the data (e.g., repeated observations within subjects, or subjects observed within clusters). A maximum marginal likelihood (MML) solution is described using Gauss-Hermite quadrature to numerically integrate over the distribution of normally-distributed random effects. Data from a skin cancer prevention study, in which su...

The co-twin control design has been widely used in studying the effects of environmental factors on the development of diseases. For binary outcomes that arise from co-twin control studies, the conditional likelihood method is commonly used. This approach, however, does not readily extend to ordinal response data because the standard conditional likelihood does not exist for cumulative logit or proportional odds models. In this paper, we investigate the applicability of the random-effects and GEE approaches in analysing ordinal response data from co-twin control studies. Using both approaches, we re-analyse data from a co-twin control study of the impact of military services during the Vietnam era on post-traumatic stress disorders (PTSD). The ordinal models have considerably increased power in detecting the effects of exposure when compared to the analyses using a dichotomized response. We discuss the interpretation of the estimates from GEE and random-effects models in the context of the twin data. © 1998 John Wiley & Sons, Ltd.

We report on the development, reliability, and validity of the Altman Self-Rating Mania Scale (ASRM). The ASRM was completed during medication washout and after treatment by 22 schizophrenic, 13 schizoaffective, 36 depressed, and 34 manic patients. The Clinician-Administered Rating Scale for Mania (CARS-M) and Mania Rating Scale (MRS) were completed at the same time to measure concurrent validity. Test-retest reliability was assessed separately on 20 depressed and 10 manic patients who completed the ASRM twice during washout. Principal components analysis of ASRM items revealed three factors: mania, psychotic symptoms, and irritability. Baseline mania subscale scores were significantly higher for manic patients compared to all other diagnostic groups. Manic patients had significantly decreased posttreatment scores for all three subscales. ASRM mania subscale scores were significantly correlated with MRS total scores (r = .718) and CARS-M mania subscale scores (r = .766). Test-retest reliability for the ASRM was significant for all three subscales. Significant differences in severity levels were found for some symptoms between patient ratings on the ASRM and clinician ratings on the CARS-M. Mania subscale scores of greater than 5 on the ASRM resulted in values of 85.5% for sensitivity and 87.3% for specificity. Advantages of the ASRM over other self-rating mania scales are discussed.

The present study tested whether smokeless tobacco (ST) fits a unidimensional model of drug involvement and tried to locate ST along the dimension that underlies drug use. The latent trait analysis was employed to quantify drug involvement in a sample of high school students. Analyses showed that although ST use fits the unidimensional model of drug involvement, the place of ST use along the continuum of drug involvement is not stable and differs by gender and ethnicity. Particularly for males, ST use is likely to be preceded by soft drug use and followed by hard drug use; for females, however, ST use is closely associated with hard drug use. The analyses also revealed that the fit of the unidimensional model and the location of ST use along the dimension vary with different ethnic groups.

Random-effects regression models have become increasingly popular for analysis of longitudinal data. A key advantage of the random-effects approach is that it can be applied when subjects are not measured at the same number of timepoints. In this article we describe use of random-effects pattern-mixture models to further handle and describe the influence of missing data in longitudinal studies. For this approach, subjects are first divided into groups depending on their missing-data pattern and then variables based on these groups are used as model covariates. In this way, researchers are able to examine the effect of missing-data patterns on the outcome (or outcomes) of interest. Furthermore, overall estimates can be obtained by averaging over the missing-data patterns. A psychiatric clinical trials data set is used to illustrate the random-effects pattern-mixture approach to longitudinal data analysis with missing data. Longitudinal studies occupy an important role in psychological and psychiatric research. In these studies the same individuals are repeatedly measured on a number of important variables over a series of timepoints. As an example, a longitudinal design is often used to determine whether a particular therapeutic agent can produce changes in clinical status over the course of an illness. Another application for the longitudinal study is to assess potential indicators of a change, in the subject's clinical status; for example, the assessment of whether drug plasma level measurements indicate clinical outcome. Even in well-controlled situations, missing data invariably occur in longitudinal studies. Subjects can be

Objectives. Cognitive processing plays an important role in balance and gait and is a contributing factor to falls in older adults. This relationship may be explained by the fact that higher order cognitive functions such as executive functions are called upon while walking. The purpose of this study was to examine whether a cognitive training intervention leads to significant improvements on measures of balance and gait. Method. This randomized trial tested whether cognitive training over 10 weeks improves balance and gait in older adults. Participants were randomly assigned to a computer-based cognitive training intervention or measurement-only control. Outcomes included Timed Up and Go (TUG), gait speed, and gait speed with a cognitive distraction. Data were analyzed using analysis of covariance models with change scores. Results. Participants’ (N = 51) average age was 82.7 for those randomized to intervention and 81.1 for those randomized to control. After 10 weeks, intervention group participants performed significantly better than controls on the TUG. When the cohort was limited to those categorized as slow walkers (baseline 10-m walk ≥ 9 s), intervention participants performed significantly better than controls on TUG and distracted walking. Discussion. Cognitive training slows degradation of balance and improves gait while distracted, rendering it a promis

INTRODUCTION Ecological momentary assessments (EMAs) are increasingly used in smoking research to understand contextual and individual differences related to smoking and changes in smoking. To date, there has been little detailed research into the predictors of EMA compliance. However, patterns or predictors of compliance may affect key relationships under investigation and introduce sources of bias in results. The purpose of this study was to investigate predictors of compliance to random prompts among a sample of adolescents who had ever smoked. METHODS Data for this study were drawn from a sample of 461 adolescents (9th and 10th graders at baseline) participating in a longitudinal study of smoking escalation. We examined 2 outcomes: subject-level EMA compliance (overall rate of compliance over a week-long EMA wave), and in-the-moment prompt-level compliance to the most proximal random prompt. We investigated several covariates including gender, race, smoking rate, alcohol use, psychological symptomatology, home composition, mood, social context, time in study, inter-prompt interval, and location. RESULTS At the overall subject level, higher mean negative affect, smoking rate, alcohol use, and male gender predicted lower compliance with random EMA prompts. At the prompt level, after controlling for significant subject-level predictors of compliance, increased positive affect, being outside of the home, and longer inter-prompt interval predicted lower momentary compliance. CONCLUSIONS This study identifies several factors associated with overall and momentary EMA compliance among a sample of adolescents participating in a longitudinal study of smoking. We also propose a conceptual framework for investigating the contextual and momentary predictors of compliance within EMA studies.

INTRODUCTION Ecological momentary assessments (EMAs) are useful for understanding both between- and within-subject dynamic changes in smoking and mood. Modeling 2 moods (positive affect [PA] and negative affect [NA], PA and NA) simultaneously will better enable researchers to explore the association between mood variables and what influences them at both the momentary and subject level. METHODS The EMA component of a natural history study of adolescent smoking was analyzed with a bivariate location-scale mixed-effects model. The proposed model separately estimates the between- and within-subject variances and jointly models the 2 mood constructs. A total of 461 adolescents completed the baseline EMA wave, which resulted in 14,105 random prompts. Smoking level, represented by the number of smoking events on EMA, entered the model as 2 predictors: one that compared nonsmokers during the EMA week to 1-cigarette smokers, and the second one that estimated the effect of smoking level on mood among smokers. RESULTS Results suggest that nonsmokers had more consistent positive and negative moods compared to 1-cigarette smokers. Among those who smoked, both moods were more consistent at higher smoking levels. The effects of smoking level were greater for NA than for PA. The within-subject association between mood constructs was negative and strongest among 1-cigarette smokers; the within-subject association between positive and negative moods was negatively associated with smoking. CONCLUSIONS Mood variation and association between mood constructs varied across smoking levels. The most infrequent smokers were characterized with more inconsistent moods, whereas mood was more consistent for subjects with higher smoking levels.

Attempts to characterize the momentary antecedents of smoking are among the earliest and most prominent uses of ecological momentary assessment (EMA) methodology in tobacco research. In this work, the frequencies of various contextual attributes recorded in user-logged smoking events are contrasted with the base rates at which each is observed during time-sampled diary entries (Paty, Kassel, & Shiffman, 1992). Features that are found more commonly during smoking than background states may represent triggers for cigarette use. Theoretically, the salient triggers for smoking might change across the early smoking career as tobacco dependence progresses. The nature of such changes might help to refine our theoretical understanding of tobacco dependence and inform tobacco prevention and intervention efforts. Investigators have attempted to investigate this issue by collecting EMA data from samples representing a range of tobacco involvement, then testing whether dependence level or daily smoking moderates associations between measured antecedent conditions and smoking events (e.g., Cronk & Piasecki, 2010; Krukowski, Solomon, & Naud, 2005; Otsuki, 2009; Otsuki, Tinsley, Chao, & Unger, 2008; Shiffman & Paty, 2006; Shiffman & Rathbun, 2011). Findings from these studies vary, but they frequently indicate that cigarette use among smokers higher in dependence is more weakly related to specific contextual setting events. Conversely, smoking among less dependent individuals occurs under more circumscribed conditions, especially “indulgent” conditions such as relaxing, socializing, eating, or drinking alcohol (Shiffman & Paty, 2006). Such results have been interpreted as suggesting that the progression of tobacco dependence is accompanied by an erosion of stimulus control over smoking (Piasecki, Richardson, & Smith, 2007; Shiffman & Paty, 2006). With increasing smoking exposure, cigarette use is thought to be increasingly driven by habit and withdrawal avoidance. Although the existing EMA findings provide valuable clues about the trajectory of smoking patterns, they rely on comparing data from individuals who differ in dependence level at a single point in time. Thus, these designs cannot rigorously resolve the developmental question of whether the triggers for smoking evolve as a given individual’s dependence grows. An alternate explanation might be that individuals who eventually develop dependence have always used cigarettes in distinctive conditions compared to persons destined to be long-term chippers, intermittent smokers, or those who “mature out” of smoking experimentation (Shiffman & Paty, 2006; Shiffman et al., 2012). Longitudinal studies incorporating multiple waves of EMA data collection are needed to more directly address this developmental question. In this article, we investigate this question using data from a study of adolescents recruited in 9th and 10th grade and followed for 2 years. During this span, participants completed up to four 1-week bouts of EMA recording using electronic diaries, answering randomly timed prompts, and logging smoking events. At each wave, participants also completed a youth-specific version of the Nicotine Dependence Syndrome Scale (NDSS; Sterling et al., 2009). This design permits a separation of the NDSS data variation within a multilevel model into between-person and intraindividual components, allowing direct tests of whether each aspect moderates associations between contextual antecedents and smoking behavior.

Background. Neurorehabilitation studies suggest that manipulation of error signals during practice can stimulate improvement in coordination after stroke. Objective. To test visual display and robotic technology that delivers augmented error signals during training, in participants with stroke. Methods. A total of 26 participants with chronic hemiparesis were trained with haptic (via robot-rendered forces) and graphic (via a virtual environment) distortions to amplify upper-extremity (UE) tracking error. In a randomized crossover design, the intervention was compared with an equivalent amount of practice without error augmentation (EA). Interventions involved three 45-minute sessions per week for 2 weeks, then 1 week of no treatment, and then 2 additional weeks of the alternate treatment. A therapist provided a visual cursor using a tracking device, and participants were instructed to match it with their hand. Haptic and visual EA was used with blinding of participant, therapist, technician-operator, and evaluator. Clinical measures of impairment were obtained at the beginning and end of each 2-week treatment phase as well as at 1 week and at 45 days after the last treatment. Results. Outcomes showed a small, but significant benefit to EA training over simple repetitive practice, with a mean 2-week improvement in Fugl-Meyer UE motor score of 2.08 and Wolf Motor Function Test of timed tasks of 1.48 s. Conclusions. This interactive technology may improve UE motor recovery of stroke-related hemiparesis.

Adolescent alcohol use is a serious public health concern. Despite advances in the theoretical conceptualization of pathways to alcohol use, researchers are limited by the statistical techniques currently available. Researchers often fit linear models and restrictive categorical models (e.g., proportional odds models) to ordinal data with many response categories defined by collapsed count data (0 drinking days, 1-2 days, 3-6 days, etc.). Consequently, existing models ignore the underlying count process, resulting in disjoint between the construct of interest and the models being fitted. Our proposed ordinal modeling approach overcomes this limitation by explicitly linking ordinal responses to a suitable underlying count distribution. In doing so, researchers can use maximum likelihood estimation to fit count models to ordinal data as if they had directly observed the underlying discrete counts. The usefulness of our ordinal negative binomial and ordinal zero-inflated negative binomial models is verified by simulation studies. We also demonstrate our approach using real empirical data from the 2010 National Survey of Drug Use and Health. Results show the benefit of the proposed ordinal modeling framework compared with existing methods.

BACKGROUND: In three randomized trials medical male circumcision (MMC) reduced HIV acquisition in heterosexual men in sub-Saharan Africa by approximately 60% after 21-24 months of follow-up. We estimated the 72-month efficacy of MMC against HIV among men retained in the Kisumu randomized trial in which HIV acquisition was reduced by 60% after 24 months. METHODS: From 2002 to 2005 2784 men aged 18-24 were enrolled and randomized 1 : 1 to immediate circumcision or control. At trial end in December 2006 control men were offered free circumcision. Follow-up continued to September 2010. Cox proportional hazards regression incorporating stabilized inverse probability of treatment and censoring weights generated through marginal structural modeling was used to account for potential time-varying confounding and censoring to estimate the efficacy of MMC on HIV risk. RESULTS: The cumulative 72-month HIV incidence was 7.21% [95% confidence interval (CI): 5.98-8.68%]: 4.81% among circumcised men 11.0% among uncircumcised men. The crude hazard ratio of HIV seroconversion for circumcised vs. uncircumcised men was 0.38 [95% CI: 0.26-0.55]. In weight-adjusted Cox regression the hazard ratio was 0.42 [95% CI: 0.26-0.66]. CONCLUSION: The efficacy of MMC was sustained at 58% at 72 months similar to overall findings of the three trials under conditions of randomization. These findings provide an estimate of the long-term efficacy of circumcision against HIV acquisition. Our results support programmatic scale-up recommendations that are based on assumptions of sustained efficacy.

The true missing data mechanism is never known in practice. We present a method for generating multiple imputations for binary variables, which formally incorporates missing data mechanism uncertainty. Imputations are generated from a distribution of imputation models rather than a single model, with the distribution reflecting subjective notions of missing data mechanism uncertainty. Parameter estimates and standard errors are obtained using rules for nested multiple imputation. Using simulation, we investigate the impact of missing data mechanism uncertainty on post-imputation inferences and show that incorporating this uncertainty can increase the coverage of parameter estimates. We apply our method to a longitudinal smoking cessation trial where nonignorably missing data were a concern. Our method provides a simple approach for formalizing subjective notions regarding nonresponse and can be implemented using existing imputation software.

INTRODUCTION Maternal smoking during pregnancy (MSP) is a known risk factor for regular smoking in young adulthood and may pose a risk independently of mother's lifetime smoking. The processes through which MSP exerts this influence are unknown but may occur through greater smoking quantity and frequency following initiation early in adolescence or increased sensitivity to nicotine dependence (ND) at low levels of smoking. METHODS This study used path analysis to investigate adolescent smoking quantity, smoking frequency, and ND as potential simultaneous mediating pathways through which MSP and mother's lifetime smoking (whether she has ever smoked) increase the risk of smoking in young adulthood among experimenters (at baseline, 100 cigarettes/lifetime). RESULTS For experimenters, MSP was directly associated with more frequent young adult smoking and was not mediated by adolescent smoking behavior or ND. Independently of MSP, the effect of mother's lifetime smoking was fully mediated through frequent smoking and was heightened ND during adolescence. Controlling for MSP eliminated a previously observed direct association between mother's lifetime smoking and future smoking among experimenters. For current smokers, only prior smoking behavior was associated with future smoking frequency. CONCLUSIONS These results seem to rule out sensitivity to ND and increased smoking behavior as contributing pathways of MSP. Further, the impact of MSP on young adult smoking extends beyond that of having an ever-smoking mother. Future work should test other possible mediators; for example, MSP-related epigenetic changes or gene variants influencing the brain's nicotine response.

MIXREGLS is a program which provides estimates for a mixed-effects location scale model assuming a (conditionally) normally-distributed dependent variable. This model can be used for analysis of data in which subjects may be measured at many observations and interest is in modeling the mean and variance structure. In terms of the variance structure, covariates can by specified to have effects on both the between-subject and within-subject variances. Another use is for clustered data in which subjects are nested within clusters (e.g., clinics, hospitals, schools, etc.) and interest is in modeling the between-cluster and within-cluster variances in terms of covariates. MIXREGLS was written in Fortran and uses maximum likelihood estimation, utilizing both the EM algorithm and a Newton-Raphson solution. Estimation of the random effects is accomplished using empirical Bayes methods. Examples illustrating stand-alone usage and features of MIXREGLS are provided, as well as use via the SAS and R software packages.

This article describes and illustrates use of random-effects regression models (RRM) in relapse research. RRM are useful in longitudinal analysis of relapse data since they allow for the presence of missing data, time-varying or invariant covariates, and subjects measured at different timepoints. Thus, RRM can deal with "unbalanced" longitudinal relapse data, where a sample of subjects are not all measured at each and every timepoint. Also, recent work has extended RRM to handle dichotomous and ordinal outcomes, which are common in relapse research. Two examples are presented from a smoking cessation study to illustrate analysis using RRM. The first illustrates use of a random-effects ordinal logistic regression model, examining longitudinal changes in smoking status, treating status as an ordinal outcome. The second example focuses on changes in motivation scores prior to and following a first relapse to smoking. This latter example illustrates how RRM can be used to examine predictors and consequences of relapse, where relapse can occur at any study timepoint.

In this report we present evidence that early social experience influences aspects of the function of brain biogenic amine systems, most notably the noradrenergic system. Biogenic amine activity was studied in mother- vs. peer-reared monkey infants over the first 6 months of life and in response to two housing transitions. Norepinephrine (NE), 3-methoxy-4-hydroxyphenylglycol (MHPG), dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), and 5-hydroxyindoleacetic acid (5-HIAA) levels in cerebrospinal fluid (CSF) were measured. Peer-reared monkeys showed significantly higher CSF levels of norepinephrine and MHPG than mother-reared animals over early development, but showed an attentuated NE response to separation and group formation compared to mother-reared animals. Peer-reared monkeys showed a greater developmental decline in 5-HIAA levels than mother-reared monkeys. There were no rearing effects for DOPAC or HVA over early development; however, peer-reared monkeys showed significantly lower HVA and DOPAC concentrations at 6-8 months of age. The results add to evidence for the influence of primate mothers on the psychobiological development of central nervous system neurotransmitter systems in their infants, and suggest that the noradrenergic system is among the more sensitive of these to early experience.

Most school-based smoking prevention studies employ designs in which schools or classrooms are assigned to different treatment conditions while observations are made on individual students. This design requires that the treatment effect be assessed against the between-school variance. However, the between-school variance is usually larger than the variance that would be obtained if students were individually randomized to different conditions. Consequently, the power of the test for a treatment effect is reduced, and it becomes difficult to detect important treatment effects. To assess the potential loss of power or to calculate appropriate sample sizes, investigators need good estimates of the intraclass correlations for the variables of interest. The authors calculated intraclass correlations for some common outcome variables in a school-based smoking prevention study, using a three-level model-i.e., students nested within classrooms and classrooms nested within schools. The authors present the intraclass correlation estimates for the entire data set, as well as separately by sex and ethnicity. They also illustrate the use of these estimates in the planning of future studies.

MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model can be used for analysis of unbalanced longitudinal data, where individuals may be measured at a different number of timepoints, or even at different timepoints. Autocorrelated errors of a general form or following an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also be used for analysis of clustered data, where the mixed-effects model assumes data within clusters are dependent. The degree of dependency is estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm and a Fisher-scoring solution. For the scoring solution, the covariance matrix of the random effects is expressed in its Gaussian decomposition, and the diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished using an empirical Bayes approach. Examples illustrating usage and features of MIXREG are provided.

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR 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 effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

This research examines the relative importance of parental and friends' influences on adolescents' smoking behavior and changes in the effects of social influences during adolescence. Data were collected at 4 times from 7th to 9th grades. Random-effects ordinal regression models were employed to predict the repeated classification of adolescent smoking status using time effects, prior smoking status, friends' smoking, and parental smoking. In general, the effects of friends' smoking are stronger than those of parental smoking, and these differences increase over time. In addition, friends' smoking has greater effects on nonsmokers than smokers. Separate models for males and females disclose some gender differences. In particular, the effects of friends' smoking are stronger for females than for males, and the increasing trend of friends' influences is more noticeable for females than for males. Models for 4 ethnic groups also suggest differential susceptibility to social influences in different cultures.

Introduction Prior research suggests the CHRNA5A3B4 and CHRNB3A6 gene clusters have independent effects on smoking progression in young smokers. Here classification tree analysis uncovers conditional relations between these genes. Methods Conditional classification tree and random forest analyses were employed to predict daily smoking at 6-year follow-up in a longitudinal sample of young smokers (N = 480) who had smoked at least one puff at baseline and were of European ancestry. Potential predictors included gender, lifetime smoking, Nicotine Dependence Syndrome Scale (NDSS), and five single nucleotide polymorphisms (SNPs) tagging CHRNB3A6 and CHRNA5A3B4 Haplotypes A, B, and C. Conditional random forest analysis was used to calculate variable importance. Results The classification tree identified NDSS, the CHRNB3A6 SNP rs2304297, and the CHRNA5A3B4 Haplotype C SNP rs6495308 as predictive of year 6 daily smoking with the baseline NDSS identified as the strongest predictor. The CHRNB3A6 protective effect was contingent on a lower level of baseline NDSS, whereas the CHRNA5A3B4 Haplotype C protective effect was seen at a higher level of baseline NDSS. A CHRNA5A3B4 Haplotype C protective effect also was observed in participants with low baseline NDSS who had no CHRNB3A6 rs2304297 minor allele. Conclusions The protective effects of CHRNA5A3B4 Haplotype C and CHRNB3A6 on smoking progression are conditional on different levels of baseline cigarette use. Also, duplicate dominant epistasis between SNPs indicated the minor allele of either SNP afforded comparable protective effects in the absence of a minor allele at the other locus. Possible mechanisms underlying these conditional relations are discussed. Implications The substantive contributions of this paper are the demonstration of a difference in the protective effects of CHRNB3A6 and CHRNA5A3B4 Haplotype C in young smokers attributable to level of cigarette use, as well as observation of duplicate dominant epistasis between the two markers. The methodological contribution is demonstrating that classification tree and random forest statistical methods can uncover conditional relations among genetic effects not detected with more common regression methods.

Background. This paper presents the student outcomes of a large-scale, social-influences-based, school and mediabased tobacco use prevention and cessation project in Southern California. Methods. The study provided an experimental comparison of classroom delivery with television delivery and the combination of the two in a 2 x 2 plus 1 design. Schools were randomly assigned to conditions. Control groups included “treatment as usual” and an “attention control” with the same outcome expectancies as the treatment conditions. Students were surveyed twice in grade 7 and once in each of grades 8 and 9. The interventions occurred during grade ‘7. Results. We observed significant effects on mediating variables such as knowledge, prevalence estimates, and coping effort. The knowledge and prevalence estimates effects decayed partially but remained significant up to a 2-year follow-up. The coping effort effect did not persist at follow-ups. There were significant main effects of both classroom training and TV programming on knowledge and prevalence estimates and significant interactions of classroom and TV programming on knowledge (negative), disapproval of parental smoking, and coping effort. There were no consistent program effects on refusal/self-efficacy, smoking intentions, or behavior. Conclusions. Previous reports demonstrated successful development and pilot testing of program components and measures and high acceptance of the program by students and parents. The lack of behavioral effects may have been the result of imperfect program implementation or low base

In studies using ecological momentary assessment (EMA), or other intensive longitudinal data collection methods, interest frequently centers on changes in the variances, both within-subjects and between-subjects. For this, Hedeker et al. (Biometrics 2008; 64: 627-634) developed an extended two-level mixed-effects model that treats observations as being nested within subjects and allows covariates to influence both the within-subjects and between-subjects variance, beyond their influence on means. However, in EMA studies, subjects often provide many responses within and across days. To account for the possible systematic day-to-day variation, we developed a more flexible three-level mixed-effects location scale model that treats observations within days within subjects, and allows covariates to influence the variance at the subject, day, and observation level (over and above their usual effects on means) using a log-linear representation throughout. We provide details of a maximum likelihood solution and demonstrate how SAS PROC NLMIXED can be used to achieve maximum likelihood estimates in an alternative parameterization of our proposed three-level model. The accuracy of this approach using NLMIXED was verified by a series of simulation studies. Data from an adolescent mood study using EMA were analyzed to demonstrate this approach. The analyses clearly show the benefit of the proposed three-level model over the existing two-level approach. The proposed model has useful applications in many studies with three-level structures where interest centers on the joint modeling of the mean and variance structure.

Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.

Many patients exhibit multiple chronic disease risk behaviors. Research provides little information about advice that can maximize simultaneous health behavior changes.To test which combination of diet and activity advice maximizes healthy change, we randomized 204 adults with elevated saturated fat and low fruit and vegetable intake, high sedentary leisure time, and low physical activity to 1 of 4 treatments: increase fruit/vegetable intake and physical activity, decrease fat and sedentary leisure, decrease fat and increase physical activity, and increase fruit/vegetable intake and decrease sedentary leisure. Treatments provided 3 weeks of remote coaching supported by mobile decision support technology and financial incentives. During treatment, incentives were contingent on using the mobile device to self-monitor and attain behavioral targets; during follow-up, incentives were contingent only on recording. The outcome was standardized, composite improvement on the 4 diet and activity behaviors at the end of treatment and at 5-month follow-up.Of the 204 individuals randomized, 200 (98.0%) completed follow-up. The increase fruits/vegetables and decrease sedentary leisure treatments improved more than the other 3 treatments (P.001). Specifically, daily fruit/vegetable intake increased from 1.2 servings to 5.5 servings, sedentary leisure decreased from 219.2 minutes to 89.3 minutes, and saturated fat decreased from 12.0% to 9.5% of calories consumed. Differences between treatment groups were maintained through follow-up. Traditional dieting (decrease fat and increase physical activity) improved less than the other 3 treatments (P.001).Remote coaching supported by mobile technology and financial incentives holds promise to improve diet and activity. Targeting fruits/vegetables and sedentary leisure together maximizes overall adoption and maintenance of multiple healthy behavior changes.

Introduction Measures of nicotine dependence typically use the item average or total score from rating scales, such as the Nicotine Dependence Syndrome Scale (NDSS). Alternatively, item response theory (IRT) methods can provide useful item-specific information. IRT methods developed for longitudinal data can additionally provide information about item-specific changes over time. Methods We describe a longitudinal 2-parameter ordinal IRT model, and compare the results from this model with those from an IRT model for only the baseline item responses, and a conventional longitudinal analysis of the item-average NDSS score. We examined a 10-item, adolescent version of the NDSS at baseline, 6, 15, and 24 months for 1,097 9th or 10th graders. Results IRT analysis of the baseline data revealed that the items "willing to go out of the house in a storm to find a cigarette," "choose to spend money on cigarettes than lunch," "function better after morning cigarette," and "worth smoking in cold or rain," were good items at distinguishing individuals' levels of nicotine dependency. While the analysis of the averaged NDSS score indicated linear growth over time, the longitudinal IRT method revealed that only 5 out of the 10 items showed statistical increase over time. Conclusions Infrequently endorsed NDSS items were generally better able to distinguish higher levels of dependency. The endorsement of such items increased over time. Items that changed significantly over time reflected the general drive concept of dependence, as well as the total first overarching dimension of dependence.

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 analysis of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies.

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 = . 9 7 ). Across conditions, a main effect of financial motivation predicted a steeper rate of weight regained during the maintenance period, 𝑡 ( 1 6 5 ) = 2 . 1 5 , 𝑃 = . 0 4 . Furthermore, financial motivation and gender interacted significantly in predicting maintenance of healthy diet and activity changes, 𝑡 ( 1 6 0 ) = 2 . 4 2 , 𝑃 = . 0 1 6 , 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.

Situations in which multiple outcomes and predictors of different distributional types are collected are becoming increasingly common in public health practice, and joint modeling of mixed types has been gaining popularity in recent years. Evaluation of various statistical techniques that have been developed for mixed data in simulated environments necessarily requires joint generation of multiple variables. Most massive public health data sets include different types of variables. For instance, in clustered or longitudinal designs, often multiple variables are measured or observed for each individual or at each occasion. This work is motivated by a need to jointly generate binary and possibly non-normal continuous variables. We illustrate the use of power polynomials to simulate multivariate mixed data on the basis of a real adolescent smoking study. We believe that our proposed technique for simulating such intensive data has the potential to be a handy methodological addition to public health researchers' toolkit.

The US Food and Drug Administration issued separate warnings for suicidality with antidepressants and antiepileptic drugs in the past 5 years. This study describes methods for examining the association of these agents with suicide attempts and suicide deaths in more broadly generalizable samples than examined by the US Food and Drug Administration. An observational study of mood disorders was examined that includes three decades of prospective assessments. Because of sample size differences, two distinct longitudinal implementations of the propensity adjustment are used in separate analyses of antidepressants and antiepileptic drugs. Propensity score quintile-stratified safety analyses were used with the large antidepressant data set; whereas, propensity score matched safety analyses were used with the smaller antiepileptic drug data because stratification was not feasible. In each case, mixed-effects survival models compared the safety of participants when receiving the respective class of medication to periods when they did not receive that medication. When participants were more severely ill, they were significantly more likely to receive either class of psychotropics. Propensity quintile-stratified safety analyses found that risk of suicide attempts or suicides was significantly reduced when participants received antidepressants. In contrast, propensity score matched safety analyses found neither significant risk nor protection from suicidality among participants receiving antiepileptics.

We use Oregon state data (1981-1990) on medical malpractice claims to develop a random-effects probit model for vulnerability to a medical malpractice claim in practice year k (k = 1, 2, …, ni ) for physician i(i = 1, 2, …, N physicians in the sample) conditional on an ni × p covariate matrix Wi that contains a mixture of p time-varying and time-invariant covariates. In this application, time-invariant covariates were physician sex and specialty (surgical versus nonsurgical). Time-varying covariates were age, the cumulative amount of risk management education (i.e., number of courses) taken by physician i to year k, and prior claim history. In addition, the model incorporates a random effect of “claim vulnerability” assumed to be normally distributed in the population of physicians. This random effect represents unobservable and/or unmeasured characteristics that place one physician at greater risk for experiencing a medical malpractice claim than another physician. In addition, we also determine...

This study describes the procedures used to establish videotaped inter-rater reliability in a multicenter study investigating the clinical efficacy of divalproex sodium for the treatment of acute mania. A total of 46 raters from 10 centers viewed and rated five videotaped diagnostic patients interviews using a modified SADS and SADS-C. Raters also viewed a follow-up videotaped interview at the midpoint of the study. Raters rated the quality of each interview for its teaching potential. Agreement among raters within study sites was estimated by the intraclass correlation coefficient. Results demonstrated significant agreement among raters within study centers at baseline and follow-up for both the SADS and SADS-C.

Summary A preventive, community-based program was designed to facilitate high school transition and reduce dropout. Participants in the study were ninth grade, predominantly Hispanic inner-city students. School absence was used as a variable to identify students who were at high risk for failure. Compared with low- and middle-absence groups, the high-absence groups' failures and class rankings deteriorated consistently across time points. In addition, over time, high-absence group students experienced more pronounced increases in school absence and decreases in academic achievement relative to low- and middle-absence counterparts. Findings are discussed in terms of their implications for dropout prevention programming.