The Effect of Chronic Altitude Exposure on COPD Outcomes in the SPIROMICS Cohort.

RATIONALE: Individuals with COPD have airflow obstruction and maldistribution of ventilation. For those living at high altitude, any gas exchange abnormality is compounded by reduced partial pressures of inspired oxygen. OBJECTIVES: Does residence at higher-altitude exposure affect COPD outcomes, including lung function, imaging characteristics, symptoms, health status, functional exercise capacity, exacerbations, or mortality? METHODS: From the SPIROMICS cohort, we identified individuals with COPD living below 1,000 ft (305 m) elevation (n= 1,367) versus above 4,000 ft (1,219 m) elevation (n= 288). Multivariable regression models were used to evaluate associations of exposure to high altitude with COPD-related outcomes. MEASUREMENTS AND MAIN RESULTS: Living at higher altitude was associated with reduced functional exercise capacity as defined by 6MWD (-32.3 m, (-55.7 to -28.6)). There were no differences in patient-reported outcomes as defined by symptoms (CAT, mMRC), or health status (SGRQ). Higher altitude was not associated with a different rate of FEV1 decline. Higher altitude was associated with lower odds of severe exacerbations (IRR 0.65, (0.46 to 0.90)). There were no differences in small airway disease, air trapping, or emphysema. In longitudinal analyses, higher altitude was associated with increased mortality (HR 1.25, (1.0 to 1.55)); however, this association was no longer significant when accounting for air pollution. CONCLUSIONS: Chronic altitude exposure is associated with reduced functional exercise capacity in individuals with COPD, but this did not translate into differences in symptoms or health status. Additionally, chronic high-altitude exposure did not affect progression of disease as defined by longitudinal changes in spirometry.

Longitudinal Follow-Up of Children With HLHS and Association Between Norwood Shunt Type and Long-Term Outcomes: The SVR III Study.

OBJECTIVE: In the SVR trial (Single Ventricle Reconstruction), newborns with hypoplastic left heart syndrome were randomly assigned to receive a modified Blalock-Taussig-Thomas shunt (mBTTS) or a right ventricle-to-pulmonary artery shunt (RVPAS) at Norwood operation. Transplant-free survival was superior in the RVPAS group at 1 year, but no longer differed by treatment group at 6 years; both treatment groups had accumulated important morbidities. In the third follow-up of this cohort (SVRIII [Long-Term Outcomes of Children With Hypoplastic Left Heart Syndrome and the Impact of Norwood Shunt Type]), we measured longitudinal outcomes and their risk factors through 12 years of age. METHODS: Annual medical history was collected through record review and telephone interviews. Cardiac magnetic resonance imaging (CMR), echocardiogram, and cycle ergometry cardiopulmonary exercise tests were performed at 10 through 14 years of age among participants with Fontan physiology. Differences in transplant-free survival and complication rates (eg, arrhythmias or protein-losing enteropathy) were identified through 12 years of age. The primary study outcome was right ventricular ejection fraction (RVEF) by CMR, and primary analyses were according to shunt type received. Multivariable linear and Cox regression models were created for RVEF by CMR and post-Fontan transplant-free survival. RESULTS: Among 549 participants enrolled in SVR, 237 of 313 (76%; 60.7% male) transplant-free survivors (mBTTS, 105 of 147; RVPAS, 129 of 161; both, 3 of 5) participated in SVRIII. RVEF by CMR was similar in the shunt groups (RVPAS, 51±9.6 [n=90], and mBTTS, 52±7.4 [n=75]; P=0.43). The RVPAS and mBTTS groups did not differ in transplant-free survival by 12 years of age (163 of 277 [59%] versus 144 of 267 [54%], respectively; P=0.11), percentage predicted peak Vo2 for age and sex (74±18% [n=91] versus 72±18% [n=84]; P=0.71), or percentage predicted work rate for size and sex (65±20% versus 64±19%; P=0.65). The RVPAS versus mBTTS group had a higher cumulative incidence of protein-losing enteropathy (5% versus 2%; P=0.04) and of catheter interventions (14 versus 10 per 100 patient-years; P=0.01), but had similar rates of other complications. CONCLUSIONS: By 12 years after the Norwood operation, shunt type has minimal association with RVEF, peak Vo2, complication rates, and transplant-free survival. RVEF is preserved among the subgroup of survivors who underwent CMR assessment. Low transplant-free survival, poor exercise performance, and accruing morbidities highlight the need for innovative strategies to improve long-term outcomes in patients with hypoplastic left heart syndrome. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT0245531.

Bayesian transition models for ordinal longitudinal outcomes.

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

Modeling multiple correlated end-organ disease trajectories: A tutorial for multistate and joint models with applications in diabetes complications.

State-of-the-art biostatistics methods allow for the simultaneous modeling of several correlated non-fatal disease processes over time, but there is no clear guidance on the optimal analysis in most settings. An example occurs in diabetes, where it is not known with certainty how microvascular complications of the eyes, kidneys, and nerves co-develop over time. In this article, we propose and contrast two general model frameworks for studying complications (sequential state and parallel trajectory frameworks) and review multivariate methods for their analysis, focusing on multistate and joint modeling. We illustrate these methods in a tutorial format using the long-term follow-up from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study public data repository. A formal comparison of prediction error and discrimination is included. Multistate models are particularly advantageous for determining the order and timing of complications, but require discretization of the longitudinal outcomes and possibly a very complex state space process. Intermittent observation of the states must be accounted for, and discretization is a probable disadvantage in this setting. In contrast, joint models can account for variations of continuous biomarkers over time and are particularly designed for modeling complex association structures between the complications and for performing dynamic predictions of an outcome of interest to inform clinical decisions (eg, a late-stage complication). We found that both models have helpful features that can better-inform our understanding of the complex trajectories that complications may take and can therefore help with decision making for patients presenting with diabetes complications.

A Bayesian joint model for multivariate longitudinal and time-to-event data with application to ALL maintenance studies.

The most common type of cancer diagnosed among children is the Acute Lymphocytic Leukemia (ALL). A study was conducted by Tata Translational Cancer Research Center (TTCRC) Kolkata, in which 236 children (diagnosed as ALL patients) were treated for the first two years (approximately) with two standard drugs (6MP and MTx) and were then followed nearly for the next 3 years. The goal is to identify the longitudinal biomarkers that are associated with time-to-relapse, and also to assess the effectiveness of the drugs. We develop a Bayesian joint model in which a linear mixed model is used to jointly model three biomarkers (i.e. white blood cell count, neutrophil count, and platelet count) and a semi-parametric proportional hazards model is used to model the time-to-relapse. Our proposed joint model can assess the effects of different covariates on the progression of the biomarkers, and the effects of the biomarkers (and the covariates) on time-to-relapse. In addition, the proposed joint model can impute the missing longitudinal biomarkers efficiently. Our analysis shows that the white blood cell (WBC) count is not associated with time-to-relapse, but the neutrophil count and the platelet count are significantly associated with it. We also infer that a lower dose of 6MP and a higher dose of MTx jointly result in a lower relapse probability in the follow-up period. Interestingly, we find that relapse probability is the lowest for the patients classified into the "high-risk" group at presentation. The effectiveness of the proposed joint model is assessed through the extensive simulation studies.

SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling.

Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer's Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.

Longitudinal urgency outcomes following robotic-assisted laparoscopic prostatectomy.

PURPOSE: Stress urinary incontinence (SUI) is a well-known adverse outcome following robotic-assisted laparoscopic prostatectomy (RALP). Although postoperative SUI has been extensively studied, little focus has been placed on understanding the natural history and impact of urgency symptoms following RALP. The UVA prostatectomy functional outcomes program (PFOP) was developed to comprehensively assess and optimize continence outcomes following RALP. The present study focuses on assessing urgency outcomes in this cohort. METHODS: PFOP patients with a minimum of 6-months follow up following RALP were included. The PFOP includes prospectively assessed incontinence and quality of life outcomes utilizing ICIQ-MLUTS, Urgency Perception Score (UPS), and IIQ-7 questionnaires. The primary study outcome was urgency urinary incontinence (UUI) as determined by ICIQ-MLUTS UUI domain. Secondary outcomes included urgency (UPS score) and quality of life (IIQ-7). RESULTS: Forty patients were included with median age 63.5 years. Fourteen (35%) patients reported UUI at baseline. UUI and QOL scores worsened compared to baseline at all time-points. Urgency worsened at 3-weeks and 3-months but returned to baseline by 6-months. Notably, 63% of patients without baseline UUI reported de-novo UUI at 6 months. Although QOL was lower in patients with versus without UUI (IIQ-7 score 3.0 vs 0.0, p = 0.009), severity of UUI was not associated with QOL when controlling for SUI severity. CONCLUSION: Our data demonstrate significantly worsened UUI from baseline and a large incidence of de-novo UUI following RALP. Further study is needed to inform how urgency and UUI and its treatment affect health-related quality of life following RALP.

Solutions for surrogacy validation with longitudinal outcomes for a gene therapy.

Valid surrogate endpoints S can be used as a substitute for a true outcome of interest T to measure treatment efficacy in a clinical trial. We propose a causal inference approach to validate a surrogate by incorporating longitudinal measurements of the true outcomes using a mixed modeling approach, and we define models and quantities for validation that may vary across the study period using principal surrogacy criteria. We consider a surrogate-dependent treatment efficacy curve that allows us to validate the surrogate at different time points. We extend these methods to accommodate a delayed-start treatment design where all patients eventually receive the treatment. Not all parameters are identified in the general setting. We apply a Bayesian approach for estimation and inference, utilizing more informative prior distributions for selected parameters. We consider the sensitivity of these prior assumptions as well as assumptions of independence among certain counterfactual quantities conditional on pretreatment covariates to improve identifiability. We examine the frequentist properties (bias of point and variance estimates, credible interval coverage) of a Bayesian imputation method. Our work is motivated by a clinical trial of a gene therapy where the functional outcomes are measured repeatedly throughout the trial.

Gaussian variational approximate inference for joint models of longitudinal biomarkers and a survival outcome.

The shared random effects joint model is one of the most widely used approaches to study the associations between longitudinal biomarkers and a survival outcome and make dynamic risk predictions using the longitudinally measured biomarkers. Various types of joint models have been developed under different settings in the past decades. One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large. Moreover, the inferential accuracy of joint models could also be diminished for complex models due to approximation errors. However, complex models are frequently needed in practice, for example, when the longitudinal biomarkers have nonlinear trajectories over time or the number of longitudinal biomarkers of interest is large. In this article, we propose a novel Gaussian variational approximate inference approach for fitting joint models, which significantly improves computational efficiency while maintaining inferential accuracy. We conduct extensive simulation studies to evaluate the performance of our proposed method and compare it to existing methods. The performance of our proposed method is further demonstrated on a dataset of patients with primary biliary cirrhosis.

A threshold longitudinal Tobit quantile regression model for identification of treatment-sensitive subgroups based on interval-bounded longitudinal measurements and a continuous covariate.

Identification of a subgroup of patients who may be sensitive to a specific treatment is an important problem in precision medicine. This article considers the case where the treatment effect is assessed by longitudinal measurements, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. Recently, a linear mixed threshold regression model has been proposed but it assumes the longitudinal measurements are normally distributed. In many applications, longitudinal measurements, such as quality of life data obtained from answers to questions on a Likert scale, may be restricted in a fixed interval because of the floor and ceiling effects and, therefore, may be skewed. In this article, a threshold longitudinal Tobit quantile regression model is proposed and a computational approach based on alternating direction method of multipliers algorithm is developed for the estimation of parameters in the model. In addition, a random weighting method is employed to estimate the variances of the parameter estimators. The proposed procedures are evaluated through simulation studies and applications to the data from clinical trials.

Bayesian predictive model averaging approach to joint longitudinal-survival modeling: Application to an immuno-oncology clinical trial.

In immuno-oncology clinical trials, multiple immunological biomarkers are usually examined over time to comprehensively and appropriately evaluate the efficacy of treatments. Because predicting patients' future survival statuses on the basis of such recorded longitudinal information might be of great interest, joint modeling of longitudinal and time-to-event data has been intensively discussed as a toolkit to implement such a prediction. To achieve a desirable predictive performance, averaging over multiple candidate predictive models to account for the model uncertainty might be a more suitable statistical approach than selecting the single best model. Although Bayesian model averaging can be one of the approaches, several problems related to model weights with marginal likelihoods have been discussed. To address these problems, we here propose a Bayesian predictive model averaging (BPMA) method that uses Bayesian leave-one-out cross-validation predictive densities to account for the subject-specific and time-dependent nature of the prediction. We examine the operating characteristics of the proposed BPMA method in terms of the predictive accuracy (ie, the calibration and discrimination abilities) in extensive simulation studies. In addition, we discuss the strengths and limitations of the proposed method by applying it to an immuno-oncology clinical trial in patients with advanced ovarian cancer.

Individual differences in the development of youth externalizing problems predict a broad range of adult psychosocial outcomes.

This study examined how youth aggressive and delinquent externalizing problem behaviors across childhood and adolescence are connected to consequential psychosocial life outcomes in adulthood. Using data from a longitudinal, high-risk sample (N = 1069) that assessed children and their parents regularly from early childhood (ages 3-5) through adulthood, multilevel growth factors of externalizing behaviors were used to predict adult outcomes (age 24-31), providing a sense of how externalizing problems across development were related to these outcomes via maternal, paternal, teacher, and child report. Findings indicated strong support for the lasting connections between youth externalizing problems with later educational attainment and legal difficulties, spanning informants and enduring beyond other meaningful contributors (i.e., child sex, cognitive ability, parental income and education, parental mental health and relationship quality). Some support was also found, although less consistently, linking externalizing problems and later alcohol use as well as romantic relationship quality. Delinquent/rule-breaking behaviors were often stronger predictors of later outcomes than aggressive behaviors. Taken together, these results indicate the importance of the role youth externalizing behaviors have in adult psychosocial functioning one to two decades later.

A Bayesian joint model for multivariate longitudinal and time-to-event data with application to ALL maintenance studies.

The most common type of cancer diagnosed among children is the acute lymphocytic leukemia (ALL). A study was conducted by Tata Translational Cancer Research Center (TTCRC) Kolkata, in which 236 children (diagnosed as ALL patients) were treated for the first two years (approximately) with two standard drugs (6MP and MTx) and were then followed nearly for the next three years. The goal is to identify the longitudinal biomarkers that are associated with time-to-relapse, and also to assess the effectiveness of the drugs. We develop a Bayesian joint model in which a linear mixed model is used to jointly model three biomarkers (i.e. white blood cell count, neutrophil count, and platelet count) and a semi-parametric proportional hazards model is used to model the time-to-relapse. Our proposed joint model can assess the effects of different covariates on the progression of the biomarkers, and the effects of the biomarkers (and the covariates) on time-to-relapse. In addition, the proposed joint model can impute the missing longitudinal biomarkers efficiently. Our analysis shows that the white blood cell (WBC) count is not associated with time-to-relapse, but the neutrophil count and the platelet count are significantly associated with it. We also infer that a lower dose of 6MP and a higher dose of MTx jointly result in a lower relapse probability in the follow-up period. Interestingly, we find that relapse probability is the lowest for the patients classified into the "high-risk" group at presentation. The effectiveness of the proposed joint model is assessed through the extensive simulation studies.

Longitudinal analysis of shoulder arthroplasty utilization, clinical outcomes, and value: a comparative assessment of changes in improvement over 15 years with a single platform shoulder prosthesis.

BACKGROUND: The goal of this longitudinal analysis of anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA) utilization from 2007 to 2021 is to quantify changes in clinical outcomes, cost, and value, resulting from the introduction and adoption of new shoulder arthroplasty (SA) technologies. METHODS: We analyzed an international database of a single SA prosthesis (Equinoxe; Exactech, Inc.; Gainesville, FL, USA) for all clinical sites that have continuously enrolled cases from 2007 to 2021 to compare changes in primary aTSA and primary rTSA utilization and outcomes across 3, 5-year cohorts based upon the date of implantation. A value analysis was conducted across the 5-year implantation cohorts, with value measured by the ratio of each postoperative outcome measure at 24-36 months and 36-60 months after surgery, and the average implant selling price each year for the U.S. sites in constant 2007 U.S. dollars. These measures of value were compared between cohorts to quantify the impact of new technology introduced over the study period. RESULTS: A dramatic increase in rTSA utilization was observed across the 6 sites over the 15-year study period, along with a rapid adoption of new aTSA and rTSA technologies. The average patient receiving primary aTSA and primary rTSA changed over the 15-year study period, with significant shifts in diagnosis, comorbidities, and preoperative functional status. A comparison of postoperative results demonstrated that both aTSA and rTSA clinical and radiographic outcomes showed improvement relative to 2007-2011. Over this 15-year study period, the average aTSA implant selling price has been relatively stable while the average rTSA implant selling price has significantly declined. As a result, the value associated with the Equinoxe rTSA significantly increased for nearly every outcome measure at 24-36 months and 36-60 months after surgery, while value associated with the Equinoxe aTSA stayed relatively constant from 2007 to 2021. CONCLUSION: Our 6042-patient longitudinal analysis quantified numerous changes in utilization, outcomes, and value across 6 clinical sites over the 15-year study period. Rapid adoption of new aTSA and rTSA technologies was observed and clinical and radiographic outcomes improved relative to 2007-2011. These clinical improvements, in combination with steady aTSA and declining rTSA implant prices, have driven rTSA value to continuously increase while aTSA value has been maintained at a high-level over the 15-year study period with this particular SA system, even when considering the cost and adoption of new technologies.

Bayesian Design of Clinical Trials Using Joint Cure Rate Models for Longitudinal and Time-to-Event Data.

For clinical trial design and analysis, there has been extensive work related to using joint models for longitudinal and time-to-event data without a cure fraction (i.e., when all patients are at risk for the event of interest), but comparatively little treatment has been given to design and analysis of clinical trials using joint models that incorporate a cure fraction. In this paper, we develop a Bayesian clinical trial design methodology focused on evaluating the treatment's effect on a time-to-event endpoint using a promotion time cure rate model, where the longitudinal process is incorporated into the hazard model for the promotion times. A piecewise linear hazard model for the period after assessment of the longitudinal measure ends is proposed as an alternative to extrapolating the longitudinal trajectory. This may be advantageous in scenarios where the period of time from the end of longitudinal measurements until the end of observation is substantial. Inference for the time-to-event endpoint is based on a novel estimand which combines the treatment's effect on the probability of cure and its effect on the promotion time distribution, mediated by the longitudinal outcome. We propose an approach for sample size determination such that the design has a high power and a well-controlled type I error rate with both operating characteristics defined from a Bayesian perspective. We demonstrate the methodology by designing a breast cancer clinical trial with a primary time-to-event endpoint where longitudinal outcomes are measured periodically during follow up.

The Longitudinal Effects of Second Step Child Protection Unit on Children: Gender as a Moderator.

We investigated the longitudinal effects of the Second Step Child Protection Unit (CPU; Committee for Children) on student outcomes through a randomized controlled trial. Eight schools with a total sample including 2,031 students were assigned randomly to the CPU intervention or the wait-list control condition. We employed a multi-process latent growth model using a structural equation modeling framework which simultaneously analyzed student outcome growth via the effects of the intervention. The moderating effect of gender was also included. Over four data collection waves (pretest, posttest, follow-up [6 months] and follow-up [12-months]), the intervention group students were better able to recognize appropriate requests in CSA scenarios than control group students over time. Teacher-student relations improved for the students in the intervention schools but worsened for the students in the control schools over time. Despite concerns that CSA prevention programs may result in the unintended consequence of making children more fearful, children in both conditions did not show increased fears over time. The longitudinal effect on CSA recognition was more pronounced among girls, who suffer from higher CSA prevalence, compared with boys. Implications for prevention, policy, and directions for future research are discussed.

Baseline Predictors of Longitudinal Changes in Symptom Severity and Quality of Life in Patients With Suspected Gastroparesis.

BACKGROUND & AIMS: Whether gastric emptying tests predict longitudinal outcomes in patients with symptoms of gastroparesis is unclear. We aimed to determine whether baseline gastric emptying tests and gut motility parameters could impact longitudinal symptom(s) and quality of life (QOL) in a prospective, observational cohort study of patients with symptoms of gastroparesis. METHODS: One hundred fifty patients with gastroparesis symptoms underwent simultaneous scintigraphy (GES) and wireless motility capsule (WMC) measurement of gastric emptying and other motility parameters. Patient Assessment of Upper Gastrointestinal Symptoms and Quality of Life were administered at baseline, and 3 and 6 months after testing. Multivariable generalized linear marginal models were fit to determine which baseline parameters predict longitudinal changes in symptoms and QOL. RESULTS: Overall upper GI symptoms and QOL scores were moderate in severity at baseline and significantly improved over 6 months. Clinical variables, including female gender, harder stools by Bristol stool form score, and presence of functional dyspepsia (FD) by Rome III criteria, were predictive of more severe upper GI symptoms. Even after controlling for these clinical factors, delayed gastric emptying by GES or WMC was associated with worse symptom severity and QOL scores. Low gastric and elevated small bowel contractile parameters by WMC were also independently associated with more severe upper GI symptoms and worse QOL scores. CONCLUSIONS: Baseline features, including demographic and clinical variables, delayed gastric emptying and abnormal gastrointestinal contractility, were independent predictors of more severe longitudinal symptoms and worse quality of life outcomes. These factors may help to risk stratify patients and guide treatment decisions. ClinicalTrials.gov no: NCT02022826.

Perspectives and a Systematic Scoping Review on Longitudinal Profiles of Posterior Cortical Atrophy Syndrome.

PURPOSE OF REVIEW: To provide perspectives on the importance of understanding longitudinal profiles of posterior cortical atrophy (PCA) and report results of a scoping review to identify data and knowledge gaps related to PCA survival and longitudinal clinical and biomarker outcomes. RECENT FINDINGS: Thirteen longitudinal studies were identified; all but two had fewer than 30 participants with PCA. Relatively few longitudinal data exist, particularly for survival. In PCA, posterior cortical dysfunction and atrophy progress at faster rates compared to non-posterior regions, potentially up to a decade after symptom onset. Unlike typical AD, PCA phenotype-defined cognitive dysfunction and atrophy remain relatively more severe compared to other regions throughout the PCA course. Select cognitive tests hold promise as PCA outcome measures and for staging. Further longitudinal investigations are critically needed to enable PCA inclusion in treatment trials and to provide appropriate care to patients and enhance our understanding of the pathophysiology of dementing diseases.

No change in outcome ten years following locking plate repair of displaced proximal humerus fractures.

PURPOSE: To assess longer-term (> 5 years) function and outcome in patients treated with anatomic locking plates for proximal humerus fractures. METHODS: This retrospective cohort study was conducted at an urban, academic level 1 trauma center and an orthopedic specialty hospital. Patients treated operatively for proximal humerus fractures with an anatomic locking plate by three orthopedic trauma surgeons and two shoulder surgeons from 2003 to 2015 were reviewed. Patient demographics and injury characteristics, disabilities of the arm, shoulder, and hand (DASH) scores, complications, secondary surgeries, and shoulder range of motion were compared at 1 year and at latest follow-up. RESULTS: Seventy-five of 173 fractures were eligible for analysis. At a minimum 5 years and a mean of 10.0 ± 3.2 years following surgery, DASH scores did not differ from one-year compared to long-term follow-up (16.3 ± 17.4 vs. 15.1 ± 18.2, p = 0.555). Shoulder motion including: active forward flexion (145.5 vs. 151.5 degrees, p = 0.186), internal rotation (T10 vs. T9, p = 0.204), and external rotation measurements (48.4 vs. 57.9, p = 0.074) also did not differ from one year compared to long-term follow-up. By one year, all fractures had healed. After 1-year post-operatively, four patients underwent reoperation, but none for AVN or screw penetration. CONCLUSIONS: Patient-reported functional outcome scores and shoulder range of motion are stable after one year following proximal humerus fracture fixation, and outcomes do not deteriorate thereafter. After one-year, long-term follow-up of fixed proximal humerus fractures may be unnecessary for those without symptoms.

Analysis of multivariate longitudinal substance use outcomes using multivariate mixed cumulative logit model.

BACKGROUND: Longitudinal assessments of usage are often conducted for multiple substances (e.g., cigarettes, alcohol and marijuana) and research interests are often focused on the inter-substance association. We propose a multivariate longitudinal modeling approach for jointly analyzing the ordinal multivariate substance use data. METHODS: We describe how the binary random slope logistic regression model can be extended to the multi-category ordinal outcomes. We also describe how the proportional odds assumption can be relaxed by allowing differential covariate effects on different cumulative logits for multiple outcomes. Data are analyzed from a P01 study that evaluates the usage levels of cigarettes, alcohol and marijuana repeatedly across 8 measurement waves during 7 consecutive years. RESULTS: 1263 subjects participated in the study with informed consent, among whom 56.6% are females. Males and females show significant differences in terms of the time trend for substance use. Specifically, males showed steeper trends on cigarette and marijuana use over time compared to females, while less so for alcohol. For all three substances, age effects appear to be different for different cumulative logits, indicating the violation of proportional odds assumption. CONCLUSIONS: The multivariate mixed cumulative logit model offers the most flexibility and allows one to examine the inter-substance association when proportional odds assumption is violated.