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A difficult decision for patients in need of kidney–pancreas transplant is whether to seek a living kidney donor or wait to receive both organs from one deceased donor. The framework of dynamic treatment regimes (DTRs) can inform this choice, but a patient‐relevant strategy such as "wait for deceased‐donor transplant" is ill‐defined because there are multiple versions of treatment (i.e., wait times, organ qualities). Existing DTR methods average over the distribution of treatment versions in the data, estimating survival under a "representative intervention." This is undesirable if transporting inferences to a target population such as patients today, who experience shorter wait times thanks to evolutions in allocation policy. We, therefore, propose the concept of a generalized representative intervention (GRI): a random DTR that assigns treatment version by drawing from the distribution among strategy compliers in the target population (e.g., patients today). We describe an inverse‐probability‐weighted product‐limit estimator of survival under a GRI that performs well in simulations and can be implemented in standard statistical software. For continuous treatments (e.g., organ quality), weights are reformulated to depend on probabilities only, not densities. We apply our method to a national database of kidney–pancreas transplant candidates from 2001–2020 to illustrate that variability in transplant rate across years and centers results in qualitative differences in the optimal strategy for patient survival. [ABSTRACT FROM AUTHOR]

A sequential multiple assignment randomized trial, which incorporates multiple stages of randomization, is a popular approach for collecting data to inform personalized and adaptive treatments. There is an extensive literature on statistical methods to analyze data collected in sequential multiple assignment randomized trials and estimate the optimal dynamic treatment regime. Q-learning with linear regression is widely used for this purpose due to its ease of implementation. However, model misspecification is a common problem with this approach, and little attention has been given to the impact of model misspecification when treatment effects are heterogeneous across subjects. This article describes the integrative impact of two possible types of model misspecification related to treatment effect heterogeneity: omitted early-stage treatment effects in late-stage main effect model, and violated linearity assumption between pseudo-outcomes and predictors despite non-linearity arising from the optimization operation. The proposed method, aiming to deal with both types of misspecification concomitantly, builds interactive models into modified parametric Q-learning with Murphy's regret function. Simulations show that the proposed method is robust to both sources of model misspecification. The proposed method is applied to a two-stage sequential multiple assignment randomized trial with embedded tailoring aimed at reducing binge drinking in first-year college students. [ABSTRACT FROM AUTHOR]

Support vector regression (SVR) is particularly beneficial when the outcome and predictors are nonlinearly related. However, when many covariates are available, the method's flexibility can lead to overfitting and an overall loss in predictive accuracy. To overcome this drawback, we develop a feature selection method for SVR based on a genetic algorithm that iteratively searches across potential subsets of covariates to find those that yield the best performance according to a user-defined fitness function. We evaluate the performance of our feature selection method for SVR, comparing it to alternate methods including LASSO and random forest, in a simulation study. We find that our method yields higher predictive accuracy than SVR without feature selection. Our method outperforms LASSO when the relationship between covariates and outcome is nonlinear. Random forest performs equivalently to our method in some scenarios, but more poorly when covariates are correlated. We apply our method to predict donor kidney function 1 year after transplant using data from the United Network for Organ Sharing national registry. [ABSTRACT FROM AUTHOR]

M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking. [ABSTRACT FROM AUTHOR]

There is a growing demand for methods to determine the effects that chemical mixtures have on human health. One statistical challenge is identifying true "bad actors" from a mixture of highly correlated predictors, a setting in which standard approaches such as linear regression become highly variable. Weighted Quantile Sum regression has been proposed to address this problem, through a two-step process where mixture component weights are estimated using bootstrap aggregation in a training dataset and inference on the overall mixture effect occurs in a held-out test set. Weighted Quantile Sum regression is popular in applied papers, but the reliance on data splitting is suboptimal, and analysts who use the same data for both steps risk inflating the Type I error rate. We therefore propose a modification of Weighted Quantile Sum regression that uses a permutation test for inference, which allows for weight estimation using the entire dataset and preserves Type I error. To minimize computational burden, we propose replacing the bootstrap with L1 or L2 penalization and describe how to choose the appropriate penalty given expert knowledge about a mixture of interest. We apply our method to a national pregnancy cohort study of prenatal phthalate exposure and child health outcomes. [ABSTRACT FROM AUTHOR]

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group‐sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group‐sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting. [ABSTRACT FROM AUTHOR]

Cluster analysis is an unsupervised learning strategy that is exceptionally useful for identifying homogeneous subgroups of observations in data sets of unknown structure. However, it is challenging to determine if the identified clusters represent truly distinct subgroups rather than noise. Existing approaches for addressing this problem tend to define clusters based on distributional assumptions, ignore the inherent correlation structure in the data, or are not suited for high‐dimension low‐sample size (HDLSS) settings. In this paper, we propose a novel method to evaluate the significance of identified clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution that preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation, and thus, does not require that the data follow a particular distribution. By utilizing sparse covariance estimation, the method is adapted for the HDLSS setting. The approach can be used to test the null hypothesis that the data cannot be partitioned into clusters and to determine the optimal number of clusters. Simulation examples, theoretical evaluations, and applications to temporomandibular disorder research and cancer microarray data illustrate the utility of the proposed method. [ABSTRACT FROM AUTHOR]

The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression‐based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior. [ABSTRACT FROM AUTHOR]

A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered. [ABSTRACT FROM AUTHOR]

Aim Materials and Methods Results Conclusions To compare the effectiveness of sodium‐glucose co‐transporter‐2 inhibitors (SGLT2is) with dipeptidyl peptidase‐4 inhibitors (DPP4is) on major liver outcomes (MLO) in patients with type 2 diabetes (T2D) and metabolic dysfunction‐associated steatotic liver disease (MASLD).We included adult patients with T2D and MASLD, using metformin without specific liver conditions or surgeries, from the Merative MarketScan database. Patients initiating SGLT2is or DPP4is from 1 January 2014 to 31 December 2022 were identified. The primary outcome was time to MLO diagnosis. Overlap weighting balanced covariates, integrated with a Cox proportional hazards model for survival analysis.Among 44 651 patients, 22 100 initiated SGLT2is, and 22 551 began DPP4is. After weighting, the incidence rate of MLO in the SGLT2i group was 3.8 per 1000 person‐years, and it was 3.9 per 1000 person‐years in the DPP4i group, resulting in an adjusted hazard ratio (aHR) of 0.82 (95% CI, 0.60‐1.10). SGLT2i initiation was not associated with cirrhosis (aHR: 0.77; 95% CI, 0.55‐1.06) or hepatocellular carcinoma (aHR: 0.99; 95% CI, 0.47‐1.83) separately. Subgroup and sensitivity analyses did not yield significant results.In patients with T2D and MASLD, SGLT2is did not show a lower risk of MLO compared with DPP4is. Clinicians should consider the overall patient conditions and the additional benefits of SGLT2is to support the decision to switch from DPP4is. [ABSTRACT FROM AUTHOR]

Background: Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation. Methods: The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a "Living Guideline" process model for the VTE prevention CDS system. Discussion: The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics. Trial registration: Clinicaltrials.gov – NCT05628207. Prospectively registered 11/28/2022, https://classic.clinicaltrials.gov/ct2/show/NCT05628207. [ABSTRACT FROM AUTHOR]

To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan–Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from.62 to.79 and from.66 to.80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features. [ABSTRACT FROM AUTHOR]

Severe obesity is a complex, chronic disease affecting nearly 9% of adolescents in the U.S. Although the current mainstay of treatment is lifestyle therapy, pediatric clinical practice guidelines recommend the addition of adjunct anti-obesity medication (AOM), such as phentermine and topiramate. However, guidance regarding when adjunct AOM should be started and how AOM should be used is unclear. Furthermore, an inherent limitation of current treatment guidelines is their "one-size-fits-all" approach, which does not account for the heterogeneous nature of obesity and high degree of patient variability in response to all interventions. This paper describes the study design and methods of a sequential multiple assignment randomized trial (SMART), "SMART Use of Medications for the Treatment of Adolescent Severe Obesity." The trial will examine 1) when to start AOM (specifically phentermine) in adolescents who are not responding to lifestyle therapy and 2) how to modify AOM when there is a sub-optimal response to the initial pharmacological intervention (specifically, for phentermine non-responders, is it better to add topiramate to phentermine or switch to topiramate monotherapy). Critically, participant characteristics that may differentially affect response to treatment will be assessed and evaluated as potential moderators of intervention efficacy. Data from this study will be used to inform the development of an adaptive intervention for the treatment of adolescent severe obesity that includes empirically-derived decision rules regarding when and how to use AOM. Future research will test this adaptive intervention against standard "one-size-fits-all" treatments. [ABSTRACT FROM AUTHOR]

Background: Aerobic exercise has shown inconsistent cognitive effects in older adults with Alzheimer's disease (AD) dementia.Objective: To examine the immediate and longitudinal effects of 6-month cycling on cognition in older adults with AD dementia.Methods: This randomized controlled trial randomized 96 participants (64 to cycling and 32 to stretching for six months) and followed them for another six months. The intervention was supervised, moderate-intensity cycling for 20-50 minutes, 3 times a week for six months. The control was light-intensity stretching. Cognition was assessed at baseline, 3, 6, 9, and 12 months using the AD Assessment Scale-Cognition (ADAS-Cog). Discrete cognitive domains were measured using the AD Uniform Data Set battery.Results: The participants were 77.4±6.8 years old with 15.6±2.9 years of education, and 55% were male. The 6-month change in ADAS-Cog was 1.0±4.6 (cycling) and 0.1±4.1 (stretching), which were both significantly less than the natural 3.2±6.3-point increase observed naturally with disease progression. The 12-month change was 2.4±5.2 (cycling) and 2.2±5.7 (control). ADAS-Cog did not differ between groups at 6 (p = 0.386) and 12 months (p = 0.856). There were no differences in the 12-month rate of change in ADAS-Cog (0.192 versus 0.197, p = 0.967), memory (-0.012 versus -0.019, p = 0.373), executive function (-0.020 versus -0.012, p = 0.383), attention (-0.035 versus -0.033, p = 0.908), or language (-0.028 versus -0.026, p = 0.756).Conclusion: Exercise may reduce decline in global cognition in older adults with mild-to-moderate AD dementia. Aerobic exercise did not show superior cognitive effects to stretching in our pilot trial, possibly due to the lack of power. [ABSTRACT FROM AUTHOR]

Few prognostic models have been created in children that receive liver retransplantation (rLT). We examined the SRTR database of 731 children that underwent second liver transplant between 2002 and 2018. Proportional hazards models using backward variable selection were used to identify recipient, donor, and surgical characteristics associated with survival. A simple prognostic scoring system or nomogram (ie, each risk factor was weighted on a five-point scale) was constructed based on the fitted model. Recipient age (P < .001), MELD/PELD (P < .001), recipient ventilated (P = .003), donor cause of death (P = .024), graft type (P = .045), first graft loss due to biliary tract complications (P = .048), and survival time of the first graft (P = .006) were significant predictors of retransplant survival. The bias-corrected Harrell's C-index for the multivariable model was 0.63. Survival was significantly different (P < .001) for those at low risk (0-4 points), medium risk (5-7 points), and high risk (8+ points). Survival was equivalent between low risk pediatric second transplant recipients and pediatric primary liver transplant recipients (P = .67) but significantly worse for medium- (P < .001) and high-risk (P < .001) recipients. With simple clinical characteristics, this scoring tool can modestly discriminate between those children at high risk and those children at low risk of poor outcomes after second liver transplant. [ABSTRACT FROM AUTHOR]

Unbiased estimation of causal effects with observational data requires adjustment for confounding variables that are related to both the outcome and treatment assignment. Standard variable selection techniques aim to maximize predictive ability of the outcome model, but they ignore covariate associations with treatment and may not adjust for important confounders weakly associated to outcome. We propose a novel method for estimating causal effects that simultaneously considers models for both outcome and treatment, which we call the bilevel spike and slab causal estimator (BSSCE). By using a Bayesian formulation, BSSCE estimates the posterior distribution of all model parameters and provides straightforward and reliable inference. Spike and slab priors are used on each covariate coefficient which aim to minimize the mean squared error of the treatment effect estimator. Theoretical properties of the treatment effect estimator are derived justifying the prior used in BSSCE. Simulations show that BSSCE can substantially reduce mean squared error over numerous methods and performs especially well with large numbers of covariates, including situations where the number of covariates is greater than the sample size. We illustrate BSSCE by estimating the causal effect of vasoactive therapy vs. fluid resuscitation on hypotensive episode length for patients in the Multiparameter Intelligent Monitoring in Intensive Care III critical care database. [ABSTRACT FROM AUTHOR]

Summary: Although rapid discontinuation of prednisone (RDP) after kidney transplantation has been successful in low‐risk recipients, there is concern about RDP use in recipients at increased risk for rejection or recurrent disease. Using SRTR, we compared outcomes for RDP versus maintenance prednisone‐treated recipients for all adult 1st and 2nd transplants (n = 169 479) and the following 1st transplant subgroups: African American (AA); highly sensitized; those with a potentially recurrent disease; and pediatric recipients. For all adult 1st LD and DD transplants, RDP was associated with better patient and graft survival. For all LD subgroups, RDP and maintenance prednisone were associated with similar patient, graft, and death‐censored (DC) graft survival. For 1st transplant DD subgroups, RDP was associated with better patient survival in AA, those with potentially recurrent disease, and pediatric recipients; graft survival with RDP was better in AAs. For adult 2nd DD transplants, RDP was associated with worse DC‐graft survival. Importantly, for all differences, the effect size was small. With the exception of 2nd DD transplants, RDP protocols can be used without decreasing patient or graft survival for subgroups of 1st DD and LD kidney transplant recipients and for 2nd LD transplant recipients, at increased risk of rejection or recurrent disease. [ABSTRACT FROM AUTHOR]

Recent evidence from randomized clinical trials (RCTs) of very low nicotine content (VLNC) cigarettes indicates that smokers randomized to VLNC cigarettes had significantly lower cigarette use, dependence, and biomarkers of exposure than smokers randomized to normal nicotine content control cigarettes. In these trials, a substantial number of participants did not adhere to their randomized treatment assignment, i.e., they used commercial cigarettes not provided by the trial in place of or in addition to the VLNC cigarettes provided by the trial. As with most RCTs, the analysis of these trials followed the intention-to-treat principle, where participants are analyzed according to their randomized treatment assignment regardless of adherence. Alternately, the analysis of an RCT could focus on the estimation and testing of the causal effect of the intervention, which is the treatment effect if all subjects were to adhere to their randomized treatment assignment. In this commentary, we compare these two approaches, highlighting the important role of causal estimation and inference for evaluating the regulatory effect of a nicotine standard for cigarettes. Additionally, we review the results of the secondary analyses of randomized trials of VLNC cigarettes using causal inference methodology to account for non-adherence to the assigned treatment and discuss the implications for a nicotine standard for cigarettes. [ABSTRACT FROM AUTHOR]

Background Nonadherence to posttransplant immunosuppressive medication is associated with increased rates of rejection and graft loss, yet it is unknown to what degree ideal adherence is associated with the sequelae of overimmunosuppression. Specifically, we questioned whether excellent adherence increased the posttransplant cancer risk. Methods Between August 1998 and August 2006, 195 consenting kidney transplant recipients had electronic monitoring of theirimmunosuppressive medication adherence. Results Based on their average quantitative adherence to a single immunosuppressant drug over the first 6 months posttransplant, recipients were grouped into adherence tertiles (highest, >97.9% adherence; middle, 91–97.8%; lowest, <91%). The cumulative incidence of cancer was calculated for patients in each tertile, treating death as a competing risk. The association between adherence and cancer rate was calculated after adjusting for recipient risk factors, using a competing risk proportional hazards model. The median duration of follow-up was 10.1 years. The 10-year estimated cumulative cancer incidence was 59.4% in the most adherent, 36.1% in the middle group and 38.1% in the least adherent group (P = 0.006). Excluding nonmelanocytic skin cancers, cancer incidence remained significantly higher in the highest adherence group (P = 0.002). Conclusions These data provide additional support for the need to individualize immunosuppression to minimize both rejection and immunosuppressive drug–related complications including cancer. [ABSTRACT FROM AUTHOR]

Estimating causal effects from randomized controlled trials is often complicated due to participant noncompliance to randomized treatment. Although there are a variety of methods to estimate causal effects in the presence of noncompliance, they generally make the assumption that noncompliance is measured without error. This is frequently an untenable assumption, particularly when noncompliance is based on participant self-report. To overcome this issue, we treat compliance as an unobserved variable and show how to estimate the probability of compliance given a biomarker of treatment and the other observed data. We present inverse probability weighted estimators, regression-based estimators, and a doubly-robust augmented estimator that rely on the estimated probability of compliance rather than an indicator of compliance. We investigate the finite-sample properties of the estimators and their efficiency and robustness under correctly specified or misspecified models, and we apply the estimators to a recently completed trial of very low nicotine content cigarettes. [ABSTRACT FROM AUTHOR]

Summary: Patients awaiting cadaveric organ transplantation face a difficult decision if offered a low‐quality organ: accept the organ or remain on the waiting list and hope a better organ is offered in the future. A dynamic treatment regime (DTR) for transplantation is a rule that determines whether a patient should decline an offered organ. Existing methods can estimate the effect of DTRs on survival outcomes, but these were developed for applications where treatment is abundantly available. For transplantation, organ availability is limited, and existing methods can only estimate the effect of a DTR assuming a single patient follows the DTR. We show for transplantation that the effect of a DTR depends on whether other patients follow the DTR. To estimate the anticipated survival if the entire population awaiting transplantation were to adopt a DTR, we develop a novel inverse probability weighted estimator (IPCW) which re‐weights patients based on the probability of following their transplant history in the counterfactual world in which all patients follow the DTR of interest. We estimate this counterfactual probability using hot deck imputation to fill in data that is not observed for patients who are artificially censored by IPCW once they no longer follow the DTR of interest. We show via simulation that our proposed method has good finite‐sample properties, and we apply our method to a lung transplantation observational registry. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effect of transforming a right-censored outcome into binary, continuous, and censored-aware representations on radiomics feature selection and subsequent prediction of overall survival (OS) and relapse-free survival (RFS) of patients with oropharyngeal cancer. Methods and Materials: Different feature selection techniques were applied using a binary outcome indicating event occurrence before median follow-up time, a continuous outcome using the Martingale residuals from a proportional hazards model, and the raw right-censored time-to-event outcome. Radiomic signatures combined with clinical variables were used for risk prediction. Three metrics for accuracy and calibration were used to evaluate eight feature selectors and six predictive models. Results: Feature selection across 529 patients on more than 3,800 radiomic features resulted in increases ranging from 0.01 to 0.11 in C-index and area under the curve (AUC) scores compared with clinical features alone. The ensemble model yielded the best scores for AUC and C-index (often > 0.7) and calibration (Nam-D'Agostino test statistic often < 15.5 with 8 df). The random forest feature selectors achieved the best performance considering all metrics. Random regression forest performed the best in OS prediction with the ensemble model (AUC, 0.75; C-index, 0.76; calibration, 8.7). Random survival forest performed the best in RFS prediction with the ensemble model (AUC, 0.71; C-index, 0.68; calibration, 19.1). Conclusion: Including a radiomic signature results in better prediction than using only clinical data. Signatures generated randomly or without considering the outcome result in poor calibration scores. The random forest feature selectors for each of the three transformations typically selected the greatest number of features and produced the best predictions at acceptable calibration levels. In particular, random regression forest and random survival forest performed best for OS and RFS, respectively. [ABSTRACT FROM AUTHOR]

Summary: Background: Obesity is a heterogeneous disease with variable treatment response. Identification of the unique constellation of contributors to obesity may allow for targeted interventions and improved outcomes. Objective: Identify empirically derived phenotypes of pediatric patients with obesity based on appetitive and psychological correlates of obesity. Methods: This cross‐sectional study included patients aged 5–12 years who were treated in a weight management clinic and completed standard intake questionnaires including Child Eating Behavior Questionnaire (CEBQ), Vanderbilt ADHD Scale and Pediatric Symptom Checklist. Phenotypes were elicited using latent profile analysis of 12 indicators: eight CEBQ subscales, inattention, hyperactivity/impulsivity, internalizing and externalizing symptoms. Results: Parents/guardians of 384 patients (mean age 9.8 years, mean BMI 30.3 kg/m2) completed the intake questionnaires. A 4‐phenotype model best fits the data. Hedonic Impulsive phenotype (42.5%) exhibited high food enjoyment and hyperactivity/impulsivity. Inattentive Impulsive phenotype (27.4%) exhibited overall low food approach and high food avoid behaviours, and highest inattention. Hedonic Emotional phenotype (20.8%) scored the highest on food enjoyment, internalizing and externalizing symptoms. Picky Eating phenotype (9.3%) scored the lowest on food approach, inattention, hyperactivity/impulsivity, internalizing and externalizing symptoms. Conclusion: Appetitive traits and psychological symptoms appear to cluster in distinct patterns, giving rise to four unique phenotypic profiles, which, if replicated, may help inform the development of tailored treatment plans. [ABSTRACT FROM AUTHOR]

Background: The optimal age of kidney transplantation for infants and toddlers with kidney failure is unclear. We aimed to evaluate the patient survival associated with kidney transplantation before 2 years of age versus remaining on the waitlist until ≥2 years. Method: We used the Scientific Registry of Transplant Recipients to identify all children added to the deceased‐donor waitlist before 2 years of age between 1/1/2000 and 4/30/2020. For each case aged <2 years at transplant, we created a control group comprising all candidates on the waitlist on the case's transplant date. Patient survival was evaluated using sequential Cox regression. Dialysis‐free time was defined as graft survival time for cases and the sum of dialysis‐free time on the waitlist and graft survival time for controls. Results: We observed similar patient survival for posttransplant periods 0–3 and 4–12 months but higher survival for period >12 months for <2‐year decreased‐donor recipients (aHR: 0.32; 95% CI: 0.13–0.78; p =.01) compared with controls. Similarly, patient survival was higher for <2‐year living‐donor recipients for posttransplant period >12 months (aHR: 0.21; 95% CI: 0.06–0.73; p =.01). The 5‐year dialysis‐free survival was higher for <2‐year deceased‐ (difference: 0.59 years; 95% CI: 0.23–0.93) and living‐donor (difference: 1.84 years; 95% CI: 1.31–2.25) recipients. Conclusion: Kidney transplantation in children <2 years of age is associated with improved patient survival and reduced dialysis exposure compared with remaining on the waitlist until ≥2 years. [ABSTRACT FROM AUTHOR]

Context: School connectedness and engagement are key proximal social determinants of adolescent health that are associated with improved behavioral health including lower levels of substance use, improved emotional well-being, and higher academic achievement. These determinants are less accessible to Black, Indigenous, and People of Color (BIPOC) students due to structural barriers at the school level, with long-term implications for health. Objective: Assess the effectiveness of Project TRUST, a multicomponent school-based intervention, at addressing student-school connectedness and engagement. Study Design TRUST was a controlled trial that utilized participatory research approaches. The multicomponent intervention focused on school environment included: 1) Training to build teacherstudent relationships and promote positive youth development, 2) Youth-defined school policy, practice, or procedure change to promote connectedness, and 3) Parent-defined school policy, practice, or procedure change to promote parent and community connection. Primary outcomes were studentschool connectedness and engagement assessed through longitudinal online student surveys between 2017 and 2019. Five diverse middle and high schools were assigned to Wave 1 intervention implementation at study onset and five to delayed-control Wave 2 condition two years later. We used a novel weighting algorithm to mitigate the lack of randomization and potential imbalances across conditions. Setting: A large U.S. Midwestern school district. Participants: 809 6th and 9th grade students. Results: Participants were predominately female (53%), BIPOC (74%) and included 16% English language learners. School connectedness and engagement declined for all students from baseline to follow up (school connectedness, 3.15 to 3.02; school engagement, 3.22 to 3.07). No overall significant intervention effect was noted in school connectedness (difference in change 0.03 (95% CI -0.11, 0.17)) or school engagement (0.06 (-0.02, 0.14)). However Wave 1 BIPOC students maintained their level of school engagement compared to Wave 2 where it dropped (difference in change 0.109 (0.005, 0.214)). Conclusions: A multicomponent participatory intervention maintained school engagement for BIPOC students, emphasizing the importance of school environment for BIPOC student health in particular. Social determinants of health focused interventions are important for addressing health inequities and promoting population health. [ABSTRACT FROM AUTHOR]

Summary: The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this article, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single‐lung transplant on forced expiratory volume in one year after transplant using an observational registry. [ABSTRACT FROM AUTHOR]

Noncompliance or non-adherence to randomized treatment is a common challenge when interpreting data from randomized clinical trials. The effect of an intervention if all participants were forced to comply with the assigned treatment (i.e., the causal effect) is often of primary scientific interest. For example, in trials of very low nicotine content (VLNC) cigarettes, policymakers are interested in their effect on smoking behavior if their use were to be compelled by regulation. A variety of statistical methods to estimate the causal effect of an intervention have been proposed, but these methods, including inverse probability of compliance weighted (IPCW) estimators, assume that participants' compliance statuses are reported without error. This is an untenable assumption when compliance is based on self-report. Biomarkers (e.g., nicotine levels in the urine) may provide more reliable indicators of compliance but cannot perfectly discriminate between compliers and non-compliers. However, by modeling the distribution of the biomarker as a mixture distribution and writing the probability of compliance as a function of the mixture components, we show how the probability of compliance can be directly estimated from the data even when compliance status is unknown. To estimate the causal effect, we develop a new approach which re-weights participants by the product of their probability of compliance given the observed data and the inverse probability of compliance given confounders. We show that our proposed estimator is consistent and asymptotically normal and show that in some situations the proposed approach is more efficient than standard IPCW estimators. We demonstrate via simulation that the proposed estimator achieves smaller bias and greater efficiency than ad hoc approaches to estimating the causal effect when compliance is measured with error. We apply our method to data from a recently completed randomized trial of VLNC cigarettes. [ABSTRACT FROM AUTHOR]

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution and then marginalizing over the covariate distribution. Because the estimator for the restricted mean difference is defined by the estimator for the covariate-adjusted survival distribution, it is natural to expect that a better estimator of the covariate-adjusted survival distribution is associated with a better estimator of the restricted mean difference. We therefore propose estimating restricted mean differences with stacked survival models. Stacked survival models estimate a weighted average of several survival models by minimizing predicted error. By including a range of parametric, semi-parametric, and non-parametric models, stacked survival models can robustly estimate a covariate-adjusted survival distribution and, therefore, the restricted mean treatment effect in a wide range of scenarios. We demonstrate through a simulation study that better performance of the covariate-adjusted survival distribution often leads to better mean squared error of the restricted mean difference although there are notable exceptions. In addition, we demonstrate that the proposed estimator can perform nearly as well as Cox regression when the proportional hazards assumption is satisfied and significantly better when proportional hazards is violated. Finally, the proposed estimator is illustrated with data from the United Network for Organ Sharing to evaluate post-lung transplant survival between large-volume and small-volume centers. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable methods to identify subgroups that respond to the treatment differently from the population average are necessary to achieving this goal. The Virtual Twins (VT) method is a highly cited and implemented method for subgroup identification because of its intuitive framework. However, since its initial publication, many researchers still rely heavily on the authors' initial modeling suggestions without examining newer and more powerful alternatives. This leaves much of the potential of the method untapped. We comprehensively evaluate the performance of VT with different combinations of methods in each of its component steps, under a collection of linear and nonlinear problem settings. Our simulations show that the method choice for Step 1 of VT, in which dense models with high predictive performance are fit for the potential outcomes, is highly influential in the overall accuracy of the method, and Superlearner is a promising choice. We illustrate our findings by using VT to identify subgroups with heterogeneous treatment effects in a randomized, double-blind trial of very low nicotine content cigarettes. [ABSTRACT FROM AUTHOR]

Primary care is a frequent source of pain treatment and opioid prescribing. The objective of the Prescribing Interventions for Chronic Pain using the Electronic health record (PRINCE) study is to assess the effects of two behavioral economics-informed interventions embedded within the electronic health record (EHR) on guideline-concordant pain treatment and opioid prescribing decisions in primary care settings. Setting : The setting for this study is 43 primary care clinics in Minnesota. Design: The PRINCE study uses a cluster-randomized 2 × 2 factorial design to test the effects of two interventions. An adaptive design allows for the possibility of secondary randomization to test if interventions can be titrated while maintaining efficacy. Interventions: One intervention alters the "choice architecture" within the EHR to nudge clinicians toward non-opioid treatments for opioid-naïve patients and toward tapering for patients currently receiving a "high risk" opioid. The other intervention integrates the prescription drug monitoring program (PDMP) directly within the EHR. Outcome : The primary outcome for opioid-naïve patients is whether an opioid is prescribed in a primary care visit without a non-opioid alternative pain treatment. The primary outcome for current opioid-using patients is whether opioid prescriptions were tapered with a documented rationale. The PRINCE study will provide real-world evidence on two approaches to improving pain treatment in primary care using the EHR. The adaptive study design strikes a balance between establishing intervention efficacy and testing whether efficacy varies with intervention intensity. [ABSTRACT FROM AUTHOR]

Because the number of patients waiting for organ transplants exceeds the number of organs available, a better understanding of how transplantation affects the distribution of residual lifetime is needed to improve organ allocation. However, there has been little work to assess the survival benefit of transplantation from a causal perspective. Previous methods developed to estimate the causal effects of treatment in the presence of time-varying confounders have assumed that treatment assignment was independent across patients, which is not true for organ transplantation. We develop a version of G-estimation that accounts for the fact that treatment assignment is not independent across individuals to estimate the parameters of a structural nested failure time model. We derive the asymptotic properties of our estimator and confirm through simulation studies that our method leads to valid inference of the effect of transplantation on the distribution of residual lifetime. We demonstrate our method on the survival benefit of lung transplantation using data from the United Network for Organ Sharing. [ABSTRACT FROM AUTHOR]

Background: Lung transplantation is an effective treatment for patients with advanced lung disease. In the United States, lungs are allocated on the basis of the lung allocation score (LAS), a composite measure of transplantation urgency and utility. Clinical deteriorations result in increases to the LAS; however, whether the trajectory of the LAS has prognostic significance is uncertain.Objective: To determine whether an acute increase in the LAS before lung transplantation is associated with reduced posttransplant survival.Design: Retrospective cohort study of adult lung transplant recipients listed for at least 30 days between 4 May 2005 (LAS implementation) and 31 December 2010 in the United Network for Organ Sharing registry. An acute increase in the LAS was defined as an LAS change (LASΔ) greater than 5 units between the 30 days before and the time of transplantation. Multivariable Cox proportional hazard models were used to examine the relationship between an LASΔ >5 and posttransplant graft survival.Setting: All U.S. lung transplantation centers.Patients: 5749 lung transplant recipients.Measurements: Survival time after lung transplantation.Results: 702 (12.2%) patients experienced an LASΔ >5. These patients had significantly worse posttransplant survival (hazard ratio, 1.31 [95% CI, 1.11 to 1.54]; P = 0.001]) after adjustment for the LAS at transplantation (LAS-T) and other clinical covariates. The effect of an LASΔ >5 was independent of the LAS-T, underlying diagnosis, center volume, or donor characteristics.Limitation: Analysis was based on center-reported data.Conclusion: An acute increase in LAS before transplantation is associated with posttransplant survival after adjustment for LAS-T. Further emphasis on serial assessment of the LAS could improve the ability to offer accurate prediction of survival after transplantation.Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

Background: Lung transplantation is an effective treatment for patients with advanced lung disease. In the United States, lungs are allocated on the basis of the lung allocation score (LAS), a composite measure of transplantation urgency and utility. Clinical deteriorations result in increases to the LAS; however, whether the trajectory of the LAS has prognostic significance is uncertain. Objective: To determine whether an acute increase in the LAS before lung transplantation is associated with reduced posttransplant survival. Design: Retrospective cohort study of adult lung transplant recipients listed for at least 30 days between 4 May 2005 (LAS implementation) and 31 December 2010 in the United Network for Organ Sharing registry. An acute increase in the LAS was defined as an LAS change (LASΔ) greater than 5 units between the 30 days before and the time of transplantation. Multivariable Cox proportional hazard models were used to examine the relationship between an LASΔ > 5 and posttransplant graft survival. Setting: All U.S. lung transplantation centers. Patients: 5749 lung transplant recipients. Measurements: Survival time after lung transplantation. Results: 702 (12.2%) patients experienced an LASΔ >5. These patients had significantly worse posttransplant survival (hazard ratio, 1.31 [95% CI, 1.11 to 1.54]; P = 0.001]) after adjustment for the LAS at transplantation (LAS-T) and other clinical covariates. The effect of an LASΔ > 5 was independent of the LAS-T, underlying diagnosis, center volume, or donor characteristics. Limitation: Analysis was based on center-reported data. Conclusion: An acute increase In LAS before transplantation is associated with posttransplant survival after adjustment for LAS-T. Further emphasis on serial assessment of the LAS could improve the ability to offer accurate prediction of survival after transplantation. Primary Funding Source: National Institutes of Health. [ABSTRACT FROM AUTHOR]

Background: Quality of life (QOL) is an important but understudied outcome after lung transplantation. Previous cross-sectional, single-center studies suggest improved QOL, but few prior longitudinal multicenter data exist regarding the effect of transplantation on the patient’s QOL.Methods: We hypothesized that lung transplantation confers a 1-year QOL benefit in both physical and psychologic well-being; we further hypothesized that the magnitude of benefit would vary by sex, native disease, age, or type of transplant operation. To test these hypotheses, we conducted a secondary analysis using QOL data prospectively and serially measured with the Medical Outcomes Study 36-Item Short-Form Health Survey, version 2 (SF-36) in a multicenter cytomegalovirus prevention clinical trial. Linear mixed-effects models were used to assess the impact of transplantation on the recipient’s QOL.Results: Over the first year after lung transplantation, the SF-36 Physical Component Score significantly increased an average of 10.9 points from baseline levels (P < .0001). A positive benefit was observed for all native diseases; however, the magnitude varied slightly by native disease (P = .04) but not by sex (P = .35), age (P = .06), or transplant type (P = .30). In contrast, the SF-36 Mental Component Score did not change from baseline (P = .36) and remained well below population norms.Conclusions: Our results demonstrate that lung transplantation confers clinically important QOL benefits in physical domains but not in psychologic well-being. A better understanding of the barriers to psychologic well-being after transplant is critical to enhancing the benefits of lung transplantation. [ABSTRACT FROM AUTHOR]

The article presents a secondary analysis on the impact of lung transplantation on the patient's quality of life (QOL) using data prospectively and serially measured with the Medical Outcomes Study 36-Item Short-Form Health Survey, version 2 (SF-36). The study hypothesized that the procedure confers a one-year QOL benefit in both physical and psychologic well-being. Results show that lung transplantation provides clinically important QOL benefits physically but not in psychologic well-being.

Mixed models are commonly used to represent longitudinal or repeated measures data. An additional complication arises when the response is censored, for example, due to limits of quantification of the assay used. While Gaussian random effects are routinely assumed, little work has characterized the consequences of misspecifying the random-effects distribution nor has a more flexible distribution been studied for censored longitudinal data. We show that, in general, maximum likelihood estimators will not be consistent when the random-effects density is misspecified, and the effect of misspecification is likely to be greatest when the true random-effects density deviates substantially from normality and the number of noncensored observations on each subject is small. We develop a mixed model framework for censored longitudinal data in which the random effects are represented by the flexible seminonparametric density and show how to obtain estimates in SAS procedure NLMIXED. Simulations show that this approach can lead to reduction in bias and increase in efficiency relative to assuming Gaussian random effects. The methods are demonstrated on data from a study of hepatitis C virus. [ABSTRACT FROM PUBLISHER]

Objective To illustrate the effects of failing to account for model uncertainty when modelling is used to estimate the global burden of disease, with specific application to childhood deaths from rotavirus infection. Methods To estimate the global burden of rotavirus infection, different random-effects meta-analysis and meta-regression models were constructed by varying the stratification criteria and including different combinations of covariates. Bayesian model averaging was used to combine the results across models and to provide a measure of uncertainty that reflects the choice of model and the sampling variability. Findings In the models examined, the estimated number of child deaths from rotavirus infection varied between 492 000 and 664 000. While averaging over the different models' estimates resulted in a modest increase in the estimated number of deaths (541 000 as compared with the World Health Organization's estimate of 527 000), the width of the 95% confidence interval increased from 105 000 to 198000 deaths when model uncertainty was taken into account. Conclusion Sampling variability explains only a portion of the overall uncertainty in a modelled estimate. The uncertainty owing to both the sampling variability and the choice of model(s) should be given when disease burden results are presented. Failure to properly account for uncertainty in disease burden estimates may lead to inappropriate uses of the estimates and inaccurate prioritization of global health needs. [ABSTRACT FROM AUTHOR]

Chen et al. (2022) recently proposed a set of estimating equations that incorporate data from secondary endpoints to improve precision in parameter estimates related to a primary endpoint. We were motivated to translate their methodology to the context of randomized controlled trials to gain precision in treatment effect estimation using data from secondary endpoints. Our results suggest that this estimator cannot gain efficiency in this context because of random treatment assignment, especially when there is a treatment effect on secondary endpoints, and that further methodological work in this area is needed. [ABSTRACT FROM AUTHOR]

Adolescent school connectedness generally protects from risk behaviors such as tobacco use; however, its relationship to e-cigarette use is unclear. This study examines the relationship between adolescent school connectedness and e-cigarette susceptibility in a diverse longitudinal sample. This secondary analysis of a school-based intervention surveyed 608 middle (66%) and high school (34%) students from 10 schools at 3 time points over 1 year. At baseline, respondents had a mean age of 14 years, 54% were female, and 71% were BIPOC (Black, Indigenous, People of Color). Logistic regression models examined unadjusted and adjusted associations between school connectedness (both baseline and concurrent) and e-cigarette susceptibility over time. E-cigarettes represented the most prevalent form of current nicotine-containing product use in spring 2019 (2.3%), and most respondents reported no e-cigarette susceptibility (69%). E-cigarette susceptibility remained relatively stable during the study. Higher baseline school connectedness levels were associated with lower odds of e-cigarette susceptibility over time. Similarly, higher concurrent school connectedness scores were associated with lower odds of e-cigarette susceptibility over time: spring 2019 (OR, 0.39; 95% CI, 0.32, 0.47), fall 2019 (OR, 0.49; 95% CI, 0.34, 0.72), and spring 2020 (OR, 0.64; 95% CI, 0.47, 0.87). Findings were similar for middle and high school students and did not differ significantly after adjusting for other covariates. Adolescents' school connectedness appears to protect from e-cigarette susceptibility over time, underscoring the importance of promoting positive school experiences to reduce adolescent risk e-cigarette use. [ABSTRACT FROM AUTHOR]

Liver transplantation for inborn errors of metabolism is increasingly common and has historically had positive outcomes. However, this therapeutic modality is not without risks, and patient post-transplant quality of life should be part of the consideration. This retrospective, observational cohort study included all pediatric patients receiving liver transplant from 2010 through 2020 at a single center. Recipients were split into 2 groups based on metabolic or non-metabolic indications for liver transplant. Ten-year patient survival and graft survival were analyzed. The PedsQL Transplant Module and RAND 36-Item Health Survey 1.0 were administered prospectively to those recipients with metabolic indications. Ten-year patient survival was statistically significantly higher in the metabolic group than in the non-metabolic (p <.05), and there was no difference in 10-year graft survival between groups. Of the 12 patients in the metabolic group who completed the PedsQL Transplant Module or RAND 36-Item Health Survey 1.0, the median score was 88, similar to the score seen in healthy children. Liver transplantation for inborn errors of metabolism provides excellent long-term outcomes in terms of patient and graft survival, while maintaining a high quality of life. [ABSTRACT FROM AUTHOR]

Obese transplant recipients (BMI ≥ 30 kg/m2) have decreased posttransplant patient and graft survival compared with their nonobese counterparts. At the same time, many prednisone‐related side effects (eg, new‐onset diabetes) are similar to those associated with obesity. Using SRTR data, we studied outcomes associated with prednisone‐free maintenance immunosuppression (rapid discontinuation of prednisone—RDP). Between January 1, 2000, and December 31, 2014, 44 635 first transplant recipients with BMI ≥ 30 kg/m2 had a first kidney transplant (28 176 DD; 16 459, LD); 12,994 (29%) were discharged from the hospital on a prednisone‐free protocol. We compared outcomes to those discharged on a protocol incorporating maintenance prednisone (intention‐to‐treat analysis). RDP‐treated obese first DD recipients had significantly better patient survival (HR, 0.88; CI, 0.81‐0.96) and graft survival (HR, 0.93; CI, 0.88‐0.99) compared with their counterparts on maintenance immunosuppression. Although not statistically significant, the same trends were seen for LD recipients. For both DD and LD recipients, there was no difference between groups for death‐censored graft survival, suggesting that the benefit of RDP was due to improved patient survival. Our findings suggest that kidney transplant recipients with BMI ≥ 30 kg/m2 benefit from a protocol that incorporates RDP. [ABSTRACT FROM AUTHOR]

Objective: This study determined the characteristics of engagement and whether engagement in an adaptive preventive intervention (API) was associated with reduced binge drinking and alcohol-related consequences. Method: Incoming students were recruited for a sequential multiple assignment randomized trial (SMART; N = 891, 62.4% female, 76.8% non-Hispanic White) with an assessment-only control group. The API occurred during the first semester of college, with outcomes assessed at the end of the semester. The API involved two stages. Stage 1 included universal intervention components (personalized normative feedback [PNF] and self-monitoring). Stage 2 bridged heavy drinkers to access additional resources. We estimated the effect of engagement in Stage 1 only and in the whole API (Stages 1 and 2) among the intervention group, and the effect of the API versus control had all students assigned an API engaged, on alcohol-related outcomes. Results: Precollege binge drinking, intention to pledge a fraternity/sorority, and higher conformity motives were most associated with lower odds of Stage 1 engagement. Action (readiness to change) and PNF engagement were associated with Stage 2 engagement. API engagement was associated with significant reductions in alcohol-related consequences among heavy drinkers. Compared to the control, we estimated the API would reduce the relative increase in alcohol-related consequences from baseline to follow-up by 25%, had all API students engaged. Conclusions: Even partial engagement in each component of the "light-touch" API rendered benefits. Analyses suggested that had all students in the intervention group engaged, the API would significantly reduce the change in alcohol-related consequences over the first semester in college. What is the public health significance of this article?: This study focuses on the characteristics associated with engaging in a brief adaptive preventive intervention and concludes that engagement is associated with reduced alcohol-related consequences among first-year college students. Identifying characteristics associated with intervention engagement (e.g., intending to pledge into a fraternity/sorority, precollege drinking motives and behaviors, readiness to change) can inform efforts to improve intervention implementation and thereby improve outcomes. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular (CV) risk prediction is useful in prioritizing resources and guiding treatment decisions. Most risk prediction equations are constructed using data from cohort studies. However, these cohorts often suffer from lack of diversity or generalizability. Therefore, there is an increasing interest in using electronic health data (EHD) to construct CV risk models. Given the size and complexity of EHD, machine learning (ML) techniques are an appealing alternative to time-to-event regression. However, the nature of EHD is such that subjects have varying duration of follow-up and some event times may be right censored. However, many ML techniques do not accommodate censoring. Methods: We propose a simple and universal method that allows any ML technique to accommodate right-censored data by averaging predictions over a set of weighted bootstrap samples, where the sampling weights are computed using inverse probability of censoring weighting (IPCW). IPCW is a method that reweights the observed data to represent the underlying population by assigning more weight to observed subjects who were likely to be censored. After resampling from the fully observed data, we fit a model and produce predictions in each iteration. Lastly, we offset computational times by using an ensemble. We consider the problem of predicting 5-year CV risk using EHD from a large health system (N=87,348), where 50% of subjects are censored. We compare our method to other ways of handling censoring. Results: Our technique consistently improved calibration by about 70-90% than approaches which ignored censoring (see below). However, there were no differences in discrimination. In conclusion, we show that miscalibration due to censoring can affect real-world treatment decisions, as demonstrated using the 2013 ACC/AHA treatment recommendation for statins. In such a scenario, one does not aim to identify for example top 10% highest risk patients, but rather the actual magnitude of risk for a single patient. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we evaluated the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in randomized controlled trials (RCTs) of very low nicotine content (VLNC) cigarettes. Methods: We combined data from 2 studies to evaluate the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in RCTs of VLNC cigarettes. Using a novel approach that did not require knowledge of each participant's compliance status, we modeled the distributions of total nicotine equivalents (TNE), total cotinine, and anatabine in compliant and noncompliant participants using a mixture model. Estimates of the classification accuracy were derived from the estimated densities. Results: TNE and total cotinine had near-perfect classification accuracy, but TNE had better classification accuracy than total cotinine (p = .03) and anatabine (p = .014). The classification accuracy of TNE and total cotinine decreased as self-reported study cigarettes increased; anatabine was similar, but the results were more statistically variable. Conclusions: TNE and total cotinine are better classifiers of compliance than anatabine. These results will be useful in determining biomarker thresholds for identifying noncompliance and will aid in the interpretation of future RCTs of VLNC cigarettes. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we evaluated the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in randomized controlled trials (RCTs) of very low nicotine content (VLNC) cigarettes. Methods: We combined data from 2 studies to evaluate the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in RCTs of VLNC cigarettes. Using a novel approach that did not require knowledge of each participant's compliance status, we modeled the distributions of total nicotine equivalents (TNE), total cotinine, and anatabine in compliant and noncompliant participants using a mixture model. Estimates of the classification accuracy were derived from the estimated densities. Results: TNE and total cotinine had near-perfect classification accuracy, but TNE had better classification accuracy than total cotinine (p = .03) and anatabine (p = .014). The classification accuracy of TNE and total cotinine decreased as self-reported study cigarettes increased; anatabine was similar, but the results were more statistically variable. Conclusions: TNE and total cotinine are better classifiers of compliance than anatabine. These results will be useful in determining biomarker thresholds for identifying noncompliance and will aid in the interpretation of future RCTs of VLNC cigarettes. [ABSTRACT FROM AUTHOR]

Background: Gonorrhea incidence in the United States has risen by nearly 50% in the last decade, while screening rates have increased. Gonorrhea sequelae rates could indicate whether increased gonorrhea incidence is due to better screening. We estimated the association of gonorrhea diagnosis with pelvic inflammatory disease (PID), ectopic pregnancy (EP), and tubal factor infertility (TFI) in women and detected changes in associations over time. Materials and Methods: This retrospective cohort study included 5,553,506 women aged 18–49 tested for gonorrhea in the IBM MarketScan claims administrative database from 2013–2018 in the United States. We estimated incidence rates and hazard ratios (HRs) of gonorrhea diagnosis for each outcome, adjusting for potential confounders using Cox proportional hazards models. We tested the interaction between gonorrhea diagnosis and the initial gonorrhea test year to identify changes in associations over time. Results: We identified 32,729 women with a gonorrhea diagnosis (mean follow-up time in years: PID = 1.73, EP = 1.75, TFI = 1.76). A total of 131,500 women were diagnosed with PID, 64,225 had EP, and 41,507 had TFI. Women with gonorrhea diagnoses had greater incidence per 1000 person-years for all outcomes (PID = 33.5, EP = 9.4, TFI = 5.3) compared to women without gonorrhea diagnoses (PID = 13.9, EP = 6.7, TFI = 4.3). After adjustment, HRs were higher in women with a gonorrhea diagnosis vs. those without [PID = 2.29 (95% confidence interval, CI: 2.15–2.44), EP = 1.57, (95% CI: 1.41–1.76), TFI = 1.70 (95% CI: 1.47–1.97)]. The interaction of gonorrhea diagnosis and test year was not significant, indicating no change in relationship by initial test year. Conclusion: The relationship between gonorrhea and reproductive outcomes has persisted, suggesting a higher disease burden. [ABSTRACT FROM AUTHOR]

The Heart of New Ulm Project (HONU), is a population-based project designed to reduce modifiable cardiovascular disease (CVD) risk factors in the rural community of New Ulm, MN. HONU interventions address multiple levels of the social-ecological model. The community is served by one health system, enabling the use of electronic health record (EHR) data for surveillance. The purpose of this study was to assess if trends in CVD risk factors and healthcare utilization differed between a cohort of New Ulm residents age 40-79 and matched controls selected from a similar community, using EHR data from baseline (2008-2009) through three follow up time periods (2010-2011, 2012-2013, 2014-2015). Matching, using covariate balance sparse technique, yielded a sample of 4077 New Ulm residents and 4077 controls. We used mixed effects longitudinal models to examine trends over time between the two groups. Blood pressure, total cholesterol, low-density lipoprotein-cholesterol, and triglycerides showed better management in New Ulm over time compared to the controls. The proportion of residents in New Ulm with controlled blood pressure increased by 6.2 percentage points compared to an increase of 2 points in controls (p < 0.0001). As the cohort aged, 10-year ASCVD risk scores increased less in New Ulm (5.1) than the comparison community (5.9). The intervention and control community did not differ with regard to inpatient stays, smoking, or glucose. Findings suggest efficacy for the HONU project interventions for some outcomes. [ABSTRACT FROM AUTHOR]

The net reclassification improvement (NRI) is a widely used metric used to assess the relative ability of 2 risk models to distinguish between low- and high-risk individuals. However, the validity and usefulness of the NRI have been questioned. Criticism of the NRI focuses on its use comparing nested risk models, whereas in practice it is often used to compare nonnested risk models derived from distinct data sources. In this study, we evaluated the performance of the NRI in a nonnested context by using it to compare competing cardiovascular risk-prediction models. We explored the NRI's sensitivity to variations in risk categories and to the calibration of the compared models. We found that the NRI was very sensitive to changes in the definition of risk categories, especially when at least 1 model was miscalibrated. To address these shortcomings, we describe a novel alternative to the usual NRI that uses percentiles of risk instead of cutoffs based on absolute risk. This percentile-based NRI demonstrates the relative ability of 2 models to rank patient risk. It displays more stable behavior, and we recommend its use when there are no established risk categories or when models are miscalibrated. [ABSTRACT FROM AUTHOR]