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Assessment of study success using conditional power (CP), the predictive power of success (PPoS) and probability of success (PoS) is becoming increasingly common for resource optimization and adaption of trials in clinical investigation. Determination of these measures is often a non-trivial mathematical task. Further, the terminologies used across the literature are not consistent, and there is no consolidated presentation on this. We have made a structured presentation on these measures for both trial success and clinical success: first, we have summarized the expressions of CP, PPoS and PoS in a general setting and subsequently, expressions for these measures are obtained for continuous, binary, and time-to-event endpoints in single-arm and two-arm trial settings. Many of these expressions are previously published; however, some of the expressions are very new including the expressions for testing median of time-to-event endpoint in a single-arm trial. We have also shown that 1 / (no. of events) consistently underestimates the variance of log(median) and alternative expression for variance was derived. Examples are given along with the comparison of CP and PPoS. Expressions presented in this paper are implemented in LongCART package in R and in R shiny app https://ppos.shinyapps.io/public/. [ABSTRACT FROM AUTHOR]

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insures from 2001 to 2016. [ABSTRACT FROM AUTHOR]

Opioid-related mortality increased by nearly 400% between 2000 and 2018. In response, federal, state, and local governments have enacted a heterogeneous collection of opioid-related policies in an effort to reverse the opioid crisis, producing a policy landscape that is both complex and dynamic. Correspondingly, there has been a rise in opioid-policy related evaluation studies, as policymakers and other stakeholders seek to understand which policies are most effective. In this paper, we provide an overview of methodological challenges facing opioid policy researchers when conducting opioid policy evaluation studies using observational data, as well as some potential solutions to those challenges. In particular, we discuss the following key challenges: (1) Obtaining high-quality opioid policy data; (2) Appropriately operationalizing and specifying opioid policies; (3) Obtaining high-quality opioid outcome data; (4) Addressing confounding due to systematic differences between policy and non-policy states; (5) Identifying heterogeneous policy effects across states, population subgroups, and time; (6) Disentangling effects of concurrent policies; and (7) Overcoming limited statistical power to detect policy effects afforded by commonly-used methods. We discuss each of these challenges and propose some ways forward to address them. Increasing the methodological rigor of opioid evaluation studies is imperative to identifying and implementing opioid policies that are most effective at reducing opioid-related harms. [ABSTRACT FROM AUTHOR]

Intraclass correlation coefficients (ICCs) are frequently employed in health science research, often to assess intrarater and interrater reliability. In many cases, insufficient details are provided about these ICCs and there seem to be misunderstandings about their selection and how they should be interpreted. This paper is intended primarily to provide a clear, accessible description of ICCs, including how they should be selected, interpreted, and reported. Emphasis is given to areas where researchers seem to encounter the greatest conceptual difficulties and to exhibit the greatest misconceptions. Two extended examples are used to support the points being made. Major additional aims of this paper are to raise the awareness of authors, reviewers, and editors concerning the importance of using appropriate ICCs, and to encourage them to ensure that complete and accurate information about ICCs is reported in journal articles. Failure to do so perpetuates a risk that incorrect decisions might be made about matters that are of crucial importance for people's health. [ABSTRACT FROM AUTHOR]

While record linkage can expand analyses performable from survey microdata, it also incurs greater risk of privacy-encroaching disclosure. One way to mitigate this risk is to replace some of the information added through linkage with synthetic data elements. This paper describes a case study using the National Hospital Care Survey (NHCS), which collects patient records under a pledge of protecting patient privacy from a sample of U.S. hospitals for statistical analysis purposes. The NHCS data were linked to the National Death Index (NDI) to enhance the survey with mortality information. The added information from NDI linkage enables survival analyses related to hospitalization, but as the death information includes dates of death and detailed causes of death, having it joined with the patient records increases the risk of patient re-identification (albeit only for deceased persons). For this reason, an approach was tested to develop synthetic data that uses models from survival analysis to replace vital status and actual dates-of-death with synthetic values and uses classification tree analysis to replace actual causes of death with synthesized causes of death. The degree to which analyses performed on the synthetic data replicate results from analysis on the actual data is measured by comparing survival analysis parameter estimates from both data files. Because synthetic data only have value to the degree that they can be used to produce statistical estimates that are like those based on the actual data, this evaluation is an essential first step in assessing the potential utility of synthetic mortality data. [ABSTRACT FROM AUTHOR]

The nominal group technique (NGT) is a method to elicit healthcare priorities. Yet, there is variability on how to conduct the NGT, and limited guidance on how to analyse a diverse sample of multiple groups. This paper addresses some of this ambiguity, and explores whether different approaches to analysis provide the same outcome/s. Conceptual papers and empirical studies were identified via PubMed and informed an adapted version of the NGT. Twenty-six nominal groups were conducted, which provided in-depth knowledge on how to best conduct this method. Pilot group data were used to compare different analysis methods and to explore how this impacted on reported outcomes. Data analyses for large data-sets are complex; thematic analysis is needed to be able to conduct across group comparisons of participant priorities. Consideration should be given not just to the strength, i.e. sum of votes, or relative importance of the priority, but to the voting frequency, i.e. the popularity of the idea amongst participants; our case study demonstrated that this can affect priority rankings for those ideas with the same score. As a case study, this paper provides practical information on analysis for complex data sets. Researchers need to consider more than one analysis process to ensure that the results truly reflect participant priorities. A priority that has a high score may not necessarily reflect its popularity within the group; the voting frequency may also need to be considered. [ABSTRACT FROM AUTHOR]

Hidden Markov models (HMM) presents an attractive analytical framework for capturing the state-switching process for auto-correlated data. These models have been extended to longitudinal data setting where simultaneous multiple processes are observed by including subject specific random effects. However, application of HMMs for intensive longitudinal data, where each subject gets measured intensively over relatively short period of time, has not been widely studied. In this paper, we extend the mixed hidden Markov model and allow subject heterogeneity with respect to the mean and within subject variance by including subject random effects in both perspectives. We focus on the application of this model to intensive longitudinal studies in psychological and behavioral research where individual's latent states and state-switching process are of interest. Models are estimated using forward–backward algorithm via Bayesian sampling approach. Advantages over regular HMM and mixed HMM that only accounts for the subjects' mean heterogeneity are illustrated through a series of simulation studies. Finally, models are applied to an adolescent mood study data set and results show that the proposed mixed location scale HMM achieves better model fit and more interpretative mood state identification in terms of state specific covariate effects compared to regular HMM and mixed HMM. [ABSTRACT FROM AUTHOR]

The English healthcare sector underwent extensive system reform over the period from 2010 to 2015, aimed principally at improving technical efficiency. This paper examines the effect of the reforms on foundation trusts in England with particular emphasis on technical efficiency. By employing data envelopment analysis (DEA) and a second-stage regression, we found evidence of an overall improvement in efficiency, notwithstanding some fluctuations. Specifically, we found that bed utilization had positive and statistically significant association with the efficiency of acute foundation trusts; suggesting that better management of patient flows and bed utilization might be expected to improve hospital efficiency. We also found evidence to suggest that efficiency might also be improved through better management of staff numbers, optimizing liquidity, and better utilization of assets such as buildings and information technology. [ABSTRACT FROM AUTHOR]

The purpose of this study is to illustrate how matching approaches based on different balancing scores lead to variations in the treatment effect estimators. We introduced a case study evaluating the effect of anti-thrombotic agents on the severe progression of patients with intracerebral hemorrhage. We extracted subpopulations based on propensity and prognostic scores and then estimated the relative risk between the treatment groups. Furthermore, to illustrate the situation where the treatment effect estimates varied depending on employed balancing scores, we conducted a simulation experiment. In the case study, the matching using different balancing scores extracted subpopulations with different characteristics. Then, the estimated relative risk (95% confidence interval) was 1.27 (0.98–1.94) among the propensity score matched cohort, whereas it was 0.91 (0.76–1.08) among the prognostic score matched cohort. In the simulation experiments, the results indicated that the matching schemes based on different balancing scores created distinct matched cohorts, leading to varying estimates under treatment effect heterogeneity. Moreover, the variability of the estimated effect becomes substantial when there are covariates strongly related to the dependent variable of the nuisance functions. The difference in the selected subpopulation via matching based on different balancing scores is a thoughtful factor that can result in different estimates when there is effect heterogeneity. In practice, we recommend assessing the characteristics of the matched subpopulation and employing the balancing score that can estimate the treatment effect among the target population of interest in each study. Plain Language Summary: This study aimed to understand why different methods of matching patients in a study can lead to different estimates of how well a treatment works. We used a real example of a study where the treatment's effectiveness varied for different patients and compared how different methods of matching patients affected the results. We also did a series of experiments to see how the different methods affected estimates of treatment effect when the effectiveness varied among patients. Our study found that the different matching methods extracted the different patients. The difference in the matched patients led to very different results, especially when the treatment's effectiveness was not the same for all patients. Therefore, it is important to check the characteristics of patients after matching and consider whether the treatment's effectiveness is not the same for all patients. [ABSTRACT FROM AUTHOR]

Social processes and social context are increasingly recognized as key factors shaping health-related behaviors and outcomes. One social process that may be acting within social networks is social influence, in which an individual's characteristic (e.g., specific health behavior) is potentially impacted by the corresponding characteristic of connected individuals in the network. In the health services context, healthcare providers who work together and share patients may influence each other through the knowledge transmission or development of clinical practice norms. Although many statistical techniques assume independence of data points, when analyzing data that may reflect social processes acting across a social network, it is imperative to account for the interdependencies (i.e., non-independence) across individuals. In practice, studies account for nonindependence in the context of estimating bivariate relations (correlations or linear regression) using a variety of analytic methods (some of which have previously been shown to yield biased results). To date, it is unclear which methods yield acceptable false positive rates, unbiased coefficient estimates, and acceptable statistical power, because there have been no systematic simulation studies comparing methods for addressing network nonindependence arising from social influence. To address this gap, we compared eight commonly used methods that purport to account for nonindependence using simulated network data. While results indicated that none of the techniques reduced false positive rates to the predicted (nominal) 0.05 level, random sampling of network nodes was the method that yielded the smallest false positive rates, yet came at a price of reduced statistical power. Further methodological development is needed. [ABSTRACT FROM AUTHOR]

Propensity score methods (e.g., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates. These methods were developed in the context of data with no hierarchical structure or clustering. Yet in many applications the data have a clustered structure that is of substantive importance, such as when individuals are nested within healthcare providers or within schools. Recent work has extended propensity score methods to a multilevel setting, primarily focusing on binary exposures. In this paper, we focus on propensity score weighting for a continuous, rather than binary, exposure in a multilevel setting. Using simulations, we compare several specifications of the propensity score: a random effects model, a fixed effects model, and a single-level model. Additionally, our simulations compare the performance of marginal versus cluster-mean stabilized propensity score weights. In our results, regression specifications that accounted for the multilevel structure reduced bias, particularly when cluster-level confounders were omitted. Furthermore, cluster mean weights outperformed marginal weights. [ABSTRACT FROM AUTHOR]

Hospital care focuses on improving patients' long-term quality of life, yet hospital quality metrics typically focus on short-term processes. Attempting to understand a patient's long-term process introduces sample selection bias since patients must survive the hospitalization in order to observe post-hospitalization outcomes. As a result, proper analysis of long-term outcomes should account for clustering, due to the hierarchical structure of hospital data, as well as sample selection bias. The objective of this paper was to evaluate random effect parameter estimation and higher-level ranking of long-term count outcomes and short-term selection processes in the presence of cluster and selection bias by comparing multilevel Poisson models, multilevel zero-inflated Poisson models, and multilevel Poisson sample selection models (MPSSMs) in a series of simulations. We simulated an outcome resembling a post-discharge Poisson count with a pre-specified selection process determining a patient's hospitalization survival with each hospital having a unique effect on both processes. In order to clarify the methodology, we also analyzed a real-world hospital dataset involving a count outcome conditioned on the selection process of hospital survival. Across all simulations, the random effect parameter estimates were directly compared and the empirical Bayes estimates were extracted, ranked, and compared using the Spearman rank correlation. Results show that the MPSSM produces more accurate random effect parameter estimates and higher-level empirical Bayes ranks. When modeling multilevel effects on long-term count outcomes observed after a short-term selection process, higher-level effects are more reliably measured using MPSSMs. [ABSTRACT FROM AUTHOR]

In this paper we advance a dual-prong method for evaluating whether data from a natural experiment can be leveraged to draw convincing implications about causal effects of neighborhood context on health. In particular, we probe a natural experiment's ability to surmount potentially confounding measurement issues arising from geographic selection bias. We offer methods for assessing the degree of this potential bias both immediately after households are assigned to neighborhoods and over longer durations. We then illustrate these methods using data from a natural experiment involving scattered-site public housing in Denver and test whether neighborhood context affects the hazard of children's asthma diagnoses. We find that, conditioned on ethnicity, the tenant allocation process used by the Denver Housing Authority produced a quasi-random initial assignment across neighborhood characteristics and that the range of bias arising subsequently through selective out-migration can be narrowly delineated by replicating analyses for alternative samples defined by period of residence in public housing. We further find support for the proposition that observing no correlation between neighborhood and observed household characteristics is tantamount to observing no correlation between neighborhood and (typically) unobserved household characteristics. There appears to be little bias reintroduced through selective out-migration and staying in initially assigned neighborhood. These findings suggest that, after employing the evaluation strategy suggested here, natural experiments can indeed be a powerful tool for assessing neighborhood effects on health. [ABSTRACT FROM AUTHOR]

Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many policy evaluation studies do not attempt to control for effects of co-occurring policies, and this issue has not received extensive attention in the methodological literature to date. In this study, we utilized Monte Carlo simulations to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state policy evaluations. Simulation conditions varied effect sizes of the co-occurring policies and length of time between policy enactment dates, among other factors. Outcome data (annual state-specific opioid mortality rate per 100,000) were obtained from 1999 to 2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files, thus yielding longitudinal annual state-level data over 18 years from 50 states. When co-occurring policies are ignored (i.e., omitted from the analytic model), our results demonstrated that high relative bias (> 82%) arises, particularly when policies are enacted in rapid succession. Moreover, as expected, controlling for all co-occurring policies will effectively mitigate the threat of confounding bias; however, effect estimates may be relatively imprecise (i.e., larger variance) when policies are enacted in near succession. Our findings highlight several key methodological issues regarding co-occurring policies in the context of opioid-policy research yet also generalize more broadly to evaluation of other state-level policies, such as policies related to firearms or COVID-19, showcasing the need to think critically about co-occurring policies that are likely to influence the outcome when specifying analytic models. [ABSTRACT FROM AUTHOR]

In addition to applications in meta-analysis, funnel plots have emerged as an effective graphical tool for visualizing the detection of health care providers with unusual performance. Although there already exist a variety of approaches to producing funnel plots in the literature of provider profiling, limited attention has been paid to elucidating the critical relationship between funnel plots and hypothesis testing. Within the framework of generalized linear models, here we establish methodological guidelines for creating funnel plots specific to the statistical tests of interest. Moreover, we show that the test-specific funnel plots can be created merely leveraging summary statistics instead of individual-level information. This appealing feature inhibits the leak of protected health information and reduces the cost of inter-institutional data transmission. Two data examples, one for surgical patients from Michigan hospitals and the other for Medicare-certified dialysis facilities, demonstrate the applicability to different types of providers and outcomes with either individual- or summary-level information. [ABSTRACT FROM AUTHOR]

Purpose: There is a lack of well-validated self-report measures to assess public speaking anxiety. This study explored the psychometric properties of the Public Speaking Anxiety Scale (PSAS). Methods: Seventy-two adults completed the PSAS as part of the baseline screening procedure of a randomized controlled trial. Rasch analysis was used to assess the scale's response category functioning, precision, targeting, unidimensionality, and differential item functioning. Construct validity was assessed using classical test theory methods. Results: While thresholds were ordered and no systematic bias in responses for age, gender, or screen failure was found, the PSAS demonstrated evidence of multidimensionality (variance by first factor = 39.7%, eigenvalue of first contrast = 2.76). Multidimensionality was resolved after splitting the scale into two discrete subscales: Emotional and Physiological. Three misfitting items (i.e. item 5 from Emotional, items 6 and 14 from Physiological) were removed. Scale precision and targeting remained suboptimal after subscale split and removal of misfitting items (PSI = 1.41, PR = 0.67 for Emotional; PSI = 1.49, PR = 0.69 for Physiological). Conclusion: The PSAS demonstrated adequate convergent validity. Psychometric properties of the PSAS after Rasch-guided modifications were overall promising. Further studies are needed to confirm our results. [ABSTRACT FROM AUTHOR]

Causal variance decompositions for a given disease-specific quality indicator can be used to quantify differences in performance between hospitals or health care providers. While variance decompositions can demonstrate variation in quality of care, causal mediation analysis can be used to study care pathways leading to the differences in performance between the institutions. This raises the question of whether the two approaches can be combined to decompose between-hospital variation in an outcome type indicator to that mediated through a given process (indirect effect) and remaining variation due to all other pathways (direct effect). For this purpose, we derive a causal mediation analysis decomposition of between-hospital variance, discuss its interpretation, and propose an estimation approach based on generalized linear mixed models for the outcome and the mediator. We study the performance of the estimators in a simulation study and demonstrate its use in administrative data on kidney cancer care in Ontario. [ABSTRACT FROM AUTHOR]

Revisions of hip and knee arthroplasty implants and cardiac pacemakers pose a large medical and economic burden for society. Consequently, the identification of health care providers with potential for quality improvements regarding the reduction of revision rates is a central aim of quality assurance in any healthcare system. Even though the time span between initial and possible subsequent operations is a classical time-to-event endpoint, hospital-specific quality indicators are in practice often measured as revisions within a fixed follow-up period and subsequently analyzed by traditional methods like proportions or logistic regression. Methods from survival analysis, in contrast, allow the inclusion of all observations, i.e. also those with early censoring or events, and make thus more efficient and more timely use of the available data than traditional methods. This may be obvious to a statistician but in an applied context with historic traditions, the introduction of more complicated methods needs a clear presentation of their added value. We demonstrate how standard survival methods like the Kaplan–Meier estimator and a multiplicative hazards model outperform traditional methods with regard to the identification of performance outliers. Following that, we use the proposed methods to analyze 640,000 hip and knee replacement operations with about 13,000 revisions between 2015 and 2016 in more than 1200 German hospitals in the annual evaluation of quality of care. Based on the results, performance outliers are identified which are to be further investigated qualitatively with regard to their provided quality of care and possible necessary measures for improvement. Survival analysis is a sound statistical framework for analyzing data in the context of quality assurance and survival methods outperform the statistical methods that are traditionally used in this area. [ABSTRACT FROM AUTHOR]

Data from the National Health and Nutrition Examination Survey have been linked to the Center for Medicare and Medicaid Services' Medicaid Enrollment and Claims Files for the survey years 1999–2004. The linked data are produced by the National Center for Health Statistics' (NCHS) Data Linkage Program and are available in the NCHS Research Data Center. This project compares the usefulness of multiple imputation to account for data linkage ineligibility and other survey nonresponse with currently recommended weight adjustment procedures. Estimated differences in environmental smoke exposure across Medicaid/Children's Health Insurance Program (CHIP) enrollment status among children ages 3–15 years are examined as a motivating example. Comparisons are drawn across the three different estimates: one that uses MI to impute the administrative Medicaid/CHIP status of those who are ineligible for linkage, a second that uses the linked data restricted to linkage eligible participants with a basic weight adjustment, and a third that uses self-reported Medicaid/CHIP status from the survey data. The results indicate that estimates from the multiple imputation analysis were comparable to those found when using weight adjustment procedures and had the added benefit of incorporating all survey participants (linkage eligible and linkage ineligible) into the analysis. We conclude that both multiple imputation and weight adjustment procedures can effectively account for survey participants who are ineligible for linkage. [ABSTRACT FROM AUTHOR]

Propensity score-based methods have been widely developed to adjust for confounders in observational studies to estimate causal treatment effect for binary treatments. We generalize these causal inference methods to the multi-level treatment case. We review the generalized causal inference framework and several propensity score estimation methods. We conduct a comprehensive simulation study to evaluate the performance of multinomial logistic regression, generalized boosted models, random forest and data adaptive matching score for estimating propensity scores based on inverse probability of treatment weighting. From our findings, multinomial logistic regression is susceptible to yielding extreme weights while a mis-specified model is assumed, which results in poor performance of the inverse probability weighted estimator. On the other hand, machine-learned propensity scores tend to have less biased and more stable performance, and the data adaptive matching score tends to perform the best overall. The above-mentioned propensity score based methods are applied to the Taobao dataset to evaluate the causal effect of reputation on sales. [ABSTRACT FROM AUTHOR]

Randomized clinical trials often serve the purpose of assessing the efficacy and safety of a compound. By combining real-world evidence and randomization, pragmatic randomized clinical trials (PrCTs) can be used to inform treatment effectiveness and healthcare decisions. PrCTs, referring to studies where several pragmatic elements are used (eligibility, endpoints, follow-up, etc.), pose unique challenges (Loudon et al. in BMJ 350:h2147, 2015). From a literature review, we propose a definition of PrCT and discuss strategies to overcome some PrCT challenges. Use of alternative data collection approaches may lead to uncertainties, and absence of blinding could potentially lead to non-random missing data at study endpoints such that randomization is no longer protected by an intent to treat. Therefore, more complex randomization strategies may be needed to minimize bias. Additional data sources could be used to synthesize information and create a more accurate endpoint definition, which may require tools such as natural language processing. The statistician must become familiar with the challenges and strengths of PrCTs, ranging from design to analysis to interpretation, in order to transform data into evidence (Califf in Clin Trials 13:471-477, 2016). [ABSTRACT FROM AUTHOR]

Prognostic scores have been proposed as outcome based confounder adjustment scores akin to propensity scores. However, prognostic scores have not been widely used in the substantive literature. Instead, comorbidity scores, which are limited versions of prognostic scores, have been used extensively by clinical and health services researchers. A comorbidity is an existing disease an individual has in addition to a primary condition of interest, such as cancer. Comorbidity scores are used to reduce the dimension of a vector of comorbidity variables into a single scalar variable. Such scores are often added to regression models with other non-comorbidity variables such as age and sex, both for analyzing prognosis and for confounder adjustment when analyzing treatment effects. Despite their widespread use, the properties of and conditions under which comorbidity scores are valid dimension reduction tools in statistical models is largely unknown. In this article, we show that under relatively standard assumptions, comorbidity scores can have equal prognostic and confounder-adjustment abilities as the individual comorbidity variables, but that biases can occur if there are additional effects, such as interactions, of covariates beyond that captured by the comorbidity score. Simulations were performed to illustrate empirical properties and a data example using breast cancer data from the SEER Medicare Database demonstrates the application of these results. [ABSTRACT FROM AUTHOR]

Patient-centered care involves patients in decision-making about their medical care. Effective shared decision-making requires relevant evidence on the expected health outcomes of treatment, assessment of patient preferences for possible health outcomes, and a method to synthesize this information. Few tools exist to help clinicians and patients synthesize clinical evidence and personal preferences. We develop a statistical framework that combines multiple data sources on expected treatment outcomes with individual preferences to produce a personalized preference-weighted outcome score (PWOS). The PWOS can be calculated for multiple treatments and compared to determine which will provide the best balance of risks and benefits given a patient's preferences. We demonstrate our method by evaluating adjuvant chemotherapy regimens for colorectal cancer. We begin by identifying heterogeneity in patient preferences for health outcomes associated with colorectal cancer disease and treatment, finding classes of patients who value efficacy and side effects differently. Then we fit a hierarchical Bayesian model to randomized clinical trials and produce posterior distributions of expected outcomes under three chemotherapy regimens. We use the PWOS to combine distinct sets of patient preferences with these expected outcomes. Our method preserves estimation uncertainty and accounts for correlation among outcomes. In a simulation study of applying PWOS to make decisions at the population level, we find that our methods are most useful when there are large differences in individual preferences and small distinctions in treatment efficacy (i.e., preference-sensitive settings). [ABSTRACT FROM AUTHOR]

Exposures derived from electronic health records (EHR) may be misclassified, leading to biased estimates of their association with outcomes of interest. An example of this problem arises in the context of cancer screening where test indication, the purpose for which a test was performed, is often unavailable. This poses a challenge to understanding the effectiveness of screening tests because estimates of screening test effectiveness are biased if some diagnostic tests are misclassified as screening. Prediction models have been developed for a variety of exposure variables that can be derived from EHR, but no previous research has investigated appropriate methods for obtaining unbiased association estimates using these predicted probabilities. The full likelihood incorporating information on both the predicted probability of exposure-class membership and the association between the exposure and outcome of interest can be expressed using a finite mixture model. When the regression model of interest is a generalized linear model (GLM), the expectation-maximization algorithm can be used to estimate the parameters using standard software for GLMs. Using simulation studies, we compared the bias and efficiency of this mixture model approach to alternative approaches including multiple imputation and dichotomization of the predicted probabilities to create a proxy for the missing predictor. The mixture model was the only approach that was unbiased across all scenarios investigated. Finally, we explored the performance of these alternatives in a study of colorectal cancer screening with colonoscopy. These findings have broad applicability in studies using EHR data where gold-standard exposures are unavailable and prediction models have been developed for estimating proxies. [ABSTRACT FROM AUTHOR]

Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion software beanz can facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE. Topics highlighted include shrinkage estimation, model choice, sensitivity analysis, and posterior predictive checking. A case study is presented in which we demonstrate the use of the methods discussed. [ABSTRACT FROM AUTHOR]

Care management is becoming increasingly offered in the U.S. as a means of helping patients better manage their chronic diseases and possibly avoid worsening or exacerbations of their conditions, which can result in potentially avoidable and costly healthcare services. Since care management can be provided using different intervention methods and by different entities, we sought to compare different models of care management delivery on their long-term medical savings. Specifically, we compare health plan provided care management to provider delivered care management. Evaluation of the effectiveness of care management programs can be challenging because it can take time for patients to make recommended changes and then demonstrate healthcare savings associated with those changes. In this study, we modeled the unknown form of the time-varying program effects using a spline-based technique in the Bayesian framework, where the number and locations of knots were treated unknown and learned via reversible jump Markov chain Monte Carlo. We also addressed additional modeling challenges from features seen in our healthcare cost data such as highly right-skewed outcomes with non-constant variances and extra zeros. To provide a more robust analysis, we incorporated a follow-up period of up to 4 years that is longer than the most of the published studies on care management. The results of this work demonstrate that cost savings do accrue with specific models of care management. In particular, the embedded model of care management was significantly more effective than the health plan provided care management in controlling medical costs after 2 years of engagement, and the savings increased over time. This information should help policy makers and employer groups achieve a better understanding of the potential value of care management. [ABSTRACT FROM AUTHOR]

Recurrent event data is frequently encountered in biomedical research. Estimators describing the time to the next event occurrence could be useful information in healthcare settings to deliver targeted, preemptive, patient-specific care. Distribution estimation of recurrence times is varied based on both the underlying assumptions of the estimator (e.g., dependence vs. independence of recurrence times within individual) and estimator construct. In an example among Veterans with spinal cord injury, distributional changes in recurrence times of health complications subsequent lower extremity (LE) fracture are explored. Veterans with LE fracture (cases) were matched to Veterans without LE fracture (controls) on demographic (age, race), level of injury (paraplegia vs. tetraplegia), extent of injury, Veterans Affairs connected service status, and comorbidities using Mahalanobis metric matching. Stratified distributional estimates of recurrence times between successive morbidity outcomes are compared between Veterans with/without LE fracture using three estimators: independent identically distributed product limit estimator, Wang-Chang product limit estimator, and a gamma frailty maximum likelihood estimator. It can be seen that the estimator selection can provide a very different showcasing of a recurrence time distribution. There is a change in the time-to-recurrence of recurrent urinary tract infections and pressure ulcers for fracture cases directly following LE fracture, however testing if this difference is statistically significant remains unclear. Causal inference of gap time analyses in observational data with recurrent events is considered and a call for methods in this area is much warranted. [ABSTRACT FROM AUTHOR]

In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, the average treatment effect is often estimated by using propensity scores. Typically, propensity scores are estimated by logistic regression. More recent suggestions have been to employ nonparametric classification algorithms from machine learning. In this article, we propose a weighted estimator combining parametric and nonparametric models. Some theoretical results regarding consistency of the procedure are given. Simulation studies are used to assess the performance of the newly proposed methods relative to existing methods, and a data analysis example from the Surveillance, Epidemiology and End Results database is presented. [ABSTRACT FROM AUTHOR]

I present an improved methodology for estimating local prevalence rates using classical econometric methods. I provide information on the variation within national mental health surveys associated with ICD versus DSM coding. Conditional on the validity of national survey responses, I estimate precise and statistically significant models associated with binary measures of mental health diagnoses. I also present estimates from polychotomous discrete choice allowing for covariance in errors. Focusing on binary discrete measures, empirical results from NCS-R and NSADMHP are qualitatively similar though very different from NHIS. I speculate that, to a significant degree, this occurs because both NCS-R and NSADMHP rely on popular screening tools to mechanically diagnosis sample participants, while NHIS relies on self-diagnosis. I also discuss the effects on local prevalence estimates caused by unobserved community-specific effects. Finally, I use the results to make policy statements about the provision of public mental health services in central Virginia; the results document a severe shortage of services for people who are unlikely to be able to afford services in the private market. [ABSTRACT FROM AUTHOR]