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Parkinson's disease is related to nervous system disorders in human beings. In this paper, the status of Parkinson's disease has been predicted based on the analysis of features of two Parkinson's disease symptoms: Dysphonia and Dysarthria. The Dysphonia symptom has been measured by analyzing the features extracted from the vocal sounds of a patient. The Dysarthria symptom of Parkinson's disease has been analyzed by the features extracted from the speech signals of a patient. Hence, two different prediction models have been built for deriving the status of Parkinson's disease. The derivation of these prediction systems is based on statistical and discriminant feature analysis. The statistical-based feature analysis is carried out using descriptive and inferential analysis. In contrast, the enteric, deviation, and correlation-based information between the features are utilized for descriptive analysis. The inferential analysis has been performed using hypothetical testing of the distribution of features. The descriptive and inferential feature analyses are used to derive effective features for the Parkinson's disease problem based on the Dysphonia and Dysarthria symptoms. In the discriminant feature analysis, the feature sets undergo deriving discriminant features. The best-selected features are used to build a prediction model for detecting Parkinson's disease problems in a patient. Extensive experimentation has been carried out using a Dysphonia dataset containing 195 samples. Each sample has 23 features. One dataset for Dysarthria symptoms contains 756 observations, with each observation having 754 features. The best prediction models that have been obtained correspond to these datasets, and 93.50% is obtained for Dysphonia and 87.63% for Dysarthria symptoms databases, respectively. The performance of the proposed system has been compared with some existing methods concerning each employed dataset, showing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

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]

Growing healthcare costs have been accompanied by increased policymakers' interest in the efficiency of healthcare systems. Network formation by hospitals as a vehicle for consolidation and achieving economies of scale has emerged as an important topic of conversation among academics and practitioners. Within networks, consolidation of particular specialties or entire campuses is expected and encouraged to take place. This paper describes the main findings of an effort to build gravity-type models to describe patient choices in inpatient and daycare hospital facilities. It analyzes the distance decay effects as a function of car travel times and great-circle distance, and it offers a method for inclusion of university hospitals. Additionally, it reviews the impact of driving and transit accessibility on hospital attraction and reviews the differences in distance decay for patient age groups and hospitalization types. In the described application, the best models achieve a Mean Absolute Percentage Error of around 10% in non-metropolitan areas, and 14.5% across different region types. Results in metropolitan areas suggest that latent factors unrelated to proximity and size have a significant role in determining hospital choices. Furthermore, the effects of relative driving and transit accessibility are found to be small or non-existent. [ABSTRACT FROM AUTHOR]

Analysis of public policy affecting dual eligibles requires accurate identification of survey respondents eligible for both Medicare and Medicaid. Doing so for Medicaid is particularly challenging given the complex eligibility rules tied to income and assets. In this paper we provide guidance on how to best identify eligible respondents in household surveys that have limited income or asset information, such as the National Health Interview Survey, American Community Survey, Current Population Survey, and Medical Expenditure Panel Survey. We show how two types of errors—false negative and false positive errors—are impacted by incorporating limited income or asset information, relative to the commonly-used approach of solely comparing total income to the income threshold. With the 2018 Health and Retirement Study, which has detailed income and asset information, we mimic the income and asset information available in those other household surveys and quantify how errors change when imposing income or asset tests with limited information. We show that incorporating all available income and asset data results in the lowest number of errors and the lowest overall error rates. We recommend that researchers adjust income and impose the asset test to the fullest extent possible when imputing Medicaid eligibility for Medicare enrollees. [ABSTRACT FROM AUTHOR]

In a medical setting, observational studies commonly involve patients who initiate a particular treatment (e.g., medication therapy) and others who do not, and the goal is to draw causal inferences about the effect of treatment on a time-to-event outcome. A difficulty with such studies is that the notion of a treatment initiation time is not well-defined for the control group. In this paper, we propose a Bayesian approach to estimate treatment effects in longitudinal observational studies where treatment is given by indication and thereby the exact timing of treatment is only observed for treated units. We present a framework for conceptualizing an underlying randomized experiment in this setting based on separating the time of indication for treatment, which we model using a latent state-space process, from the mechanism that determines assignment to treatment versus control. Next, we develop a two-step inferential approach that uses Markov Chain Monte Carlo (MCMC) posterior sampling to (1) infer the unobserved indication times for units in the control group, and (2) estimate treatment effects based on inferential conclusions from Step 1. This approach allows us to incorporate uncertainty about the unobserved indication times which induces uncertainty in both the selection of the control group and the measurement of time-to-event outcomes for these controls. We demonstrate our approach to study the effects on mortality of inappropriately prescribing phosphodiesterase type 5 inhibitors (PDE5Is), a medication contraindicated for certain types of pulmonary hypertension, using data from the Veterans Affairs (VA) health care system. [ABSTRACT FROM AUTHOR]

The preponderance of large-scale healthcare databases provide abundant opportunities for comparative effectiveness research. Evidence necessary to making informed treatment decisions often relies on comparing effectiveness of multiple treatment options on outcomes of interest observed in a small number of individuals. Causal inference with multiple treatments and rare outcomes is a subject that has been treated sparingly in the literature. This paper designs three sets of simulations, representative of the structure of our healthcare database study, and propose causal analysis strategies for such settings. We investigate and compare the operating characteristics of three types of methods and their variants: Bayesian Additive Regression Trees (BART), regression adjustment on multivariate spline of generalized propensity scores (RAMS) and inverse probability of treatment weighting (IPTW) with multinomial logistic regression or generalized boosted models. Our results suggest that BART and RAMS provide lower bias and mean squared error, and the widely used IPTW methods deliver unfavorable operating characteristics. We illustrate the methods using a case study evaluating the comparative effectiveness of robotic-assisted surgery, video-assisted thoracoscopic surgery and open thoracotomy for treating non-small cell lung cancer. [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]

Difference-in-difference (DD) methods are a common strategy for evaluating the effects of policies or programs that are instituted at a particular point in time, such as the implementation of a new law. The DD method compares changes over time in a group unaffected by the policy intervention to the changes over time in a group affected by the policy intervention, and attributes the 'difference-in-differences' to the effect of the policy. DD methods provide unbiased effect estimates if the trend over time would have been the same between the intervention and comparison groups in the absence of the intervention. However, a concern with DD models is that the program and intervention groups may differ in ways that would affect their trends over time, or their compositions may change over time. Propensity score methods are commonly used to handle this type of confounding in other non-experimental studies, but the particular considerations when using them in the context of a DD model have not been well investigated. In this paper, we describe the use of propensity scores in conjunction with DD models, in particular investigating a propensity score weighting strategy that weights the four groups (defined by time and intervention status) to be balanced on a set of characteristics. We discuss the conceptual issues associated with this approach, including the need for caution when selecting variables to include in the propensity score model, particularly given the multiple time point nature of the analysis. We illustrate the ideas and method with an application estimating the effects of a new payment and delivery system innovation (an accountable care organization model called the 'Alternative Quality Contract' (AQC) implemented by Blue Cross Blue Shield of Massachusetts) on health plan enrollee out-of-pocket mental health service expenditures. We find no evidence that the AQC affected out-of-pocket mental health service expenditures of enrollees. [ABSTRACT FROM AUTHOR]

Regression, propensity score (PS) and double-robust (DR) methods can reduce selection bias when estimating average treatment effects (ATEs). Economic evaluations of health care interventions exemplify complex data structures, in that the covariate–endpoint relationships tend to be highly non-linear, with highly skewed cost and health outcome endpoints. When either the regression or PS model is correct, DR methods can provide unbiased, efficient estimates of ATEs, but generally the specification of both models is unknown. Regression-adjusted matching can also protect against bias from model misspecification, but has not been compared to DR methods. This paper compares regression-adjusted matching to selected DR methods (weighted regression and augmented inverse probability of treatment weighting) as well as to regression and PS methods for addressing selection bias in cost-effectiveness analyses (CEA). We contrast the methods in a CEA of a pharmaceutical intervention, where there are extreme estimated PSs, hence unstable inverse probability of treatment (IPT) weights. The case study motivates a simulation which considers settings with functional form misspecification in the PS and endpoint regression models (e.g. cost model with log instead of identity link), stable and unstable PS weights. We find that in the realistic setting of unstable IPT weights and misspecifications to the PS and regression models, regression-adjusted matching reports less bias than DR methods. We conclude that regression-adjusted matching is a relatively robust method for estimating ATEs in applications with complex data structures exemplified by CEA. [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]

In this paper, we introduce several statistical methods to evaluate the uncertainty in the concentration index ( C) for measuring socioeconomic equality in health and health care using aggregated total population register data. The C is a widely used index when measuring socioeconomic inequality, but previous studies have mainly focused on developing statistical inference for sampled data from population surveys. While data from large population-based or national registers provide complete coverage, registration comprises several sources of error. We simulate confidence intervals for the C with different Monte Carlo approaches, which take into account the nature of the population data. As an empirical example, we have an extensive dataset from the Finnish cause-of-death register on mortality amenable to health care interventions between 1996 and 2008. Amenable mortality has been often used as a tool to capture the effectiveness of health care. Thus, inequality in amenable mortality provides evidence on weaknesses in health care performance between socioeconomic groups. Our study shows using several approaches with different parametric assumptions that previously introduced methods to estimate the uncertainty of the C for sampled data are too conservative for aggregated population register data. Consequently, we recommend that inequality indices based on the register data should be presented together with an approximation of the uncertainty and suggest using a simulation approach we propose. The approach can also be adapted to other measures of equality in health. [ABSTRACT FROM AUTHOR]

This paper develops a model for defining Cesarean sections' over- and under-utilization as deviations from clinically appropriate treatment due to non-clinical factors. Physician decisions can be affected by both monetary and non-monetary incentives, and the perception of the patient's medical information and preferences. This structural model elucidates the physician decision-making process and identifies and tests for the degree of deviation from appropriate treatment (over- and under-utilization). The model is applied to estimate over-utilization of Cesarean section deliveries performed in the state of New Jersey during the 1999-2002 period. Non-clinically appropriate Cesarean sections occur at a moderate but growing rate of roughly 3.2 %. The growth of the Cesarean section rate in New Jersey over these years was explained mainly by non-clinical factors. [ABSTRACT FROM AUTHOR]

The need to harmonize different outcome metrics is a common problem in research synthesis and economic evaluation of health interventions and technology. The purpose of this paper is to describe the use of multidimensional item response theory (IRT) to equate different scales which purport to measure the same construct at the item level. We provide an overview of multidimensional IRT in general and the bi-factor model which is particularly relevant for applications in this area. We show how both the underlying true scores of two or more scales that are intended to measure the same latent variable can be equated and how the item responses from one scale can be used to predict the item responses for a scale that was not administered but are necessary for the purpose of economic evaluations. As an example, we show that a multidimensional IRT model predicts well both the EQ-5D descriptive system and the EQ-5D preference index from SF-12 data which cannot be directly used to perform an economic evaluation. Results based on multidimensional IRT performed well compared to traditional regression methods in this area. A general framework for harmonization of research instruments based on multidimensional IRT is described. [ABSTRACT FROM AUTHOR]

Change point estimation is recognized as an essential tool of root cause analyses within quality control programs as it enables clinical experts to search for potential causes of change in hospital outcomes more effectively. In this paper, we consider estimation of the time when a linear trend disturbance has occurred in survival time following an in-control clinical intervention in the presence of variable patient mix. To model the process and change point, a linear trend in the survival time of patients who underwent cardiac surgery is formulated using hierarchical models in a Bayesian framework. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. We use Markov Chain Monte Carlo to obtain posterior distributions of the change point parameters including the location and the slope size of the trend and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time cumulative sum control chart (CUSUM) control charts for different trend scenarios. In comparison with the alternatives, step change point model and built-in CUSUM estimator, more accurate and precise estimates are obtained by the proposed Bayesian estimator over linear trends. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered. [ABSTRACT FROM AUTHOR]

Accurate interval estimation of standardized hospital morbidity rates is essential for comparing health care utilization across geographical areas or different populations. There are typically repeated hospital admissions for individuals in the population in any specific time period; hence counts of admissions may not be well modelled by standard distributions for count data based on a Poisson assumption. This leads to empirical distributions of the counts with heavier tails than the Poisson. This paper complies and reviews various approaches for interval estimation of standardized hospital morbidity rates used at health agencies and examines their suitability under a broad range of conditions. The focus here is on approaches used for automated production of rates for developing atlases, using large-scale (e.g., country-wide) medical databases. We consider parametric models which incorporate such overdispersion, including the zero-inflated Poisson, negative binomial, zero-inflated negative binomial and Poisson-inverse-Gaussian distributions. Additionally, we consider the use of robust methods based on simple moment estimators for computing the mean and variance of the distribution of the counts, and Poisson-based methods currently utilized for some published rates when patient-level data are unavailable. A simulation study is conducted to compare the different approaches. Various confidence interval construction methods are also examined. Our results indicate that the moment approach as well as confidence intervals based on a log transformation of the rate provide more accurate inference for morbidity rates than the other methods. We recommend the use of the moment approach due to its simplicity of implementation. Some cautions regarding ad hoc approaches currently in use are also provided. [ABSTRACT FROM AUTHOR]

Classic instrumental variable techniques involve the use of structural equation modeling or other forms of parameterized modeling. In this paper we use a nonparametric, matching-based instrumental variable methodology that is based on a study design approach. Similar to propensity score matching, though unlike classic instrumental variable approaches, near/far matching is capable of estimating causal effects when the outcome is not continuous. Unlike propensity score matching, though similar to instrumental variable techniques, near/far matching is also capable of estimating causal effects even when unmeasured covariates produce selection bias. We illustrate near/far matching by using Medicare data to compare the effectiveness of carotid arterial stents with cerebral protection versus carotid endarterectomy for the treatment of carotid stenosis. [ABSTRACT FROM AUTHOR]

Instrumental variables (IVs) enable causal estimates in observational studies to be obtained in the presence of unmeasured confounders. In practice, a diverse range of models and IV specifications can be brought to bear on a problem, particularly with longitudinal data where treatment effects can be estimated for various functions of current and past treatment. However, in practice the empirical consequences of different assumptions are seldom examined, despite the fact that IV analyses make strong assumptions that cannot be conclusively tested by the data. In this paper, we consider several longitudinal models and specifications of IVs. Methods are applied to data from a 7-year study of mental health costs of atypical and conventional antipsychotics whose purpose was to evaluate whether the newer and more expensive atypical antipsychotic medications lead to a reduction in overall mental health costs. [ABSTRACT FROM AUTHOR]

Mixed-effects regression models have extraordinary capacity to capture the essence of complex longitudinal data. In this paper we present analyses from a longitudinal study on 157 patients with osteoarthritis (OA) where real-time momentary self-reported pain and physical activity level were collected. We illustrate the application of heterogeneous mixed effect (HME) models to examine the effect of time-varying activity level assessments (sedentary vs. moderate/vigorous) during the 30 min prior to the pain recording on mean pain levels and variability. We also examine the variation in pain associated with activity level and the degree that participant characteristics (weight-bearing vs. non-weight bearing joint w/OA, body mass index, white vs. non-white race, gender) and psychosocial factors (mood, tension, pain catastrophizing) influence pain variation. We fit separate HME models for each characteristic or factor. These HME models are fit with standard statistical software, but there are challenges to fitting and interpreting these models. Once fit the models provide a powerful tool to help guide researchers in understanding highly individualized pain trajectories. [ABSTRACT FROM AUTHOR]

The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as 'good balance'. Good balance guarantees less biased estimates of the treatment effect. However, the cost of achieving good balance is that the variance of the estimates increases due to a reduction in effective sample size, either through the introduction of propensity score weights or dropping cases when propensity score matching. In this paper, we investigate whether it is best to optimize the balance or to settle for a less than optimal balance and use double robust estimation to adjust for remaining differences. We compare treatment effect estimates from regression, propensity score weighting, and double robust estimation with varying levels of effort expended to achieve balance using data from a study about the differences in outcomes by HIV status in heterosexually active homeless men residing in Los Angeles. Because of how costly data collection efforts are for this population, it is important to find an alternative estimation method that does not reduce effective sample size as much as methods that aggressively aim to optimize balance. Results from a simulation study suggest that there are instances in which we can obtain more precise treatment effect estimates without increasing bias too much by using a combination of regression and propensity score weights that achieve a less than optimal balance. There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance. [ABSTRACT FROM AUTHOR]

Two major statistical issues confronting comparative analyses of hospital outcomes are adequacy of case-mix adjustment and proper accounting for random variation. Hierarchical modeling has been proposed to improve precision and reduce the impact of random variation but becomes difficult to implement when there are numerous case-mix factors to control. In this paper we formulate the problem of hospital performance comparisons within the framework of potential outcomes and illustrate an approach to hospital comparisons which combines multiple category propensity score methods for the control of case-mix variations with hierarchical Bayesian modeling of case-mix adjusted summaries. The approach is similar to that proposed by Huang et al. (Health Serv Res 40:253-278, ) but extends their approach by using a Bayesian model to accommodate hospital level attributes and to facilitate joint modeling of performance for multiple outcomes. The analytical approach is illustrated by a comparison of 30 day post admission mortality risks for patients treated for acute myocardial infarction, pneumonia or stroke in 34 New Zealand public hospitals. In a small simulation study, reported in electronic supplementary material, hierarchical models outperformed non-hierarchical models, achieving both better credible interval coverage and shorter average interval lengths for measures of between hospital variation based on contrasts between the 90th and 10th percentiles of the mortality risk distribution. Simulation performance of hierarchical and non-hierarchical models in detecting unusual performance was similar. [ABSTRACT FROM AUTHOR]

Introduction: EQ-5D is an internationally acknowledged tool for assessing health-related quality of life. Our aim was to examine how pragmatic dynamics may influence answers to the EQ-5D-5 L in items where the structure of answer options is disjunctive. Methods: We performed a 3-step linguistic analysis building on the seminal work of Grice, including (1) examination of the lexical meanings of the answer options, (2) considerations of how conversational maxims might affect the respondent's interpretation of compatible answer options under a single item, and (3) analysis of how the questionnaire's context might counteract the problem of omitted answer options by shifting the meaning of context-sensitive expressions. Results: All items with disjunctive answer options exhibit both compatibilities and omissions. In combination with the disjunctive form of answer options these features of the EQ-5D-5 L complicates the communicative task for respondents relying on conversational norms to identify the most suitable answers to the instrument's questions. Discussion: In items where answer options have a disjunctive structure, respondents relying on Gricean conversational maxims will have to depend on their individual understanding of fine-grained details concerning the questionnaire's purpose and may have to weigh how conflicting norms should be balanced. While such dynamics are likely to go undetected in cognitive interviews, linguistic analysis may help to identify them. [ABSTRACT FROM AUTHOR]

Simulation studies were performed to investigate for which conditions of sample size of patients (n) and number of repeated measurements (k) (e.g., raters) the optimal (i.e., balance between precise and efficient) estimations of intraclass correlation coefficients (ICCs) and standard error of measurements (SEMs) can be achieved. Subsequently, we developed an online application that shows the implications for decisions about sample sizes in reliability studies. We simulated scores for repeated measurements of patients, based on different conditions of n, k, the correlation between scores on repeated measurements (r), the variance between patients' test scores (v), and the presence of systematic differences within k. The performance of the reliability parameters (based on one-way and two-way effects models) was determined by the calculation of bias, mean squared error (MSE), and coverage and width of the confidence intervals (CI). We showed that the gain in precision (i.e., largest change in MSE) of the ICC and SEM parameters diminishes at larger values of n or k. Next, we showed that the correlation and the presence of systematic differences have most influence on the MSE values, the coverage and the CI width. This influence differed between the models. As measurements can be expensive and burdensome for patients and professionals, we recommend to use an efficient design, in terms of the sample size and number of repeated measurements to come to precise ICC and SEM estimates. Utilizing the results, a user-friendly online application is developed to decide upon the optimal design, as 'one size fits all' doesn't hold. [ABSTRACT FROM AUTHOR]

Selective mortality and nonresponse in longitudinal analyses would lead to biased estimates. In this study, we draw data from the 1992–2016 Health and Retirement Study and used a multinomial logit model to examine the impacts of participants' demographics, health conditions, and socioeconomic status on both follow-up status in 2016 (Always-in; Died; Other Attritors, non-death sample attrition; Ever-out, skipped some intermediate surveys) and between-wave dropout. We then applied an inverse probability weighting approach to compensate for attrition in the analysis of education and hospitalizations. We found that many demographics (e.g., sex, age, race, ethnicity, marital status), socioeconomic factors (e.g., education, house ownership, labor force participation) and health conditions (e.g., self-reported health, and chronic conditions) had large and statistically significant associations with loss of follow-up. Our results show that loss of follow-up leads to substantial underestimation of the education-hospitalization association. After correcting attrition bias in a pooled cross-sectional analysis, the association of having a high school degree, some college, and college or above with any two-year hospitalizations increased by 59.7%, 72.9%, and 42.6%, respectively. Differences in estimates before and after correcting attrition bias were only statistically significant for college graduates, but who make up 24.7% of the "always-in" sample. In the longitudinal analysis of the association between education and hospitalization, correcting attrition bias also increases estimates of education by up to 58.9%, although not statistically significant. It suggests that empirical analyses that inform health policy decisions using the Health and Retirement Study should assess attrition bias from selective mortality and nonresponse. [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]

Many medical studies have used non-randomized sampling, which tends to be the case in research that involves a surgical procedure. Intra-operative procedures and actions conducted by the attending surgical team may be based on pre-operative conditions of the patient, which can introduce endogeneity. Using acute kidney injury (AKI) following cardiopulmonary bypass surgery as a research setting, the present study uses a control function approach to explain why two perfusionist-directed principle components of delivered oxygen (DO2) while the patient is on pump—hemoglobin and cardiac index—need to be treated as endogenous variables rather than exogenous ones. We further show conditions in which the exogenous model understates and overstates the predicted probability of AKI. [ABSTRACT FROM AUTHOR]

Unmeasured confounding undermines the validity of observational studies. Although randomized clinical trials (RCTs) are considered the "gold standard" of study types, we often observe divergent findings between RCTs and empirical settings. We present the "L-table", a simulation-based, prior knowledge (e.g., RCTs) guided approach that estimates the true effect adjusting for the potential influence of unmeasured confounders when using observational data. Using electronic health record data from Kaiser Permanente Southern California, we compare the effectiveness of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) on endpoints at 1, 3, 5, and 10 years for patients with stable ischemic heart disease. We applied the L-table approach to the propensity score adjusted cohort to derive the omitted-confounder-adjusted estimated effects. After the L-table adjustment, CABG patients are 57.6% less likely to encounter major adverse cardiac and cerebrovascular event (MACCE) at 1 year (OR [95% CI] 0.424 [0.396, 0.517]), 56.4% less likely at 3 years (OR [95% CI] 0.436 [0.369, 0.527]), and 48.9% less likely at 5 years (OR [95% CI] 0.511 [0.451, 0.538]). CABG patients are also 49.5% less likely to die by the end of 10 years than PCI patients (OR [95% CI] 0.505 [0.446, 0.582]). We found the estimated true effects all shifted towards CABG as a more effective procedure that led to better health outcomes compared to PCI. Unlike existing sensitivity tools, the L-table approach explicitly lays out probable values and can therefore better support clinical decision-making. We recommend using L-table as a supplement to available techniques of sensitivity analysis. [ABSTRACT FROM AUTHOR]

The SF-36 is a commonly used tool for measuring health status in a general population. Despite the overall moderate to high validity scores, certain communicative dynamics of the questionnaire deserve attention. Our aim was to examine how pragmatic dynamics, semantic context-sensitivity and epistemic reflection may influence answers to the SF-36. We applied a three-step Gricean analysis, including identification of the items in which pragmatic dynamics are most likely to have a significant effect, examination of how Gricean maxims might affect the answers given to the items identified, and assessment of whether the combined influence of linguistic context-sensitivity and pragmatic norms is benign. We found that the pragmatic dynamics of scalar implicatures are crucial to the interpretation of answer options but generally benign to its purpose. Regarding context, we raised concerns about the answer option 'Ved ikke' (Don't know); rather than representing a neutral midpoint, the answer is compatible with both a positive and a negative answer option. Whereas scalar implicatures are helpful to the purpose of SF-36, other contextual effects appear to be more worrisome. However, since pragmatic norms of communication, semantic context-sensitivity, and attention to epistemic error possibilities can all be expected to shape answers to the SF-36, we think that all three factors belong in a description of how the questionnaire works. [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]

To introduce Random Forest (RF), a machine learning method, in an accessible way for health services researchers and highlight its unique considerations when applied to health administrative data. Physician claims' data from the universal public insurer linked with the Canadian Community Health Survey for the Canadian province of Quebec. We describe in detail how RF can be useful in health services research, provide guidance on data set up, modeling decisions and demonstrate how to interpret results. We also highlight specific considerations for applying RF to health administrative data. In a working example, we compare RF with logistic regression, Ridge regression and LASSO in their ability to predict whether a person has a regular medical doctor. We use survey responses to "do you have a regular medical doctor" from three cycles of the Canadian Community Health Survey (2007, 2009, 2011). Responses are linked with physician claims' data from 2002 to 2012. We limit our cohort to persons 40 years and older at the time of responding to the survey. We discuss the strengths and weaknesses of using RF in a health services research setting in comparison to using more conventional modeling techniques. Applying a RF model in a health services research setting can have advantages over conventional modeling approaches and we encourage health services researchers to add RF to their toolbox of predictive modeling methods. [ABSTRACT FROM AUTHOR]

Constructing accurate patient transfer networks between hospitals is critical for understanding the spread of healthcare associated infections through statistical and mathematical modeling, and for determining optimal screening and treatment strategies. The Healthcare Cost & Utilization Project (HCUP) State Inpatient Databases (SID) provide valuable information on patient transfers from publicly obtainable claims databases, yet often give an incomplete picture due to missingness of patient tracking identifiers. We designed a novel imputation algorithm that enabled us to estimate the true number of patient transfers between each pair of hospitals in a state over a specified time period and age group in the presence of these missing identifiers. We then validated the algorithm's performance through a series of simulation experiments using the HCUP SID, and finally tested the algorithm on multiple states' genuine data. Our proposed method significantly reduced the total mean squared error in predicting the true number of transfers amongst hospitals for all simulation experiments, and it also yielded epidemic simulations that more closely approximated those corresponding to the true patient transfer network. [ABSTRACT FROM AUTHOR]

A frequent concern in making statistical inference for causal effects of a policy or treatment based on observational studies is that there are unmeasured confounding variables. The instrumental variable method is an approach to estimating a causal relationship in the presence of unmeasured confounding variables. A valid instrumental variable needs to be independent of the unmeasured confounding variables. It is important to control for the confounding variable if it is correlated with the instrument. In health services research, socioeconomic status variables are often considered as confounding variables. In recent studies, distance to a specialty care center has been used as an instrument for the effect of specialty care vs. general care. Because the instrument may be correlated with socioeconomic status variables, it is important that socioeconomic status variables are controlled for in the instrumental variables regression. However, health data sets often lack individual socioeconomic information but contain area average socioeconomic information from the US Census, e.g., average income or education level in a county. We study the effects on the bias of the two stage least squares estimates in instrumental variables regression when using an area-level variable as a controlled confounding variable that may be correlated with the instrument. We propose the aggregated instrumental variables regression using the concept of Wald's method of grouping, provided the assumption that the grouping is independent of the errors. We present simulation results and an application to a study of perinatal care for premature infants. [ABSTRACT FROM AUTHOR]

Surveys, questionnaires, and assessments are the only approved means for measuring outcomes, yet there is a paucity of evidence that these tools can perform well as outcomes measures. A major barrier to utilizing these tools as measures of improvement in care is an inability to recognize when the responses are incomplete, inaccurate, or insincere. In this study, we propose a hybrid unsupervised-supervised approach to identify inauthentic assessments via anomalous response patterns. The study utilized 60-question behavioral assessment from a clinical population served by a county-operated public children's behavioral health services (n = 42,945). A novel hybrid unsupervised-supervised approach was developed to identify inauthentic assessment records from highly dimensional assessment data without the need for a priori record labels, which would otherwise require countless hours of record review by highly trained clinical staff. A neural network trained with 75% of the labeled data recognized anomalous response patterns in the test data with 84.5% sensitivity and 97.3% specificity. The model identified 26% of records as potentially inauthentic based on anomalous response patterns. For mental health and behavioral health, this novel method could flag a relatively small proportion of the records for clinical review while allowing records with probable authenticity to bypass review processes, saving time and creating efficiency in care. This method is also a potential tool for improving service quality through the identification of circumstances that have been overlooked but could otherwise be addressed during care. For decades, assessment data has been collected by policy and ignored by practice due to limitations of utility. With the right approaches, we could uncover the patterns of what works for whom that are currently hidden in our existing assessments. [ABSTRACT FROM AUTHOR]

Methods for identifying heterogeneity of treatment effects in randomized trials have seen recent advances, yet applying these methods to health services intervention trials has not been well investigated. Our objective was to compare two approaches—predictive risk modeling and model-based recursive partitioning—for identifying subgroups of trial participants with potentially differential response to an intervention involving health risk assessment completion alone (n = 192) versus health risk assessment completion plus telephone-delivered health coaching (n = 173). Notably, these approaches have been developed by investigators from distinct disciplines and reported in separate literatures and have generally not been compared in prior work. Furthermore, these methods approach subgroup identification differently and answer related but slightly different questions. The primary outcome for both approaches was prevention health program enrollment by six months. The predictive risk model was developed in two steps, where, first, a single risk score was derived from a logistic regression model with 12 a priori chosen covariates by the scientific investigator team (c-statistic = 0.63). Then, the treatment effect was calculated within quartiles of risk via interaction in a logistic regression model (c-statistic = 0.69; c-for-benefit = 0.43). The greatest treatment effect was in the second quartile, in which 54% (22 of 41) of intervention patients and 10% (5 of 50) of control patients reported prevention program enrollment. In contrast, with the data-driven approach of model-based recursive partitioning, all 28 baseline covariates were considered, with the algorithm selecting covariates and optimal split points. Final model results had a c-statistic of 0.69 and a c-for-benefit of 0.55 (optimism-corrected c-statistic = 0.62 and c-for-benefit = 0.53) and identified 4 subgroups, with the greatest treatment effect among patients with lower mean numeracy, education less than a bachelor's degree, and diabetes, in which 54% (15 of 28) of intervention patients reported prevention program enrollment versus 7% (3 of 41) of control patients. While there is increasing interest in discovering heterogeneity of treatment effects, our analyses highlight the important differences between these approaches, both from questions answered, model development, and results obtained. Specifying goals of treatment heterogeneity analyses, choosing the appropriate method to best address the goals, and external validation of results are important steps when applying these methods. Clinicaltrials.gov identifier: NCT01828567 [ABSTRACT FROM AUTHOR]

Marital status is recognized as an important social determinant of health, income, and social support, but is rarely available in administrative data. We assessed the feasibility of using exact address data and zip code history to identify cohabiting couples using the 2018 Medicare Vital Status file and ZIP codes in the 2011–2014 Master Beneficiary Summary Files. Medicare beneficiaries meeting our algorithm displayed characteristics consistent with assortative mating and resembled known married couples in the Health and Retirement Study linked to Medicare claims. Address information represents a promising strategy for identifying cohabiting couples in administrative data including healthcare claims and other data types. [ABSTRACT FROM AUTHOR]

As healthcare costs continue to increase, studies assessing costs are becoming increasingly common, but researchers planning for studies that measure costs differences (savings) encounter a lack of literature or consensus among researchers on what constitutes "small" or "large" cost savings for common measures of resource use. Other fields of research have developed approaches to solve this type of problem. Researchers measuring improvement in quality of life or clinical assessments have defined minimally important differences (MID) which are then used to define magnitudes when planning studies. Also, studies that measure cost effectiveness use benchmarks, such as cost/QALY, but do not provide benchmarks for cost differences. In a review of the literature, we found no publications identifying indicators of magnitude for costs. However, the literature describes three approaches used to identify minimally important outcome differences: (1) anchor-based, (2) distribution-based, and (3) a consensus-based Delphi methods. In this exploratory study, we used these three approaches to derive MID for two types of resource measures common in costing studies for: (1) hospital admissions (high cost); and (2) clinic visits (low cost). We used data from two (unpublished) studies to implement the MID estimation. Because the distributional characteristics of cost measures may require substantial samples, we performed power analyses on all our estimates to illustrate the effect that the definitions of "small" and "large" costs may be expected to have on power and sample size requirements for studies. The anchor-based method, while logical and simple to implement, may be of limited value in cases where it is difficult to identify appropriate anchors. We observed some commonalities and differences for the distribution and consensus-based approaches, which require further examination. We recommend that in cases where acceptable anchors are not available, both the Delphi and the distribution-method of MID for costs be explored for convergence. [ABSTRACT FROM AUTHOR]

Limited research exists on the health of the homeless population, and often relies on large health care datasets and varied methods, with little work to understand how the choice of measurement, such as an address-based indicator versus diagnosis code, affects the estimates of demographics and comorbidity burden of this population. Using state-wide hospital discharge data, we identified homeless individuals three ways: via ICD-10 Z59.0, an administrative indicator, and those with both. From 2,427,549 discharges, 17,479 individuals were identified via only Z-code, 9183 via only the indicator, and 4578 had both. There were differences in the race, sex, payer, and admission type between the groups. Similarly, comorbidity profiles were different, with the differences reaching 11.4% for alcohol abuse. The difference in the size, composition, and comorbidities between these groups suggests that researchers, clinicians, and policy experts should be wary of current methods to identify homeless individuals and continue to improve capture of this determinant of health. Simply, if we are unable to accurately identify the group of interest and their potential health needs we are equally unable to effectively intervene and provide appropriate and relevant care. [ABSTRACT FROM AUTHOR]

Men engaging in reproductive coercion may coerce, force, or deceive female partners into pregnancy. This study evaluates whether the 3-month incidence of pregnancy is higher among women reporting reproductive coercion than similar women reporting no reproductive coercion. We tested this hypothesis in longitudinal data from a sample of African-American women ages 18–24 recruited from community settings in Atlanta, Georgia, US, in 2012–2014 (n = 560). Participants were surveyed at baseline, 3 months, 6 months, 9 months, and 12 months. To reduce selection bias, we used full matching on 22 baseline variables related to demographics, economic power, risky alcohol use, and gender-based power inequality. We used logistic regression in the matched sample with outcome pregnancy 3 months later, controlling for baseline fertility intentions (n = 482, n = 458, n = 452 at respectively 3, 6, 9 months). At 3 months, 15% of women reported reproductive coercion. At 6 months, 11.3% of women reporting coercion were pregnant vs. 4.6% of matched women reporting no coercion (p = 0.06). Women reporting coercion had 3 times the odds of pregnancy as matched women reporting no coercion (AOR 2.95, 95% CI (1.16, 6.98), p = 0.02). Among women pregnant after coercion, only 15% wanted to be pregnant then or sooner. Women reporting reproductive coercion are at greater risk of unwanted or mistimed pregnancies, and the semen exposure that caused these pregnancies could also transmit STI/HIV. Clinicians should screen patients for reproductive coercion; consider using semen exposure biomarkers such as PSA or Yc-PCR to identify condom sabotage or stealthing; and refer women experiencing reproductive coercion to supportive services. [ABSTRACT FROM AUTHOR]

In the United States the number of health systems that own practices or hospitals have increased in number and complexity leading to interest in assessing the relationship between health organization factors and health outcomes. However, the existence of multiple types of organizations combined with the nesting of some hospitals and practices within health systems and the nesting of some health systems within larger health systems generates numerous analytic objectives and complicates the construction of optimal survey designs. An objective function that explicitly weighs all objectives is theoretically appealing but becomes unwieldy and increasingly ad hoc as the number of objectives increases. To overcome this problem, we develop an alternative approach based on constraining the sampling design to satisfy desired statistical properties. For example, to support evaluations of the comparative importance of factors measured in different surveys on health system performance, a constraint that requires at least one organization of each type (corporate owner, hospital, practice) to be sampled whenever any component of a system is sampled may be enforced. Multiple such constraints define a nonlinear system of equations that "couples" the survey sampling designs whose solution yields the sample inclusion probabilities for each organization in each survey. A Monte Carlo algorithm is developed to solve the simultaneous system of equations to determine the sampling probabilities and extract the samples for each survey. We illustrate the new sampling methodology by developing the constraints and solving the ensuing systems of equations to obtain the sampling design for the National Surveys of United States Health Care Systems, Hospitals and Practices. We illustrate the virtues of "coupled sampling" by comparing the proportion of eligible systems for whom the corporate owner and both a hospital and a practice that are expected to be sampled to that expected under alternative sampling designs. Comparative and descriptive analyses that illustrate features of the sampling design are also presented. [ABSTRACT FROM AUTHOR]

Researchers, healthcare providers, and policy makers have become increasingly interested in the cost and quality effects of vertical integration (VI) between hospitals and physicians. However, tracking VI is often financially costly. Because the Medicare Data on Provider Practice and Specialty (MD-PPAS) annual dataset may be more cost-effective for researchers to access than private data sources, we examine the accuracy of MD-PPAS in identifying VI by comparing it to physician and hospital affiliations reported in Blue Cross Blue Shield Texas (BCBSTX) PPO claims data for 2014–2016. The BCBSTX data serve as a gold standard, because physician–hospital affiliations are based on the insurer's provider contract information. We merged the two datasets using the physician National Provider Identifier (NPI), then determined what percentage of physicians had the same Tax Identification Number (TIN) in both sources, and whether the TIN implied the physician belonged to a physician- or hospital-owned practice. We found that 71.3% of successfully matched NPIs reported the same TIN, and 95.1% of patient-level observations were attributed to organizations with the same ownership type in both datasets, regardless of TIN. We compared regression estimates of patient-level annual spending on an indicator variable for physician versus hospital ownership for the primary attributed physician and found that estimates were within one percentage point whether one determined VI based on the BCBSTX or the MD-PPAS data. The results suggest that MD-PPAS, which costs less to obtain than from a for-profit data source, can be used to reliably track VI between hospitals and physicians. [ABSTRACT FROM AUTHOR]

To generate a Hospital Medical Surge Preparedness Index that can be used to evaluate hospitals across the United States in regard to their capacity to handle patient surges during mass casualty events. Data from the American Hospital Association's annual survey, conducted from 2005 to 2014. Our sample comprised 6239 hospitals across all 50 states, with an annual average of 5769 admissions. An extensive review of the American Hospital Association survey was conducted and relevant variables applicable to hospital inpatient services were extracted. Subject matter experts then categorized these items according to the following subdomains of the "Science of Surge" construct: staff, supplies, space, and system. The variables within these categories were then analyzed through exploratory and confirmatory factor analyses, concluding with the evaluation of internal reliability. Based on the combined results, we generated individual (by hospital) scores for each of the four metrics and an overall score. The exploratory factor analysis indicated a clustering of variables consistent with the "Science of Surge" subdomains, and this finding was in agreement with the statistics generated through the confirmatory factor analysis. We also found high internal reliability coefficients, with Cronbach's alpha values for all constructs exceeding 0.9. A novel Hospital Medical Surge Preparedness Index linked to hospital metrics has been developed to assess a health care facility's capacity to manage patients from mass casualty events. This index could be used by hospitals and emergency management planners to assess a facility's readiness to provide care during disasters. [ABSTRACT FROM AUTHOR]

Difference-in-differences combined with matching on pre-treatment outcomes is a popular method for addressing non-parallel trends between a treatment and control group. However, previous simulations suggest that this approach does not always eliminate or reduce bias, and it is not clear when and why. Using Medicaid claims data from Oregon, we systematically vary the distribution of two key unobservables—fixed effects and the random error term—to examine how they affect bias of matching on pre-treatment outcomes levels or trends combined with difference-in-differences. We find that in most scenarios, bias increases with the standard deviation of the error term because a higher standard deviation makes short-term fluctuations in outcomes more likely, and matching cannot easily distinguish between these short-term fluctuations and more structural outcome trends. The fixed effect distribution may also create bias, but only when matching on pre-treatment outcome levels. A parallel-trend test on the matched sample does not reliably distinguish between successful and unsuccessful matching. Researchers using matching on pre-treatment outcomes to adjust for non-parallel trends should report estimates from both unadjusted and propensity-score matching adjusted difference-in-differences, compare results for matching on outcome levels and trends and examine outcome changes around intervention begin to assess remaining bias. [ABSTRACT FROM AUTHOR]

Given a shifted exponential distribution, the exact sampling distribution of the Gini index has been derived by Moothathu (Ann Inst Stat Math 37: 473-479, ). However, direct derivation of confidence interval of the Gini index via inverting the sampling distribution requires highly intensive computational power. In this note, we propose an exact interval estimation procedure through the concept of generalized confidence interval introduced by Weerahandi (J Am Stat Assoc 88: 899-905, ). The proposed method is much more computationally efficient and can be readily extended for Type-II doubly censored data. The idea is further developed for the interval estimation of the difference of Gini indexes from two shifted exponential distributions. Numerical studies show that the generalized interval estimator is better than commonly used asymptotic approximation and bootstrap in term of coverage probability, particularly for small to median sample size. The proposed approaches are illustrated via an application to a previously published data set. [ABSTRACT FROM AUTHOR]

Research has suggested that accessible primary healthcare may help control hospitalization for certain conditions that are often considered either preventable or treatable through primary healthcare. However, only limited research has tested this widely accepted hypothesis. We extend research on preventable hospitalization in the framework of cost-effectiveness analysis. Using hospitalization data from South Carolina, we show how access to primary healthcare may affect the volume and cost of hospitalizations for ambulatory care sensitive conditions. Our statistical models for effectiveness and cost take advantage of spatial methods to adjust for neighborhood characteristics. This adjustment also addresses correlations among variables describing each given area, providing appropriate results despite those correlations. We also use propensity scores to adjust for county level variation in access to primary healthcare. We observe that the mean differential volume and cost of lower extremity amputation associated with diabetes was significantly lower in areas served by community health centers than in other areas. This result provides support for the hypothesis that increasing access to primary healthcare may help to control the volume and cost of preventable hospitalization for some conditions. [ABSTRACT FROM AUTHOR]

Research on determinants of workplace presenteeism is based on the assumption that presenteeism is the outcome of a decision process with two options: taking sick leave, or attending work despite illness. However, frequency measures of sickness presenteeism are usually employed as the dependent variables in previous research on the determinants of the decision process. Monte Carlo simulations show that the analytical strategies used in previous research are likely to identify determinants of vulnerability, but will underestimate the determinants that affect the decision process under consideration. As a solution to this shortcoming, an alternative analytical strategy is presented, which uses an empirical estimator of the individual presenteeism propensity. [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]

Confounding is widely recognized in settings where all variables are fully observed, yet recognition of and statistical methods to address confounding in the context of latent class regression are slowly emerging. In this study we focus on confounding when regressing a distal outcome on latent class; extending standard confounding methods is not straightforward when the treatment of interest is a latent variable. We describe a recent 1-step method, as well as two 3-step methods (modal and pseudoclass assignment) that incorporate propensity score weighting. Using simulated data, we compare the performance of these three adjusted methods to an unadjusted 1-step and unadjusted 3-step method. We also present an applied example regarding adolescent substance use treatment that examines the effect of treatment service class on subsequent substance use problems. Our simulations indicated that the adjusted 1-step method and both adjusted 3-step methods significantly reduced bias arising from confounding relative to the unadjusted 1-step and 3-step approaches. However, the adjusted 1-step method performed better than the adjusted 3-step methods with regard to bias and 95 % CI coverage, particularly when class separation was poor. Our applied example also highlighted the importance of addressing confounding-both unadjusted methods indicated significant differences across treatment classes with respect to the outcome, yet these class differences were not significant when using any of the three adjusted methods. Potential confounding should be carefully considered when conducting latent class regression with a distal outcome; failure to do so may results in significantly biased effect estimates or incorrect inferences. [ABSTRACT FROM AUTHOR]

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

We apply three separate panel data estimation methods to examine the diffusion of technologies at the state-level. These methods include the Hausman-Taylor random effects model, the fixed effects vector decomposition, and generalized estimating equations. We discuss the assumptions required of each and assess the stability of our policy results across the three models for a longitudinal study of the diffusion of newer psychotropic technologies. We find a reasonable level of consistency among marginal effects for time varying independent variables between our three estimation methods but some discrepancy in the estimated measure of precision in our empirical application. We find a number of policy conclusions are quite stable across estimation methods and may be of interest to state-level mental health policy decision makers. [ABSTRACT FROM AUTHOR]

The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival. [ABSTRACT FROM AUTHOR]