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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]

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]

The World Health Organization Disability Assessment Schedule (WHODAS 2.0) is the official generic assessment instrument of WHO for disability and health. It is considered to be applicable across cultures in adult populations. The paper describes the development, cultural adaptation and validation of the Greek version of the official WHODAS 2.0, 36-item assessment schedule. The research tested the psychometric properties of this version in order to identify if it can be considered a valid and reliable instrument for measuring health and disability in the Greek population and culture. Field test studies were conducted in two hundred participants of the general population, aged over 61 years old, in a Greek Open Care Centre for the elderly. The study was carried out between July 2015 and September 2016. The linguistic adaptation of the original instrument was performed according to the most recent guidelines. Ratings of the different items were statistically analyzed to investigate the construct, criterion and concurrent validity and reliability of the instrument. Construct validity is confirmed in the Greek version of WHODAS 2.0. Concurrent validity is well established between the scores of WHODAS 2.0 and SF-36 v2. Cronbach’s α (0.673–0.901) and ICC (0.793–0.885) values indicate satisfactory reliability. The study supports the validity and reliability of the official Greek version of WHODAS 2.0, which can be used to assess effectively the quality of life and disability levels of the intended population and inform policy-making. Findings will be further tested in larger and diversified samples in subsequent parts of the study. [ABSTRACT FROM AUTHOR]

Reliability refers to how measurements can produce consistent results and are crucial for any scientific research measurement. Intraclass correlation coefficient (ICC) is the most widely used method to determine the reproducibility of measurements of various statistical techniques. Calculated ICC and its confidence interval that reveal the underlying sampling distribution may help detect an experimental method's ability to identify systematic differences between research participants in a test. This study aimed to introduce a new SAS macro, ICC6, for calculating different ICC forms and their confidence intervals. A SAS macro that employs the PROC GLM procedure in SAS was created to generate two-way random effects (ANOVA) estimates. A simulated dataset was used to input the macro to calculate the point estimates for different ICCs. The ICC forms' upper and lower confidence interval limits were calculated using the F statistics distribution. Our SAS macro provides a complete set of various ICC forms and their confidence intervals. A validation analysis using commercial software packages STATA and SPSS delivered identical results. A development of SAS methodology using publicly available statistical approaches in estimating six distinct forms of ICC and their confidence intervals has been reported in this article. This work is an extension of general methodology supported by a few other statistical software packages to SAS. [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]

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]

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]

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]

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]

While propensity score weighting has been shown to reduce bias in treatment effect estimation when selection bias is present, it has also been shown that such weighting can perform poorly if the estimated propensity score weights are highly variable. Various approaches have been proposed which can reduce the variability of the weights and the risk of poor performance, particularly those based on machine learning methods. In this study, we closely examine approaches to fine-tune one machine learning technique [generalized boosted models (GBM)] to select propensity scores that seek to optimize the variance-bias trade-off that is inherent in most propensity score analyses. Specifically, we propose and evaluate three approaches for selecting the optimal number of trees for the GBM in the twang package in R. Normally, the twang package in R iteratively selects the optimal number of trees as that which maximizes balance between the treatment groups being considered. Because the selected number of trees may lead to highly variable propensity score weights, we examine alternative ways to tune the number of trees used in the estimation of propensity score weights such that we sacrifice some balance on the pre-treatment covariates in exchange for less variable weights. We use simulation studies to illustrate these methods and to describe the potential advantages and disadvantages of each method. We apply these methods to two case studies: one examining the effect of dog ownership on the owner's general health using data from a large, population-based survey in California, and a second investigating the relationship between abstinence and a long-term economic outcome among a sample of high-risk youth. [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]

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]

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]

Provider profiling as a means to describe and compare the performance of health care professionals has gained momentum in the past decade. As a key component of pay-for-performance programs profiling has been increasingly used to identify top-performing providers. However, rigorous examination of the performance of statistical methods for profiling when used to classify top-performing providers is lacking. The objective of this study was to compare the classification accuracy of three methods for identifying providers exceeding performance thresholds and to analyze data on satisfaction with mental health care providers at Group Health Cooperative using these methods. Questionnaire data on patient satisfaction with mental health care providers at Group Health Cooperative was collected between April 2008 and January 2010. Simulated data were used to compare the classification accuracy of alternative statistical methods. We evaluated sensitivity, specificity, and root mean squared error of alternative statistical methods using simulated data. For Group Health providers, we compared agreement of alternative approaches to classification. We found that when between-provider variability in performance was low, all three methods exhibited poor classification accuracy. When used to evaluate mental health care provider performance, we found substantial uncertainty in the estimates and poor agreement across methods. Based on these findings, we recommend providing uncertainty estimates for provider rankings and caution against the use of any classification method when between-provider variability is low. [ABSTRACT FROM AUTHOR]

We examined the relationship between the proportion of time spent in the mammography exam room relative to the total visit time and the rate of repeat visits across mammography facilities. We hypothesized that the means for these proportions would be greater among facilities with high repeat rates compared to those with low repeat rates. The beta distribution was used to model the proportions. However, not all of the observations were observed as some were (right- or interval-) censored due to the inability to observe complete information (e.g., time of arrival at the facility) to calculate the proportions for some observations. Using a maximum likelihood approach, we estimated the mean proportion of time spent during appointments on the actual exams. To analyze these data we utilized survival analysis methods, which typically are applied to data on time scales, to analyze these data restricted to the range (0, 1). We found that facilities with high repeat mammography rates on average had a greater proportion of their visits spent receiving the exams than the low repeat rate facilities ( p = 0.0003). Women from low repeat rate facilities spent on average 45 % of their appointment time engaging in the exam; thus, these women averaged more time waiting for the exam to begin than receiving the exam. Conversely, those from high repeat rate facilities averaged more of their appointment time engaging in the mammographic exams. These findings were consistent with our hypothesis that women would have greater satisfaction with their mammography appointments-and thus be more likely to return for routine screening-in facilities where their appointments were more efficient. [ABSTRACT FROM AUTHOR]

Bed days is a potentially useful metric of efficiency in clinical studies involving the hospital admission decision. However, this metric involves excess zeros, possible overdispersion, and possible clustering (in multi-site studies). A random effects negative binomial hurdle model can account for each of these issues. We extend this model to include site-level correlation between the two component parts and implement best linear unbiased prediction-type estimation with restricted maximum quasi-likelihood. This approach offers computational advantages over maximum likelihood in a generalized linear mixed model setting. Simulations show that the proposed approach performs well for fixed effects and variance components under a plausible range of bivariate correlation. The Emergency Department Community Acquired Pneumonia study motivates this work and illustrates the methods. [ABSTRACT FROM AUTHOR]

Estimating the causal effect of an active ingredient in a randomized treatment may be complicated when participants receive heterogeneous doses of the active ingredient. For example, homeless veterans with mental illness were randomized to receive either case management only, or case management plus special access to a Section 8 housing voucher. However, a quarter of those randomized to the housing voucher group did not actually use the voucher, while a few of those randomized to case management actually used a housing voucher. Our goal is to estimate the causal effect of the proposed active ingredient (i.e., using a housing voucher), as opposed to the effect of being randomized to the active ingredient. The proposed instrumental variable methodology assumes that the outcome is proportional to the dose of the active ingredient and tests the significance of the instrumental variable estimate using a permutation test such as the Hodges-Lehman aligned rank statistic proposed by Rosenbaum for pair-matched data (Rosenbaum, Observational studies, ; The design of observational studies, ). This method removes unmeasured confounding when the assumptions of monotonicity and the exclusion restrictions hold; however, in practice these assumptions may not hold perfectly. Consequently, we extend and strengthen this approach by incorporating optimal full matching on the propensity to use the active ingredient, to further reduce bias and variability in the IV estimation. [ABSTRACT FROM AUTHOR]

Community colleges have increased post-secondary educational access for disadvantaged youth, but it is unknown how community college degrees fit into the educational gradient of health status disparities. Using data from high school graduates in the National Longitudinal Study of Adolescent Health, we compared young adults ages 26-31 whose highest degrees were high school diplomas ( n = 5,584), sub-baccalaureate credentials (sub-BAs include community college certificates and associate's degrees) ( n = 2,415), and baccalaureate degrees (BAs) ( n = 3,303) on measures of hypertension, obesity, smoking, sleep problems, dyslipidemia, and depression. Comparisons used multivariate Poisson regression with robust standard errors after exact and nearest-neighbor Mahalanobis matching within propensity score calipers on 23 baseline factors measured in 1995. High school graduates and sub-BAs differed significantly on 3 of 23 baseline factors. After matching, sub-BAs were 16 % less likely to smoke daily than if they had only a high school diploma but did not differ in other health status measures. Sub-BAs and BAs differed significantly on 14 of 23 baseline factors. After matching, BAs were 60 % less likely to smoke daily, 14 % less likely to be obese, and 38 % less likely to have been diagnosed with depression. Sub-BA degrees are accessible to high school graduates irrespective of academic backgrounds and predict lower smoking prevalence. BAs are less accessible to high school graduates and predict lower chances of smoking, depression, and obesity. [ABSTRACT FROM AUTHOR]

As the amount of data generated continues to increase, consideration of individuals' privacy is a growing concern. As a result, there has been a vast quantity of research done on methods of statistical disclosure control. Some of these methods propose to release a randomized version of the data rather than the actual data. While methods of this type certainly offer some layer of protection, there is still the potential for private information to be disclosed. Quantifying the level of privacy provided by these methods is often difficult. In the past, a method for assessing privacy using the receiver operating characteristic (ROC) curve based on ideas related to differential privacy was proposed. However, the method was only demonstrated for univariate randomized releases. Here, the ROC-based privacy measure is extended to the release of randomized vectors. [ABSTRACT FROM AUTHOR]

Joint modeling of longitudinal and survival data can provide more efficient and less biased estimates of treatment effects through accounting for the associations between these two data types. Sponsors of oncology clinical trials routinely and increasingly include patient-reported outcome (PRO) instruments to evaluate the effect of treatment on symptoms, functioning, and quality of life. Known publications of these trials typically do not include jointly modeled analyses and results. We formulated several joint models based on a latent growth model for longitudinal PRO data and a Cox proportional hazards model for survival data. The longitudinal and survival components were linked through either a latent growth trajectory or shared random effects. We applied these models to data from a randomized phase III oncology clinical trial in mesothelioma. We compared the results derived under different model specifications and showed that the use of joint modeling may result in improved estimates of the overall treatment effect. [ABSTRACT FROM AUTHOR]

We develop a new tool for assessing the sensitivity of findings on treatment effectiveness to differential follow-up rates in the two treatment conditions being compared. The method censors the group with the higher response rate to create a synthetic respondent group that is then compared with the observed cases in the other condition to estimate a treatment effect. Censoring is done under various assumptions about the strength of the relationship between follow-up and outcomes to determine how informative differential dropout can alter inferences relative to estimates from models that assume the data are Missing at Random. The method provides an intuitive measure for understanding the strength of the association between outcomes and dropout that would be required to alter inferences about treatment effects. Our approach is motivated by translational research in which treatments found to be effective under experimental conditions are tested in standard treatment settings. In such applications, follow-up rates in the experimental setting are likely to be substantially higher than in the standard setting, especially when observational data are used in the evaluation. We test the method on a case study evaluation of the effectiveness of an evidence-supported adolescent substance abuse treatment program (Motivational Enhancement Therapy/Cognitive Behavioral Therapy-5) delivered by community-based treatment providers relative to its performance in a controlled research trial. In this case study, follow-up rates in the community-based settings were extremely low (54 %) compared to the experimental setting (95 %) giving raise to concerns about non-ignorable drop-out. [ABSTRACT FROM AUTHOR]