Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study.

In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.

Small Area Estimation of Cancer Risk Factors and Screening Behaviors in US Counties by Combining Two Large National Health Surveys.

BACKGROUND: National health surveys, such as the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS), collect data on cancer screening and smoking-related measures in the US noninstitutionalized population. These surveys are designed to produce reliable estimates at the national and state levels. However, county-level data are often needed for cancer surveillance and related research. METHODS: To use the large sample sizes of BRFSS and the high response rates and better coverage of NHIS, we applied multilevel models that combined information from both surveys. We also used relevant sources such as census and administrative records. By using these methods, we generated estimates for several cancer risk factors and screening behaviors that are more precise than design-based estimates. RESULTS: We produced reliable, modeled estimates for 11 outcomes related to smoking and to screening for female breast cancer, cervical cancer, and colorectal cancer. The estimates were produced for 3,112 counties in the United States for the data period from 2008 through 2010. CONCLUSION: The modeled estimates corrected for potential noncoverage bias and nonresponse bias in the BRFSS and reduced the variability in NHIS estimates that is attributable to small sample size. The small area estimates produced in this study can serve as a useful resource to the cancer surveillance community.

FPCA-based method to select optimal sampling schedules that capture between-subject variability in longitudinal studies.

A critical component of longitudinal study design involves determining the sampling schedule. Criteria for optimal design often focus on accurate estimation of the mean profile, although capturing the between-subject variance of the longitudinal process is also important since variance patterns may be associated with covariates of interest or predict future outcomes. Existing design approaches have limited applicability when one wishes to optimize sampling schedules to capture between-individual variability. We propose an approach to derive optimal sampling schedules based on functional principal component analysis (FPCA), which separately characterizes the mean and the variability of longitudinal profiles and leads to a parsimonious representation of the temporal pattern of the variability. Simulation studies show that the new design approach performs equally well compared to an existing approach based on parametric mixed model (PMM) when a PMM is adequate for the data, and outperforms the PMM-based approach otherwise. We use the methods to design studies aiming to characterize daily salivary cortisol profiles and identify the optimal days within the menstrual cycle when urinary progesterone should be measured.

Longitudinal study of adolescent tobacco use and tobacco control policies in India.

BACKGROUND: This project will use a multilevel longitudinal cohort study design to assess whether changes in Community Tobacco Environmental (CTE) factors, measured as community compliance with tobacco control policies and community density of tobacco vendors and tobacco advertisements, are associated with adolescent tobacco use in urban India. India's tobacco control policies regulate secondhand smoke exposure, access to tobacco products and exposure to tobacco marketing. Research data about the association between community level compliance with tobacco control policies and youth tobacco use are largely unavailable, and are needed to inform policy enforcement, implementation and development. METHODS: The geographic scope will include Mumbai and Kolkata, India. The study protocol calls for an annual comprehensive longitudinal population-based tobacco use risk and protective factors survey in a cohort of 1820 adolescents ages 12-14 years (and their parent) from baseline (Wave 1) to 36-month follow-up (Wave 4). Geographic Information Systems data collection will be used to map tobacco vendors, tobacco advertisements, availability of e-cigarettes, COTPA defined public places, and compliance with tobacco sale, point-of-sale and smoke-free laws. Finally, we will estimate the longitudinal associations between CTE factors and adolescent tobacco use, and assess whether the associations are moderated by family level factors, and mediated by individual level factors. DISCUSSION: India experiences a high burden of disease and mortality from tobacco use. To address this burden, significant long-term prevention and control activities need to include the joint impact of policy, community and family factors on adolescent tobacco use onset. The findings from this study can be used to guide the development and implementation of future tobacco control policy designed to minimize adolescent tobacco use.

A Bayesian model for time-to-event data with informative censoring.

Randomized trials with dropouts or censored data and discrete time-to-event type outcomes are frequently analyzed using the Kaplan-Meier or product limit (PL) estimation method. However, the PL method assumes that the censoring mechanism is noninformative and when this assumption is violated, the inferences may not be valid. We propose an expanded PL method using a Bayesian framework to incorporate informative censoring mechanism and perform sensitivity analysis on estimates of the cumulative incidence curves. The expanded method uses a model, which can be viewed as a pattern mixture model, where odds for having an event during the follow-up interval $$({t}_{k-1},{t}_{k}]$$, conditional on being at risk at $${t}_{k-1}$$, differ across the patterns of missing data. The sensitivity parameters relate the odds of an event, between subjects from a missing-data pattern with the observed subjects for each interval. The large number of the sensitivity parameters is reduced by considering them as random and assumed to follow a log-normal distribution with prespecified mean and variance. Then we vary the mean and variance to explore sensitivity of inferences. The missing at random (MAR) mechanism is a special case of the expanded model, thus allowing exploration of the sensitivity to inferences as departures from the inferences under the MAR assumption. The proposed approach is applied to data from the TRial Of Preventing HYpertension.

Modeling the salivary cortisol profile in population research: the multi-ethnic study of atherosclerosis.

In many studies, it has been hypothesized that stress and its biologic consequences may contribute to disparities in rates of cardiovascular disease. However, understanding of the most appropriate statistical methods to analyze biologic markers of stress, such as salivary cortisol, remains limited. The authors explore the utility of various statistical methods in modeling daily cortisol profiles in population-based studies. They demonstrate that the proposed methods allow additional insight into the cortisol profile compared with commonly used summaries of the profiles based on raw data. For instance, one can gain insights regarding the shape of the population average curve, characterize the types of individual-level departures from the average curve, and better understand the relation between covariates and attained cortisol levels or slopes at various points of the day, in addition to drawing inferences regarding common features of the cortisol profile, such as the cortisol awakening response and the area under the curve. The authors compare the inference and interpretations drawn from these methods and use data collected as part of the Multi-Ethnic Study of Atherosclerosis to illustrate them.

On Bayesian methods of exploring qualitative interactions for targeted treatment.

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A.

A SEMIPARAMETRIC MULTIPLE IMPUTATION APPROACH TO FULLY SYNTHETIC DATA FOR COMPLEX SURVEYS.

Data synthesis is an effective statistical approach for reducing data disclosure risk. Generating fully synthetic data might minimize such risk, but its modeling and application can be difficult for data from large, complex surveys. This article extended the two-stage imputation to simultaneously impute item missing values and generate fully synthetic data. A new combining rule for making inferences using data generated in this manner was developed. Two semiparametric missing data imputation models were adapted to generate fully synthetic data for skewed continuous variable and sparse binary variable, respectively. The proposed approach was evaluated using simulated data and real longitudinal data from the Health and Retirement Study. The proposed approach was also compared with two existing synthesis approaches: (1) parametric regressions models as implemented in IVEware; and (2) nonparametric Classification and Regression Trees as implemented in synthpop package for R using real data. The results show that high data utility is maintained for a wide variety of descriptive and model-based statistics using the proposed strategy. The proposed strategy also performs better than existing methods for sophisticated analyses such as factor analysis.

Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes.

Most investigations in the social and health sciences aim to understand the directional or causal relationship between a treatment or risk factor and outcome. Given the multitude of pathways through which the treatment or risk factor may affect the outcome, there is also an interest in decomposing the effect of a treatment of risk factor into "direct" and "mediated" effects. For example, child's socioeconomic status (risk factor) may have a direct effect on the risk of death (outcome) and an effect that may be mediated through the adulthood socioeconomic status (mediator). Building on the potential outcome framework for causal inference, we develop a Bayesian approach for estimating direct and mediated effects in the context of a dichotomous mediator and dichotomous outcome, which is challenging as many parameters cannot be fully identified. We first define principal strata corresponding to the joint distribution of the observed and counterfactual values of the mediator, and define associate, dissociative, and mediated effects as functions of the differences in the mean outcome under differing treatment assignments within the principal strata. We then develop the likelihood properties and calculate nonparametric bounds of these causal effects assuming randomized treatment assignment. Because likelihood theory is not well developed for nonidentifiable parameters, we consider a Bayesian approach that allows the direct and mediated effects to be expressed in terms of the posterior distribution of the population parameters of interest. This range can be reduced by making further assumptions about the parameters that can be encoded in prior distribution assumptions. We perform sensitivity analyses by using several prior distributions that make weaker assumptions than monotonicity or the exclusion restriction. We consider an application that explores the mediating effects of adult poverty on the relationship between childhood poverty and risk of death.

A functional multiple imputation approach to incomplete longitudinal data.

In designed longitudinal studies, information from the same set of subjects are collected repeatedly over time. The longitudinal measurements are often subject to missing data which impose an analytic challenge. We propose a functional multiple imputation approach modeling longitudinal response profiles as smooth curves of time under a functional mixed effects model. We develop a Gibbs sampling algorithm to draw model parameters and imputations for missing values, using a blocking technique for an increased computational efficiency. In an illustrative example, we apply a multiple imputation analysis to data from the Panel Study of Income Dynamics and the Child Development Supplement to investigate the gradient effect of family income on children's health status. Our simulation study demonstrates that this approach performs well under varying modeling assumptions on the time trajectory functions and missingness patterns.

Type 2 diabetes mellitus and the risk of sudden cardiac arrest in the community.

The reduction of mortality from sudden cardiac arrest (SCA) in the setting of coronary heart disease (CHD) remains a major challenge, especially among patients with type 2 diabetes. Diabetes is associated with an increased risk of SCA, at least in part, from an increased presence and extent of coronary atherosclerosis (macrovascular disease). Diabetes also is associated with microvascular disease and autonomic neuropathy; and, these non-coronary atherosclerotic pathophysiologic processes also have the potential to increase the risk of SCA. In this report, we review the absolute and relative risk of SCA associated with diabetes. We summarize recent evidence that suggests that the increase in risk in patients with diabetes is not specific for SCA, as diabetes also is associated with a similar increase in risk for non-SCA CHD death and non-fatal myocardial infarction. These data are consistent with prior observations that coronary atherosclerosis is a major contributor to the increased SCA risk associated with diabetes. We also present previously published and unpublished data that demonstrates that both clinically-recognized microvascular and autonomic neuropathy also are associated with the risk of SCA among treated patients with diabetes, after taking into account prior clinically-recognized heart disease and other risk factors for SCA. We then discuss how these data might inform research and clinical efforts to prevent SCA. Although the prediction of SCA in this "high" risk population is likely to remain a challenge, as it is in other "high" risk clinical populations, we suggest that current recommendations for the prevention of SCA in the community, related to both lifestyle prescriptions and risk factor reduction, are likely to reduce mortality from SCA among patients with diabetes.

A new stopping rule for surveys.

Non-response is a problem for most surveys. In the sample design, non-response is often dealt with by setting a target response rate and inflating the sample size so that the desired number of interviews is reached. The decision to stop data collection is based largely on meeting the target response rate. A recent article by Rao, Glickman, and Glynn (RGG) suggests rules for stopping that are based on the survey data collected for the current set of respondents. Two of their rules compare estimates from fully imputed data where the imputations are based on a subset of early responders to fully imputed data where the imputations are based on the combined set of early and late responders. If these two estimates are different, then late responders are changing the estimate of interest. The present article develops a new rule for when to stop collecting data in a sample survey. The rule attempts to use complete interview data as well as covariates available on non-responders to determine when the probability that collecting additional data will change the survey estimate is sufficiently low to justify stopping data collection. The rule is compared with that of RGG using simulations and then is implemented using data from a real survey.

Improving on analyses of self-reported data in a large-scale health survey by using information from an examination-based survey.

Common data sources for assessing the health of a population of interest include large-scale surveys based on interviews that often pose questions requiring a self-report, such as, 'Has a doctor or other health professional ever told you that you have health condition of interest?' or 'What is your (height/weight)?' Answers to such questions might not always reflect the true prevalences of health conditions (for example, if a respondent misreports height/weight or does not have access to a doctor or other health professional). Such 'measurement error' in health data could affect inferences about measures of health and health disparities. Drawing on two surveys conducted by the National Center for Health Statistics, this paper describes an imputation-based strategy for using clinical information from an examination-based health survey to improve on analyses of self-reported data in a larger interview-based health survey. Models predicting clinical values from self-reported values and covariates are fitted to data from the National Health and Nutrition Examination Survey (NHANES), which asks self-report questions during an interview component and also obtains clinical measurements during a physical examination component. The fitted models are used to multiply impute clinical values for the National Health Interview Survey (NHIS), a larger survey that obtains data solely via interviews. Illustrations involving hypertension, diabetes, and obesity suggest that estimates of health measures based on the multiply imputed clinical values are different from those based on the NHIS self-reported data alone and have smaller estimated standard errors than those based solely on the NHANES clinical data. The paper discusses the relationship of the methods used in the study to two-phase/two-stage/validation sampling and estimation, along with limitations, practical considerations, and areas for future research.

State-based estimates of mammography screening rates based on information from two health surveys.

OBJECTIVES: We compared national and state-based estimates for the prevalence of mammography screening from the National Health Interview Survey (NHIS), the Behavioral Risk Factor Surveillance System (BRFSS), and a model-based approach that combines information from the two surveys. METHODS: At the state and national levels, we compared the three estimates of prevalence for two time periods (1997-1999 and 2000-2003) and the estimated difference between the periods. We included state-level covariates in the model-based approach through principal components. RESULTS: The national mammography screening prevalence estimate based on the BRFSS was substantially larger than the NHIS estimate for both time periods. This difference may have been due to nonresponse and noncoverage biases, response mode (telephone vs. in-person) differences, or other factors. However, the estimated change between the two periods was similar for the two surveys. Consistent with the model assumptions, the model-based estimates were more similar to the NHIS estimates than to the BRFSS prevalence estimates. The state-level covariates (through the principal components) were shown to be related to the mammography prevalence with the expected positive relationship for socioeconomic status and urbanicity. In addition, several principal components were significantly related to the difference between NHIS and BRFSS telephone prevalence estimates. CONCLUSIONS: Model-based estimates, based on information from the two surveys, are useful tools in representing combined information about mammography prevalence estimates from the two surveys. The model-based approach adjusts for the possible nonresponse and noncoverage biases of the telephone survey while using the large BRFSS state sample size to increase precision.

Alcohol use and cigarette smoking as risk factors for post-endoscopic retrograde cholangiopancreatography pancreatitis.

BACKGROUND & AIMS: Alcohol use and cigarette smoking are associated with various pancreatic diseases, but it is not known whether they associate with post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). We performed a retrospective case-control study to determine if these activities increase the risk of PEP. METHODS: We identified 7638 patients who had undergone ERCP in the University of Michigan Health System and applied exclusion criteria to identify 123 with PEP. We randomly selected 308 age- and sex-stratified controls (2.5-fold case sample); after applying exclusion criteria 248 remained. In a masked fashion, we collected data for alcohol use, cigarette smoking, and 5 internal control variables: suspected sphincter of Oddi dysfunction (SOD), pancreatic sphincterotomy, moderate/difficult cannulation, 2 or more pancreatic injections, and pancreatic stent placement. RESULTS: The univariate model showed an increased frequency of PEP in current drinkers (P < .001), former drinkers (P < .001), and former smokers (P < .001), as well as patients who were suspected of having SOD (P < .001), had undergone pancreatic sphincterotomy (P < .001), had a moderate/difficult cannulation (P = .001), and/or had 2 or more pancreatic injections (P = .007). The frequency of PEP was reduced in current smokers (P < .001). The multivariate model showed that the only independent significant predictors of PEP were current drinking (odds ratio [OR], 4.70; 95% confidence interval [CI], 2.60-8.50; P < .0001), former cigarette smoking (OR, 3.29; 95% CI, 1.28-8.44; P < .013), suspected SOD (OR, 3.69; 95% CI, 1.94-7.02; P < .001), and pancreatic sphincterotomy (OR, 5.91; 95% CI, 2.04-17.14; P = .001). CONCLUSIONS: Current alcohol use and potentially former cigarette smoking are new risk factors for PEP. It is important to consider these variables in designing PEP prevention trials.

Bayesian variable selection with joint modeling of categorical and survival outcomes: an application to individualizing chemotherapy treatment in advanced colorectal cancer.

Colorectal cancer is the second leading cause of cancer related deaths in the United States, with more than 130,000 new cases of colorectal cancer diagnosed each year. Clinical studies have shown that genetic alterations lead to different responses to the same treatment, despite the morphologic similarities of tumors. A molecular test prior to treatment could help in determining an optimal treatment for a patient with regard to both toxicity and efficacy. This article introduces a statistical method appropriate for predicting and comparing multiple endpoints given different treatment options and molecular profiles of an individual. A latent variable-based multivariate regression model with structured variance covariance matrix is considered here. The latent variables account for the correlated nature of multiple endpoints and accommodate the fact that some clinical endpoints are categorical variables and others are censored variables. The mixture normal hierarchical structure admits a natural variable selection rule. Inference was conducted using the posterior distribution sampling Markov chain Monte Carlo method. We analyzed the finite-sample properties of the proposed method using simulation studies. The application to the advanced colorectal cancer study revealed associations between multiple endpoints and particular biomarkers, demonstrating the potential of individualizing treatment based on genetic profiles.

Explaining The Slowdown In Medical Spending Growth Among The Elderly, 1999-2012.

We examined trends in per capita spending for Medicare beneficiaries ages sixty-five and older in the United States in the period 1999-2012 to determine why spending growth has been declining since around 2005. Decomposing spending by condition, we found that half of the spending slowdown was attributable to slower growth in spending for cardiovascular diseases. Spending growth also slowed for dementia, renal and genitourinary diseases, and aftercare for people with acute illnesses. Using estimates from the medical literature of the impact of pharmaceuticals on acute disease, we found that roughly half of the reduction in major cardiovascular events was attributable to medications controlling cardiovascular risk factors. Despite this substantial cost-saving improvement in cardiovascular health, additional opportunities remain to lower spending through disease prevention and control.

Neighborhood characteristics and hypertension.

BACKGROUND: : The goal of this study was to investigate cross-sectional associations between features of neighborhoods and hypertension and to examine the sensitivity of results to various methods of estimating neighborhood conditions. METHODS: : We used data from the Multi-Ethnic Study of Atherosclerosis on 2612 individuals 45-85 years of age. Hypertension was defined as systolic blood pressure above 140 mm Hg, diastolic pressure above 90 mm Hg, or use of antihypertensive medications. Neighborhood (census tract) conditions potentially related to hypertension (walking environment, availability of healthy foods, safety, social cohesion) were measured using information from a separate phone survey conducted in the study neighborhoods. For each neighborhood we estimated scale scores by aggregating residents' responses using simple aggregation (crude means) and empirical Bayes estimation (unconditional, conditional, and spatial). These estimates of neighborhood conditions were linked to each study participant based on the census tract of residence. Two-level binomial regression methods were used to estimate adjusted associations between neighborhood conditions and hypertension. RESULTS: : Residents of neighborhoods with better walkability, availability of healthy foods, greater safety, and more social cohesion were less likely to be hypertensive (relative prevalence [95% confidence interval] for 90th vs. 10th percentile of conditional empirical Bayes estimate = 0.75 [0.64-0.88], 0.72 [0.61-0.85], 0.74 [0.63-0.86], and 0.69 [0.57-0.83]), respectively, after adjusting for site, age, sex, income, and education. Associations were attenuated and often disappeared after additional adjustments for race/ethnicity. CONCLUSION: : Neighborhood walkability, food availability, safety, and social cohesion may be mechanisms that link neighborhoods to hypertension.

Childhood socioeconomic position, gender, adult body mass index, and incidence of type 2 diabetes mellitus over 34 years in the Alameda County Study.

OBJECTIVES: We examined the association between childhood socioeconomic position and incidence of type 2 diabetes and the effects of gender and adult body mass index (BMI). METHODS: We studied 5913 participants in the Alameda County Study from 1965 to 1999 who were diabetes free at baseline (1965). Cox proportional hazards models estimated diabetes risk associated with childhood socioeconomic position and combined childhood socioeconomic position-adult BMI categories in pooled and gender-stratified samples. Demographic confounders and potential pathway components (physical inactivity, smoking, alcohol consumption, hypertension, depression, health care access) were included as covariates. RESULTS: Low childhood socioeconomic position was associated with excess diabetes risk, especially among women. Race and body composition accounted for some of this excess risk. The association between childhood socioeconomic position and diabetes incidence differed by adult BMI category in the pooled and women-only groups. Adjustment for race and behaviors attenuated the risk attributable to low childhood socioeconomic position among the obese group only. CONCLUSIONS: Childhood socioeconomic position was a robust predictor of incident diabetes, especially among women. A cumulative risk effect was observed for both childhood socioeconomic position and adult BMI, especially among women.

Effect of Group-Administered Behavioral Treatment on Urinary Incontinence in Older Women: A Randomized Clinical Trial.

Importance: Urinary incontinence (UI) guidelines recommend behavioral interventions as first-line treatment using individualized approaches. A one-time, group-administered behavioral treatment (GBT) could enhance access to behavioral treatment. Objective: To compare the effectiveness, cost, and cost-effectiveness of GBT with no treatment for UI in older women. Design, Setting, and Participants: Multisite randomized clinical trial (the Group Learning Achieves Decreased Incidents of Lower Urinary Symptoms [GLADIOLUS] study), conducted from July 7, 2014, to December 31, 2016. The setting was outpatient practices at 3 academic medical centers. Community-dwelling women 55 years or older with UI were recruited by mail and screened for eligibility, including a score of 3 or higher on the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF), symptoms of at least 3 months' duration, and absence of medical conditions or treatments that could affect continence status. Of 2171 mail respondents, 1125 were invited for clinical screening; 463 were eligible and randomized; 398 completed the 12-month study. Interventions: The GBT group received a one-time 2-hour bladder health class, supported by written materials and an audio CD. Main Outcomes and Measures: Outcomes were measured at in-person visits (at 3 and 12 months) and by mail or telephone (at 6 and 9 months). The primary outcome was the change in the ICIQ-SF score. Secondary outcome measures assessed UI severity, quality of life, perceptions of improvement, pelvic floor muscle strength, and costs. Evaluators were masked to group assignment. Results: Participants (232 in the GBT group and 231 in the control group) were aged 55 to 91 years (mean [SD] age, 64 [7] years), and 46.2% (214 of 463) were African American. In intent-to-treat analyses, the ICIQ-SF scores for GBT were consistently lower than control across all time points but did not achieve the projected 3-point difference. At 3 months, the difference in differences was 0.96 points (95% CI, -1.51 to -0.41 points), which was statistically significant but clinically modest. The mean (SE) treatment effects at 6, 9, and 12 months were 1.36 (0.32), 2.13 (0.33), and 1.77 (0.31), respectively. Significant group differences were found at all time points in favor of GBT on all secondary outcomes except pelvic floor muscle strength. The incremental cost to achieve a treatment success was $723 at 3 months; GBT dominated at 12 months. Conclusions and Relevance: The GLADIOLUS study shows that a novel one-time GBT program is modestly effective and cost-effective for reducing UI frequency, severity, and bother and improving quality of life. Group-administered behavioral treatment is a promising first-line approach to enhancing access to noninvasive behavioral treatment for older women with UI. Trial Registration: ClinicalTrials.gov identifier: NCT02001714.