A Randomized Trial of Choice Architecture and Mailed Colorectal Cancer Screening Outreach in a Community Health Setting.

BACKGROUND & AIMS: Mailed outreach for colorectal cancer (CRC) screening increases uptake but it is unclear how to offer the choice of testing. We evaluated if the active choice between colonoscopy and fecal immunochemical test (FIT), or FIT alone, increased response compared with colonoscopy alone. METHODS: This pragmatic, randomized, controlled trial at a community health center included patients between ages 50 and 74 who were not up to date with CRC screening. Patients were randomized 1:1:1 to the following: (1) colonoscopy only, (2) active choice of colonoscopy or FIT, or (3) FIT only. Patients received an outreach letter with instructions for testing (colonoscopy referral and/or an enclosed FIT kit), a reminder letter at 2 months, and another reminder at 3 to 5 months via text message or automated voice recording. The primary outcome was CRC screening completion within 6 months. RESULTS: Among 738 patients in the final analysis, the mean age was 58.7 years (SD, 6.2 y); 48.6% were insured by Medicaid and 24.3% were insured by Medicare; and 71.7% were White, 16.9% were Black, and 7.3% were Hispanic/Latino. At 6 months, 5.6% (95% CI, 2.8-8.5) completed screening in the colonoscopy-only arm, 12.8% (95% CI, 8.6-17.0) in the active-choice arm, and 11.3% (95% CI, 7.4-15.3) in the FIT-only arm. Compared with colonoscopy only, there was a significant increase in screening in active choice (absolute difference, 7.1%; 95% CI, 2.0-12.2; P = .006) and FIT only (absolute difference, 5.7%; 95% CI, 0.8-10.6; P = .02). CONCLUSIONS: Both choice of testing and FIT alone increased response and may align with patient preferences. TRIAL REGISTRATION: clinicaltrials.gov NCT04711473.

Three-phase generalized raking and multiple imputation estimators to address error-prone data.

Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-prone data on all subjects to improve estimation. In practice, more than one round of data validation may be required, and direct application of standard approaches for combining validation data into analyses may lead to inefficient estimators since the information available from intermediate validation steps is only partially considered or even completely ignored. In this paper, we present two novel extensions of multiple imputation and generalized raking estimators that make full use of all available data. We show through simulations that incorporating information from intermediate steps can lead to substantial gains in efficiency. This work is motivated by and illustrated in a study of contraceptive effectiveness among 83 671 women living with HIV, whose data were originally extracted from electronic medical records, of whom 4732 had their charts reviewed, and a subsequent 1210 also had a telephone interview to validate key study variables.

Associations Between Gestational Weight Gain, Gestational Diabetes, and Childhood Obesity Incidence.

INTRODUCTION: Excessive maternal gestational weight gain (GWG) is strongly correlated with childhood obesity, yet how excess maternal weight gain and gestational diabetes mellitus (GDM) interact to affect early childhood obesity is poorly understood. The purpose of this study was to investigate whether overall and trimester-specific maternal GWG and GDM were associated with obesity in offspring by age 6 years. METHODS: A cohort of 10,335 maternal-child dyads was established from electronic health records. Maternal weights at conception and delivery were estimated from weight trajectory fits using functional principal components analysis. Kaplan-Meier curves and Cox regression, together with generalized raking, examined time-to-childhood-obesity. RESULTS: Obesity diagnosed prior to age 6 years was estimated at 19.7% (95% CI: 18.3, 21.1). Maternal weight gain during pregnancy was a strong predictor of early childhood obesity (p < 0.0001). The occurrence of early childhood obesity was lower among mothers with GDM compared with those without diabetes (adjusted hazard ratio = 0.58, p = 0.014). There was no interaction between maternal weight gain and GDM (p = 0.55). Higher weight gain during the first trimester was associated with lower risk of early childhood obesity (p = 0.0002) whereas higher weight gain during the second and third trimesters was associated with higher risk (p < 0.0001). DISCUSSION: Results indicated total and trimester-specific maternal weight gain was a strong predictor of early childhood obesity, though obesity risk by age 6 was lower for children of mothers with GDM. Additional research is needed to elucidate underlying mechanisms directly related to trimester-specific weight gain and GDM that impede or protect against obesity prevalence during early childhood.

Excessive maternal gestational weight gain (GWG) and gestational diabetes mellitus (GDM) have been linked to childhood obesity. Yet, research on how excessive total and trimester-specific GWG and GDM interact to affect early childhood obesity remains inconclusive. This study found that inadequate weight gain in the first trimester and excessive weight gain in the second and third trimester were associated with higher risks of childhood obesity by age 6. No significant interaction between maternal GWG and GDM was noted suggesting that these two important maternal conditions do not have a combined effect on the risk of early childhood obesity.

Practical considerations for sandwich variance estimation in two-stage regression settings.

We present a practical approach for computing the sandwich variance estimator in two-stage regression model settings. As a motivating example for two-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has been rarely applied in regression calibration, despite it requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is likely due to requiring specialized statistical coding. We first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich to a resampling variance approach for both settings. Finally, we further compare these two variance estimation approaches for data examples from the Women's Health Initiative (WHI) and Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or over-covered in certain settings, particularly when there was high correlation between covariates or large measurement error.

Nutritional Blood Concentration Biomarkers in the Hispanic Community Health Study/Study of Latinos: Measurement Characteristics and Power.

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008-2011), self-reported information on diet was collected from all participants (n = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014-2017) and third (2020-2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010-2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration-based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes.

Issues in Implementing Regression Calibration Analyses.

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

Carbon Isotope Ratios of Plasma and RBC Fatty Acids Identify Meat Consumers in a 12-Week Inpatient Feeding Study of 32 Men.

BACKGROUND: Molecular stable isotope ratios are a novel type of dietary biomarker with high sensitivity and specificity for certain foods. Among these, fatty acid carbon isotope ratios (CIRs) have strong potential but have not been investigated as dietary biomarkers. OBJECTIVES: We evaluated whether fatty acid CIRs and mass proportions were associated with meat, fish, and sugar-sweetened beverage (SSB) intake. METHODS: Thirty-two men [aged 46.2 ± 10.5 y; BMI (kg/m2): 27.2 ± 4.0] underwent a 12-wk inpatient dietary intervention at the National Institute of Diabetes and Digestive and Kidney Diseases in Phoenix, Arizona. Men were randomly assigned to 1 of 8 dietary treatments varying the presence/absence of dietary meat, fish, and SSBs in all combinations. Fatty acid CIRs and mass proportions were measured in fasting blood samples and adipose tissue biopsies that were collected pre- and postintervention. Dietary effects were analyzed using multivariable regression and receiver operating characteristic AUCs were calculated using logistic regression. RESULTS: CIRs of the several abundant SFAs, MUFAs and arachidonic acid (20:4n-6) in plasma were strongly associated with meat, as were a subset of these fatty acids in RBCs. Effect sizes in plasma ranged from 1.01‰ to 1.93‰ and were similar but attenuated in RBCs. Mass proportions of those fatty acids were not associated with diet. CIRs of plasma dihomo-γ-linolenic acid (20:3n-6) and adipose palmitic acid (16:0) were weakly associated with SSBs. Mass proportions of plasma odd-chain fatty acids were associated with meat, and mass proportions of plasma EPA and DHA (20:5n-3 and 22:6n-3) were associated with fish. CONCLUSIONS: CIRs of plasma and RBC fatty acids show promise as sensitive and specific measures of dietary meat. These provide different information from that provided by fatty acid mass proportions, and are informative where mass proportion is not. This trial is registered at www.clinicaltrials.gov as NCT01237093.

Multiwave validation sampling for error-prone electronic health records.

Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR data with confidence, but limited resources typically make complete data validation impossible. Using EHR data, we illustrate prospective, multiwave, two-phase validation sampling to estimate the association between maternal weight gain during pregnancy and the risks of her child developing obesity or asthma. The optimal validation sampling design depends on the unknown efficient influence functions of regression coefficients of interest. In the first wave of our multiwave validation design, we estimate the influence function using the unvalidated (phase 1) data to determine our validation sample; then in subsequent waves, we re-estimate the influence function using validated (phase 2) data and update our sampling. For efficiency, estimation combines obesity and asthma sampling frames while calibrating sampling weights using generalized raking. We validated 996 of 10,335 mother-child EHR dyads in six sampling waves. Estimated associations between childhood obesity/asthma and maternal weight gain, as well as other covariates, are compared to naïve estimates that only use unvalidated data. In some cases, estimates markedly differ, underscoring the importance of efficient validation sampling to obtain accurate estimates incorporating validated data.

A Randomized Controlled Pilot Study of Just4Us, a Counseling and Navigation Intervention to Promote Oral HIV Prophylaxis Uptake Among PrEP-Eligible Cisgender Women.

HIV pre-exposure prophylaxis (PrEP) uptake among cisgender women in the United States is low. Just4Us, a theory-based counseling and navigation intervention, was evaluated in a pilot randomized controlled trial among PrEP-eligible women (n = 83). The comparison arm was a brief information session. Women completed surveys at baseline, post-intervention, and at three months. In this sample, 79% were Black, and 26% were Latina. This report presents results on preliminary efficacy. At 3 months follow-up, 45% made an appointment to see a provider about PrEP; only 13% received a PrEP prescription. There were no differences in PrEP initiation by study arm (9% Info vs. 11% Just4Us). PrEP knowledge was significantly higher in the Just4Us group at post-intervention. Analysis revealed high PrEP interest with many personal and structural barriers along the PrEP continuum. Just4Us is a promising PrEP uptake intervention for cisgender women. Further research is needed to tailor intervention strategies to multilevel barriers.Clinicaltrials.gov registration NCT03699722: A Women-Focused PrEP Intervention (Just4Us).

RESUMEN: La aceptación de la profilaxis previa a la exposición (PrEP) al VIH entre las mujeres cisgénero en los Estados Unidos es baja. Just4Us, una intervención de asesoramiento y navegación basada en la teoría, se evaluó en un ensayo piloto controlado aleatorizado con mujeres aptas para la PrEP (n = 83). El brazo de comparación fue una breve sesión de información. Las mujeres completaron encuestas al inicio, después de la intervención ya los 3 meses. En la muestra, el 79% eran negros y el 26% eran latinas. Este informe presenta resultados sobre la eficacia preliminar. A los 3 meses de seguimiento, el 45% hizo una cita para ver a un proveedor acerca de la PrEP; solo el 13% recibió una receta de PrEP. No hubo diferencias en el inicio de la PrEP por brazo de estudio (9% Info frente a 11% Just4Us). El conocimiento fue significativamente mayor en el grupo Just4Us después de la intervención. El análisis reveló un alto interés por la PrEP con muchas barreras personales y estructurales a lo largo del continuo de la PrEP. Just4Us es una prometedora intervención de adopción de PrEP para mujeres cisgénero. Se necesita más investigación para adaptar las estrategias de intervención a las barreras multinivel.

Amino Acid Nitrogen Isotope Ratios Respond to Fish and Meat Intake in a 12-Week Inpatient Feeding Study of Men.

BACKGROUND: The natural abundance nitrogen stable isotope ratio (NIR) of whole tissue correlates with animal protein intakes, including meat and fish. Amino acid (AA) NIRs (NIRAAs) are more variable than the whole-tissue NIRs and may thus better differentiate among foods. OBJECTIVES: We evaluated whether NIRAAs were associated with intakes of fish and meat and whether these dietary associations were larger than with whole-tissue NIRs. METHODS: Men were recruited at the National Institute of Diabetes and Digestive and Kidney Diseases in Phoenix, Arizona, and randomly assigned to one of eight 12-wk inpatient dietary interventions, which varied the presence/absence of fish, meat, and sugar-sweetened beverages (SSBs) in all possible combinations. Fasting blood was drawn pre- and postintervention and plasma and RBC NIRAAs (free and protein-bound) were measured as secondary outcomes in 32 participants. Multivariable regression was used to determine responses of postintervention NIRAAs to dietary variables, and logistic regression was used to calculate receiver operating characteristic AUCs. RESULTS: Most plasma NIRAAs increased with fish and meat intakes, but to a greater extent with fish intake. The largest increase in response to fish intake was plasma NIRLeucine (ß = 2.19, SE = 0.26). The NIRThreonine decreased with both fish and meat intakes. Fewer RBC NIRAAs increased with fish intake, and only RBC NIRProline increased with meat intake. No plasma or RBC NIRAA responded to SSB intake. We identified fish intake with a high degree of accuracy using plasma NIRLeucine (corrected AUC, cAUC = 0.96) and NIRGlutamic acid/glutamine (cAUC = 0.93), and meat intake to a lower degree using plasma NIRProline (cAUC = 0.80) and RBC NIRProline (cAUC = 0.85). CONCLUSIONS: Plasma and RBC NIRAAs were associated with fish and meat intakes but were not superior to whole-tissue stable isotope biomarkers in identifying these intakes in a US diet. This trial is registered at www.clinicaltrials.gov as NCT01237093.

Efficient odds ratio estimation under two-phase sampling using error-prone data from a multi-national HIV research cohort.

Persons living with HIV engage in routine clinical care, generating large amounts of data in observational HIV cohorts. These data are often error-prone, and directly using them in biomedical research could bias estimation and give misleading results. A cost-effective solution is the two-phase design, under which the error-prone variables are observed for all patients during Phase I, and that information is used to select patients for data auditing during Phase II. For example, the Caribbean, Central, and South America network for HIV epidemiology (CCASAnet) selected a random sample from each site for data auditing. Herein, we consider efficient odds ratio estimation with partially audited, error-prone data. We propose a semiparametric approach that uses all information from both phases and accommodates a number of error mechanisms. We allow both the outcome and covariates to be error-prone and these errors to be correlated, and selection of the Phase II sample can depend on Phase I data in an arbitrary manner. We devise a computationally efficient, numerically stable EM algorithm to obtain estimators that are consistent, asymptotically normal, and asymptotically efficient. We demonstrate the advantages of the proposed methods over existing ones through extensive simulations. Finally, we provide applications to the CCASAnet cohort.

On the Use of Regression Calibration in a Complex Sampling Design With Application to the Hispanic Community Health Study/Study of Latinos.

Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet its application in the context of a complex sampling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose 2 variance estimators for a multistage probability-based sampling design, a parametric and a resampling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) data from 2008 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subsample of the HCHS/SOL cohort. We have provided guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these 2 variance estimators in the setting of a large multicenter study are also discussed. Code to replicate the presented results is available online.

Combining multiple imputation with raking of weights: An efficient and robust approach in the setting of nearly true models.

Multiple imputation (MI) provides us with efficient estimators in model-based methods for handling missing data under the true model. It is also well-understood that design-based estimators are robust methods that do not require accurately modeling the missing data; however, they can be inefficient. In any applied setting, it is difficult to know whether a missing data model may be good enough to win the bias-efficiency trade-off. Raking of weights is one approach that relies on constructing an auxiliary variable from data observed on the full cohort, which is then used to adjust the weights for the usual Horvitz-Thompson estimator. Computing the optimally efficient raking estimator requires evaluating the expectation of the efficient score given the full cohort data, which is generally infeasible. We demonstrate MI as a practical method to compute a raking estimator that will be optimal. We compare this estimator to common parametric and semi-parametric estimators, including standard MI. We show that while estimators, such as the semi-parametric maximum likelihood and MI estimator, obtain optimal performance under the true model, the proposed raking estimator utilizing MI maintains a better robustness-efficiency trade-off even under mild model misspecification. We also show that the standard raking estimator, without MI, is often competitive with the optimal raking estimator. We demonstrate these properties through several numerical examples and provide a theoretical discussion of conditions for asymptotically superior relative efficiency of the proposed raking estimator.

Regression calibration to correct correlated errors in outcome and exposure.

Measurement error arises through a variety of mechanisms. A rich literature exists on the bias introduced by covariate measurement error and on methods of analysis to address this bias. By comparison, less attention has been given to errors in outcome assessment and nonclassical covariate measurement error. We consider an extension of the regression calibration method to settings with errors in a continuous outcome, where the errors may be correlated with prognostic covariates or with covariate measurement error. This method adjusts for the measurement error in the data and can be applied with either a validation subset, on which the true data are also observed (eg, a study audit), or a reliability subset, where a second observation of error prone measurements are available. For each case, we provide conditions under which the proposed method is identifiable and leads to consistent estimates of the regression parameter. When the second measurement on the reliability subset has no error or classical unbiased measurement error, the proposed method is consistent even when the primary outcome and exposures of interest are subject to both systematic and random error. We examine the performance of the method with simulations for a variety of measurement error scenarios and sizes of the reliability subset. We illustrate the method's application using data from the Women's Health Initiative Dietary Modification Trial.

An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures.

Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self-reported outcomes are typical in cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self-report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure-disease associations that ultimately can mislead clinical decision-making. We have extended an existing semiparametric likelihood-based method for handling error-prone, discrete failure time outcomes to also address covariate error. We conduct an extensive numerical study to compare the proposed method to the naive approach that ignores measurement error in terms of bias and efficiency in the estimation of the regression parameter of interest. In all settings considered, the proposed method showed minimal bias and maintained coverage probability, thus outperforming the naive analysis which showed extreme bias and low coverage. This method is applied to data from the Women's Health Initiative to assess the association between energy and protein intake and the risk of incident diabetes mellitus. Our results show that correcting for errors in both the self-reported outcome and dietary exposures leads to considerably different hazard ratio estimates than those from analyses that ignore measurement error, which demonstrates the importance of correcting for both outcome and covariate error.

Raking and regression calibration: Methods to address bias from correlated covariate and time-to-event error.

Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study. These data errors, if not accounted for in study analyses, can obscure or cause spurious associations between patient exposures and disease risk. Methodology to address covariate measurement error has been well developed; however, time-to-event error has also been shown to cause significant bias, but methods to address it are relatively underdeveloped. More generally, it is possible to observe errors in both the covariate and the time-to-event outcome that are correlated. We propose regression calibration (RC) estimators to simultaneously address correlated error in the covariates and the censored event time. Although RC can perform well in many settings with covariate measurement error, it is biased for nonlinear regression models, such as the Cox model. Thus, we additionally propose raking estimators which are consistent estimators of the parameter defined by the population estimating equation. Raking can improve upon RC in certain settings with failure-time data, require no explicit modeling of the error structure, and can be utilized under outcome-dependent sampling designs. We discuss features of the underlying estimation problem that affect the degree of improvement the raking estimator has over the RC approach. Detailed simulation studies are presented to examine the performance of the proposed estimators under varying levels of signal, error, and censoring. The methodology is illustrated on observational EHR data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.

Efficient semiparametric inference for two-phase studies with outcome and covariate measurement errors.

In modern observational studies using electronic health records or other routinely collected data, both the outcome and covariates of interest can be error-prone and their errors often correlated. A cost-effective solution is the two-phase design, under which the error-prone outcome and covariates are observed for all subjects during the first phase and that information is used to select a validation subsample for accurate measurements of these variables in the second phase. Previous research on two-phase measurement error problems largely focused on scenarios where there are errors in covariates only or the validation sample is a simple random sample of study subjects. Herein, we propose a semiparametric approach to general two-phase measurement error problems with a quantitative outcome, allowing for correlated errors in the outcome and covariates and arbitrary second-phase selection. We devise a computationally efficient and numerically stable expectation-maximization algorithm to maximize the nonparametric likelihood function. The resulting estimators possess desired statistical properties. We demonstrate the superiority of the proposed methods over existing approaches through extensive simulation studies, and we illustrate their use in an observational HIV study.

Persistent Disparities in Smoking Rates Among PLWH Compared to the General Population in Philadelphia, 2009-2014.

Despite reductions in smoking rates in the general population, little is known about recent smoking trends among people living with HIV (PLWH). We compared the risk for smoking and temporal trends in smoking among PLWH and the general population in the Philadelphia metropolitan area between 2009 and 2014. We used weighted logistic regression to assess the relation between HIV and smoking, and examined temporal smoking trends. The adjusted odds ratio (OR) for smoking comparing PLWH to the general population was 1.80 (95% CI 1.55-2.09) after adjusting for socio-economic, demographic, and mental health diagnosis variables. Smoking prevalence decreased in both the PLWH and general populations during the study period, and we did not observe a significant difference in rates of decline between groups (P = 0.54). Despite overall progress in smoking cessation, a disparity persisted in smoking rates between PLWH and the general population, with and without adjustment for socio-economic, demographic, and mental health variables. Further research is needed to understand the mechanisms linking HIV and tobacco use in order to inform public health efforts to reduce smoking among PLWH.

Improved generalized raking estimators to address dependent covariate and failure-time outcome error.

Biomedical studies that use electronic health records (EHR) data for inference are often subject to bias due to measurement error. The measurement error present in EHR data is typically complex, consisting of errors of unknown functional form in covariates and the outcome, which can be dependent. To address the bias resulting from such errors, generalized raking has recently been proposed as a robust method that yields consistent estimates without the need to model the error structure. We provide rationale for why these previously proposed raking estimators can be expected to be inefficient in failure-time outcome settings involving misclassification of the event indicator. We propose raking estimators that utilize multiple imputation, to impute either the target variables or auxiliary variables, to improve the efficiency. We also consider outcome-dependent sampling designs and investigate their impact on the efficiency of the raking estimators, either with or without multiple imputation. We present an extensive numerical study to examine the performance of the proposed estimators across various measurement error settings. We then apply the proposed methods to our motivating setting, in which we seek to analyze HIV outcomes in an observational cohort with EHR data from the Vanderbilt Comprehensive Care Clinic.

Impact of Regression to the Mean on the Synthetic Control Method: Bias and Sensitivity Analysis.

To make informed policy recommendations from observational panel data, researchers must consider the effects of confounding and temporal variability in outcome variables. Difference-in-difference methods allow for estimation of treatment effects under the parallel trends assumption. To justify this assumption, methods for matching based on covariates, outcome levels, and outcome trends-such as the synthetic control approach-have been proposed. While these tools can reduce bias and variability in some settings, we show that certain applications can introduce regression to the mean (RTM) bias into estimates of the treatment effect. Through simulations, we show RTM bias can lead to inflated type I error rates and bias toward the null in typical policy evaluation settings. We develop a novel correction for RTM bias that allows for valid inference and show how this correction can be used in a sensitivity analysis. We apply our proposed sensitivity analysis to reanalyze data concerning the effects of California's Proposition 99, a large-scale tobacco control program, on statewide smoking rates.