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Background Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale. Development This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency. Application First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations. Conclusions The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs. [ABSTRACT FROM AUTHOR]

BackgroundVarious indicators of progression of chronic kidney disease (CKD) have been used as outcomes in clinical research studies. The effect of using varying measures on the association of risk factors with CKD progression has not been well characterized.Study designProspective cohort study.Setting & participantsThe Chronic Renal Insufficiency Cohort (CRIC) Study (N=3,939) enrolled men and women with mild to moderate CKD, 48% of whom had diabetes and 42% were self-reported black race.PredictorsAge, race, sex, diabetes, baseline estimated glomerular filtration rate (eGFR), proteinuria, and other established CKD risk factors.OutcomesDeath, end-stage renal disease (ESRD), and eGFR events, including: (1) eGFR halving, (2) eGFR

BackgroundGlomerular filtration rate (GFR) is considered the best measure of kidney function, but repeated assessment is not feasible in most research studies.Study designCross-sectional study of 1,433 participants in the Chronic Renal Insufficiency Cohort (CRIC) Study (ie, the GFR subcohort) to derive an internal GFR estimating equation using a split-sample approach.Setting & participantsAdults from 7 US metropolitan areas with mild to moderate chronic kidney disease; 48% had diabetes and 37% were black.Index testCRIC GFR estimating equation.Reference test or outcomeUrinary (125)I-iothalamate clearance testing (measured GFR [mGFR]).Other measurementsLaboratory measures, including serum creatinine and cystatin C, and anthropometrics.ResultsIn the validation data set, the model that included serum creatinine level, serum cystatin C level, age, sex, and race was the most parsimonious and similarly predictive of mGFR compared with a model additionally including bioelectrical impedance analysis phase angle, CRIC clinical center, and 24-hour urinary creatinine excretion. Specifically, root mean square errors for the separate models were 0.207 versus 0.202, respectively. Performance of the CRIC GFR estimating equation was most accurate for the subgroups of younger participants, men, nonblacks, non-Hispanics, those without diabetes, those with body mass index

In randomized controlled trials, the evaluation of an overall treatment effect is often followed by effect modification or subgroup analyses, where the possibility of a different magnitude or direction of effect for varying values of a covariate is explored. While studies of effect modification are typically restricted to pretreatment covariates, longitudinal experimental designs permit the examination of treatment effect modification by intermediate outcomes, where intermediates are measured after treatment but before the final outcome. We present a novel application of generalized structural mean models (GSMMs) for simultaneously assessing effect modification by post-treatment covariates and accounting for noncompliance to assigned treatment status. The proposed approach may also be used to identify post-treatment effect modifiers in the absence of noncompliance. The methods are evaluated using a simulation study that demonstrates that our approach retains consistent estimation of effect modification by intermediate variables that are affected by treatment and also predict outcomes. We illustrate the method using a randomized trial designed to promote re-employment through teaching skills to enhance self-esteem and inoculate job seekers against setbacks in the job search process. Our analysis provides some evidence that the intervention was much less successful among subjects that displayed higher levels of depression at intermediate post-treatment waves of the study. We also compare the assumptions of our approach and principal stratification as alternatives to account for differences in effects by intermediate variables.

We address several questions relating to the use of standard regression and Structural Nested Mean Model (SNMM) approach (e. g., Ten Have et al. 2007) to analyze post-randomization effect modifiers of the intent-to-treat effect of a randomized intervention on a subsequent outcome, which has not been well examined. We show through simulations that the SNMM performs better with respect to bias of estimates of the intervention and interaction effects than does the corresponding standard interaction approach when the baseline intervention is randomized and the post-randomization factors are subject to confounding, and even when there is no association between the intervention and effect modifier. However, causal inference under the SNMM makes untestable assumptions that the causal contrasts do not vary across observed levels of the intervention and post-randomization factor. In addition, the precision of the SNMM-based estimators depends on the effect of the randomized intervention on the post-randomization factor varying across baseline covariate combinations. These issues and methods are illustrated with the application of the standard and causal methods to a randomized cognitive therapy (CT) trial, for which there is a conceptual model of negative cognitive styles or distortions impacted by CT but then in turn modifying the effect of CT on subsequent suicide ideation and social problem solving outcomes.

We conducted a case-control study of adults with bacteremic pneumococcal pneumonia to identify factors associated with macrolide resistance. Study participants were identified through population-based surveillance in a 5-county region surrounding [...]

This article addresses unique causal issues in the context of a randomized study on improving adherence to best practice guidelines by primary care physicians (PCP's) in treating their depressed patients. The study assessed an encouragement strategy to improve PCP guideline adherence. In this context, we compare two causal approaches: the conditional-compliance (CC) Bayesian latent class and the conditional-observable (CO) structural mean model methods. The CC methods estimate contrasts between randomized encouragement and no-encouragement arms [intent-to-treat (ITT) estimand] given latent PCP guideline complier classes. The CO methods estimate contrasts between PCP guideline adherence and nonadherence conditions (as-treated estimand) given observed PCP adherence status. The CC ITT estimand for patients with PCP compliers equals the CO as-treated estimand depending on assumptions. One such assumption pertains to the absence of physician defiers, who do the opposite of what they are encouraged to do in treating patients for depression. We relate these two estimands to each other in our clinical context when the no-defier assumption is not plausible. In other contexts, previous statistical literature has appropriately assumed the absence of defiers. However, indications in the behavioral literature, anecdotal evidence in the study, and results from the data analysis and simulations suggest that defiers do exist in the context of physician-based interventions in primary care. Both simulation and empirical results show that even with a small estimated proportion of defiers under Bayesian model assumptions, inference is sensitive to different assumptions about this class of PCP noncompliers. KEY WORDS: Defiers; Exclusion restriction; G-estimation; Instrumental variables; Intent-to-treat; Latent class model; Noncompliance; Structural mean model., 1. INTRODUCTION We consider several unique causal modeling issues in a randomized study of an encouragement strategy for treating depression in primary care patients. In general, encouragement strategies target the [...]

The effectiveness of a kidney transplant from a living donor can be improved if the patient has not received dialysis. In a study of 8,481 patients, those who did not receive dialysis were more likely to have a functioning transplant even years after the operation.

Mitral-valve prolapse (MVP) appears to be no more common among young stroke patients than among healthy people. MVP is a defect in the mitral valve of the heart that can permit blood to backflow during the heart beat. Earlier research showed a high prevalence of this condition among stroke patients under 46 years of age. Researchers compared the mitral valves of 213 young stroke patients and 263 healthy people. Only 2% of stroke patients had MVP, compared to 2.7% of the healthy people. MVP may have been overdiagnosed in the past, when echocardiographic diagnosis was less advanced.

Introduction: We evaluated visual acuity (VA) over 5 years in a subspecialty noninfectious uveitis population.Methods: Retrospective data from 5,530 noninfectious uveitis patients with anterior, intermediate, posterior or panuveitis were abstracted by expert reviewers. Mean VA was calculated using inverse probability of censoring weighting to account for losses to follow-up.Results: Patients were a median of 41 years old, 65% female, and 73% white. Initial mean VA was worse among panuveitis (20/84) than posterior (20/64), intermediate (20/47), and anterior (20/37) uveitides. On average, mean VA improved by 0.62, 0.51, 0.37, and 0.26 logMAR-equivalent lines over 2 years, respectively (each P < .001), then remained stable, except posterior uveitis mean VA worsened to initial levels.Conclusion: Mean VA of uveitic eyes improved and, typically, improvement was sustained under uveitis subspecialty care. Because VA tends to improve under tertiary care, mean VA change appears a better outcome for clinical studies than time-to-loss of VA. [ABSTRACT FROM AUTHOR]