Search

OBJECTIVE: We examined longitudinal associations between emotional distress (specifically, depressive symptoms and diabetes distress) and medication adherence in Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE), a large randomized controlled trial comparing four glucose-lowering medications added to metformin in adults with relatively recent-onset type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: The Emotional Distress Substudy assessed medication adherence, depressive symptoms, and diabetes distress in 1,739 GRADE participants via self-completed questionnaires administered biannually up to 3 years. We examined baseline depressive symptoms and diabetes distress as predictors of medication adherence over 36 months. Bidirectional visit-to-visit relationships were also examined. Treatment satisfaction, beliefs about medication, diabetes care self-efficacy, and perceived control over diabetes were evaluated as mediators of longitudinal associations. RESULTS: At baseline, mean ± SD age of participants (56% of whom were White, 17% Hispanic/Latino, 18% Black, and 66% male) was 58.0 ± 10.2 years, diabetes duration 4.2 ± 2.8 years, HbA1c 7.5% ± 0.5%, and medication adherence 89.9% ± 11.1%. Higher baseline depressive symptoms and diabetes distress were independently associated with lower adherence over 36 months (P < 0.001). Higher depressive symptoms and diabetes distress at one visit predicted lower adherence at the subsequent 6-month visit (P < 0.0001) but not vice versa. Treatment assignment did not moderate relationships. Patient-reported concerns about diabetes medications mediated the largest percentage (11.9%–15.5%) of the longitudinal link between emotional distress and adherence. CONCLUSIONS: Depressive symptoms and diabetes distress both predict lower adherence to glucose-lowering medications over time among adults with T2DM. Addressing emotional distress and concerns about anticipated negative effects of taking these treatments may be important to support diabetes treatment adherence. [ABSTRACT FROM AUTHOR]

Background: We conducted exploratory analyses to identify distinct trajectories of estimated glomerular filtration rate (eGFR) and their relationship with hyperfiltration, subsequent rapid eGFR decline, and albuminuria in participants with youth-onset type 2 diabetes enrolled in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) study. Methods: Annual serum creatinine, cystatin C, urine albumin, and creatinine measurements were obtained from 377 participants followed for ≥ 10 years. Albuminuria and eGFR were calculated. Hyperfiltration peak is the greatest eGFR inflection point during follow-up. Latent class modeling was applied to identify distinct eGFR trajectories. Results: At baseline, participants' mean age was 14 years, type 2 diabetes duration was 6 months, mean HbA1c was 6%, and mean eGFR was 120 ml/min/1.73 m2. Five eGFR trajectories associated with different rates of albuminuria were identified, including a "progressive increasing eGFR" group (10%), three "stable eGFR" groups with varying starting mean eGFR, and an "eGFR steady decline" group (1%). Participants who exhibited the greatest peak eGFR also had the highest levels of elevated albuminuria at year 10. This group membership was characterized by a greater proportion of female and Hispanic participants. Conclusions: Distinct eGFR trajectories that associate with albuminuria risk were identified, with the eGFR trajectory characterized by increasing eGFR over time associating with the highest level of albuminuria. These descriptive data support the current recommendations to estimate GFR annually in young persons with type 2 diabetes and provide insight into eGFR-related factors which may contribute to predictive risk strategies for kidney disease therapies in youth with type 2 diabetes. Trial registration: ClinicalTrials.gov Identifier: NCT00081328, date registered 2002. [ABSTRACT FROM AUTHOR]

Background: Established diabetes care ("diabetes home") and regular healthcare visits are important to achieve optimal health. Nothing is known about psychosocial factors that predict healthcare usage (HCU) in young adults with youth-onset type 2 diabetes, at risk for early complications. Objective: To identify psychosocial predictors of HCU in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. Design: Longitudinal, measured at T1 (baseline) and T2 (1 year later). Logistic and linear regressions, adjusted for potential confounders, identified predictors of sub-optimal HCU (defined as no diabetes home, 0 visits for routine care, or ≥ 1 urgent care visit in prior 6 months). Participants: N = 366 TODAY2 participants with T1 and T2 data (381 consented). Mean age = 26.0 years, 67.8% female, 37.7% non-Hispanic Black, 35.8% Hispanic, 20.2% non-Hispanic white, 6.3% "other," mean HbA1c = 9.4%. Main Measures: HCU survey; reliable and valid measures of diabetes self-efficacy, depressive symptoms, anxiety symptoms, diabetes distress, beliefs about medicines, diabetes attitudes, material need insecurities, self-management support. Key Results: 25.4% had no diabetes home, 23.7% had 0 routine care visits, 46% had ≥ 1 urgent care visit (prior 6 months). Beliefs in the necessity of (adjusted odds ratio [OR] = 1.28; 95% confidence interval [CI] = 1.12, 1.46, p < 0.001), and concerns about (OR = 1.29;CI = 1.08,1.54, p = 0.004), diabetes medicines, and its negative psychosocial impacts (OR = 1.57;CI = 1.04, 2.38, p = 0.03), predicted higher odds of having a diabetes home at T2. Beliefs that medicines are harmful predicted lower odds of a diabetes home (OR = 0.56;CI = 0.37,0.85, p = 0.006). Necessity beliefs (OR = 1.2;CI = 1.06,1.36, p = 0.004), and self-management support (OR = 1.5;CI = 1.08,2.07, p = 0.01) predicted higher odds of having ≥ 1 diabetes care visit, harm beliefs predicted lower odds (OR = 0.6;CI = 0.41,0.88, p = 0.01). Conclusions: Sub-optimal healthcare usage, common in young adults with youth-onset type 2 diabetes, is predicted by beliefs about medicines, diabetes impact, and self-management support. We must address these factors to help this vulnerable group establish stable diabetes care. [ABSTRACT FROM AUTHOR]

Context: Obesity and diabetes are established risk factors for severe SARS-CoV-2 outcomes, but less is known about their impact on susceptibility to COVID-19 infection and general symptom severity. Objective: We hypothesized that those with obesity or diabetes would be more likely to self-report a positive SARS-CoV-2 test, and, among those with a positive test, have greater symptom severity and duration. Methods: Among 44 430 COVID-19 Community Research Partnership participants, we evaluated the association of self-reported and electronic health record obesity and diabetes with a self-reported positive COVID-19 test at any time. Among the 2663 participants with a self-reported positive COVID-19 test during the study, we evaluated the association of obesity and diabetes with self-report of symptom severity, duration, and hospitalization. Logistic regression models were adjusted for age, sex, race/ethnicity, socioeconomic status, and health care worker status. Results: We found a positive graded association between body mass index (BMI) category and positive COVID-19 test (overweight odds ratio [OR] 1.14 [1.05-1.25]; obesity I OR 1.29 [1.17-2.42]; obesity II OR 1.34 [1.19-1.50]; obesity III OR 1.53 [1.35-1.73]), and a similar but weaker association with COVID-19 symptoms and severity among those with a positive test. Diabetes was associated with COVID-19 infection but not symptoms after adjustment, with some evidence of an interaction between obesity and diabetes. Conclusion: While the limitations of this health system convenience sample include generalizability and selection around test seeking, the strong graded association of BMI and diabetes with self-reported COVID-19 infection suggests that obesity and diabetes may play a role in risk for symptomatic SARS-CoV-2 beyond co-occurrence with socioeconomic factors. [ABSTRACT FROM AUTHOR]

Aim: To identify psychosocial predictors of medication adherence in young adults with youth‐onset type 2 diabetes in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. Methods: Participants (mean age: 26 years) completed validated psychosocial measures. Unannounced telephone pill counts were completed at T1 (baseline) and T2 (follow‐up, approximately 1 year later) to assess adherence to oral hypoglycaemia agents (OHAs). Adherence to insulin was assessed by self‐report. Logistic and linear regressions identified factors that predicted 'low adherence' (<80% of pills/insulin) and per cent adherence, adjusted for potential confounders. Results: Of 171 participants with OHA adherence scores at T1 and T2 (65% women, 43% Hispanic and 35% non‐Hispanic Black), 65.4% were low adherent. After adjustment (including T1 adherence), concerns about diabetes medicines (adverse effects, dependence) at T1 predicted higher odds of being low adherent (categorical) at T2 (p = 0.019). Housing insecurity (p = 0.045) and reporting ≥2 need insecurities (p = 0.027) at T1 predicted lower per cent adherence (continuous) at T2. Of 157 participants with insulin adherence scores at T1 and T2 (69% women, 38% Hispanic and 38% non‐Hispanic Black), 36.3% were low adherent. After adjustment (including T1 adherence), beliefs that medicines are overused predicted higher odds of insulin low adherence at T2 (p = 0.013), and beliefs that medicines are harmful (p = 0.004) and overused (p = 0.010) predicted lower per cent insulin adherence at T2. Conclusions: Suboptimal medication adherence, common in young adults with youth‐onset type 2 diabetes, is predicted by interfering beliefs about medicines and social factors. We must address these beliefs and unmet needs to develop tailored interventions for this vulnerable group. [ABSTRACT FROM AUTHOR]

Few prospective studies have documented the seropositivity among those children infected with severe acute respiratory syndrome coronavirus 2. From 2 April 2021 to 24 June 2021, we prospectively enrolled children between the ages of 2 and 17 years at three North Carolina healthcare systems. Participants received at least four at-home serological tests detecting the presence of antibodies against, but not differentiating between, the nucleocapsid or spike antigen. A total of 1,058 participants were enrolled in the study, completing 2,709 tests between 1 May 2021 and 31 October 2021. Using multilevel regression with poststratification techniques and considering our assay sensitivity and sensitivity, we estimated that the seroprevalence of infection-induced antibodies among unvaccinated children and adolescents aged 2–17 years in North Carolina increased from 15.2% (95% credible interval, CrI 9.0–22.0) in May 2021 to 54.1% (95% CrI 46.7–61.1) by October 2021, indicating an average infection-to-reported-case ratio of 5. A rapid rise in seropositivity was most pronounced in those unvaccinated children aged 12–17 years, based on our estimates. This study underlines the utility of serial, serological testing to inform a broader understanding of the regional immune landscape and spread of infection. [ABSTRACT FROM AUTHOR]

Aims: To assess associations of psychosocial factors with medication adherence in young adults with youth‐onset type 2 diabetes in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. Methods: Participants (mean age 26 years) completed validated psychosocial measures. Adherence to oral hypoglycemia agents (OHAs) was assessed with 3‐monthly unannounced phone pill counts; insulin adherence by self‐report. Logistic and linear regressions identified factors associated with "low‐adherence" (<80% of pills/insulin) controlling for confounders. Results: Of 212 participants taking OHAs (67% female, 39% Hispanic, 36% non‐Hispanic Black), 69.8% were low‐adherent. After adjustment, beliefs that medicines are necessary was associated with lower odds of low‐adherence (p = 0.040, dichotomous). Less self‐management support (p = 0.008), no healthcare coverage (p = 0.001), ≥1 (p = 0.008)/≥2 (p = 0.045) need insecurities were associated with higher odds of low‐adherence. Factors associated with lower % adherence (continuous) were beliefs that medicines are harmful (p < 0.001)/overused (p = 0.007)/less necessary (p = 0.022), low self‐management support (p = 0.003), food insecurity (p = 0.036), no healthcare coverage (p < 0.001), ≥1 (p = 0.003)/≥2 (p = 0.018) need insecurities. Of 192 taking insulin (69% female, 36% Hispanic, 41% non‐Hispanic Black, 16% non‐Hispanic white), 37.0% were low‐adherent. Beliefs that medicines are overused (p = 0.009), that diabetes is not serious (p = 0.010), low diabetes self‐efficacy (p = 0.035), high distress (p = 0.027), low self‐management support (p = 0.001), food insecurity (p = 0.020), ≥1 (p = 0.011)/≥2 (p = 0.015) insecurities increased odds of insulin low‐adherence. Conclusion s : Poor medication adherence, common in young adults with youth‐onset type 2 diabetes, is associated with interfering beliefs, diabetes distress and social factors. We must address these factors to develop tailored interventions for this vulnerable group. [ABSTRACT FROM AUTHOR]

Introduction: Observational studies of SARS-CoV-2 vaccine effectiveness depend on accurate ascertainment of vaccination receipt, date, and product type. Self-reported vaccine data may be more readily available to and less expensive for researchers than assessing medical records. Methods: We surveyed adult participants in the COVID-19 Community Research Partnership who had an authenticated Electronic Health Record (EHR) (N = 41,484) concerning receipt of SARS-CoV-2 vaccination using a daily survey beginning in December 2020 and a supplemental survey in September–October 2021. We compared self-reported information to that available in the EHR for the following data points: vaccine brand, date of first dose, and number of doses using rates of agreement and Bland–Altman plots for visual assessment. Self-reported data was available immediately following vaccination (in the daily survey) and at a delayed interval (in a secondary supplemental survey). Results: For the date of first vaccine dose, self-reported "immediate" recall was within ±7 days of the date reported in the "delayed" survey for 87.9% of participants. Among the 19.6% of participants with evidence of vaccination in their EHR, 95% self-reported vaccination in one of the two surveys. Self-reported dates were within ±7 days of documented EHR vaccination for 97.6% of the "immediate" surveys and 92.0% of the "delayed" surveys. Self-reported vaccine product details matched those in the EHR for over 98% of participants for both "immediate" and "delayed" surveys. Conclusions: Self-reported dates and product details for COVID-19 vaccination can be a good surrogate when medical records are unavailable in large observational studies. A secondary confirmation of dates for a subset of participants with EHR data will provide internal validity. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 Community Research Partnership is a population-based longitudinal syndromic and sero-surveillance study. The study includes over 17,000 participants from six healthcare systems in North Carolina who submitted over 49,000 serology results. The purpose of this study is to use these serology data to estimate the cumulative proportion of the North Carolina population that has either been infected with SARS-CoV-2 or developed a measurable humoral response to vaccination. Methods: Adult community residents were invited to participate in the study between April 2020 and February 2021. Demographic information was collected and daily symptom screen was completed using a secure, HIPAA-compliant, online portal. A portion of participants were mailed kits containing a lateral flow assay to be used in-home to test for presence of anti-SARS-CoV-2 IgM or IgG antibodies. The cumulative proportion of participants who tested positive at least once during the study was estimated. A standard Cox proportional hazards model was constructed to illustrate the probability of seroconversion over time up to December 20, 2020 (before vaccines available). A separate analysis was performed to describe the influence of vaccines through February 15, 2021. Results: 17,688 participants contributed at least one serology result. 68.7% of the population were female, and 72.2% were between 18 and 59 years of age. The average number of serology results submitted per participant was 3.0 (±1.9). By December 20, 2020, the overall probability of seropositivity in the CCRP population was 32.6%. By February 15, 2021 the probability among healthcare workers and non-healthcare workers was 83% and 49%, respectively. An inflection upward in the probability of seropositivity was demonstrated around the end of December, suggesting an influence of vaccinations, especially for healthcare workers. Among healthcare workers, those in the oldest age category (60+ years) were 38% less likely to have seroconverted by February 15, 2021. Conclusions: Results of this study suggest more North Carolina residents may have been infected with SARS-CoV-2 than the number of documented cases as determined by positive RNA or antigen tests. The influence of vaccinations on seropositivity among North Carolina residents is also demonstrated. Additional research is needed to fully characterize the impact of seropositivity on immunity and the ultimate course of the pandemic. [ABSTRACT FROM AUTHOR]

Objective: To assess the prevalence of high diabetes distress and associated factors in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY2) study cohort of young adults with youth-onset type 2 diabetes.Research Design and Methods: Participants completed the Diabetes Distress Scale (DDS) at end-of-study visits. Factors examined for association with high distress were demographic (sex, race/ethnicity, age, education, income), medical (HbA1c, BMI, complications), psychological (depressive and anxiety symptoms), and social (number in household, offspring, health care coverage, established with diabetes care provider). Univariate logistic regression identified factors associated with high distress that were controlled for in multivariate logistic regressions.Results: Of 438 participants, 66% were female (mean age 26.8 years, 18% non-Hispanic White, 37% non-Hispanic Black, 38% Hispanic). High distress (DDS ≥2) was reported by 105 (24%) participants. Subscales identified 40% with high regimen distress and 29.7% with high emotional burden. A greater percentage of those with high distress were female (P = 0.002), diagnosed with hypertension (P = 0.037) and retinopathy (P = 0.005), treated with insulin, had higher HbA1c, and had moderate to severe depressive and anxiety symptoms (all P < 0.001). In multivariate analyses, female sex (P < 0.001), HbA1c (P < 0.001), anxiety symptoms (P = 0.036), and lack of health care coverage (P = 0.019) were associated with high distress, after controlling for potential confounders. Moderate to severe depressive symptoms were associated with high regimen distress (P = 0.018) and emotional burden (P < 0.001); insulin treatment was associated with high emotional burden (P = 0.027).Conclusions: Future research should identify modifiable factors associated with high diabetes distress in young adults with youth-onset type 2 diabetes that may inform distress interventions with this medically vulnerable group. [ABSTRACT FROM AUTHOR]

Background: Estimating population prevalence and incidence of prior SARS-CoV-2 infection is essential to formulate public health recommendations concerning the COVID-19 pandemic. However, interpreting estimates based on sero-surveillance requires an understanding of the duration of elevated antibodies following SARS-CoV-2 infection, especially in the large number of people with pauci-symptomatic or asymptomatic disease.Methods: We examined > 30,000 serology assays for SARS-CoV-2 specific IgG and IgM assays acquired longitudinally in 11,468 adults between April and November 2020 in the COVID-19 Community Research Partnership.Results: Among participants with serologic evidence for infection but few or no symptoms or clinical disease, roughly 50% sero-reverted in 30 days of their initial positive test. Sero-reversion occurred more quickly for IgM than IgG and for antibodies targeting nucleocapsid protein compared with spike proteins, but was not associated with age, sex, race/ethnicity, or healthcare worker status.Conclusions: The short duration of antibody response suggests that the true population prevalence of prior SARS-CoV-2 infection may be significantly higher than presumed based on earlier sero-surveillance studies. The impact of the large number of minimally symptomatic COVID-19 cases with only a brief antibody response on population immunity remains to be determined. [ABSTRACT FROM AUTHOR]

Background: Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.Methods: We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.Results: Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.Conclusions: The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators. [ABSTRACT FROM AUTHOR]

For the analysis of clinical trials, the study participants are usually assumed to be representative sample of a target population. This assumption is rarely fulfilled in clinical trials, and particularly not if the sample size is small. In addition, covariate imbalances may affect the trial. Randomization tests provide a nonparametric analysis method of the treatment effect that does not rely on population‐based assumptions. We propose a nonparametric statistical model that yields a formal basis for randomization tests. We adapt the model for the presence of covariate imbalance in the form of selection bias and investigate the effects of bias on the rejection probability of the randomization test using Monte Carlo simulations. Finally, we show that ancillary statistics can be used to control for the influence of bias. We show that covariate imbalance leads to an inflation of the type I error probability. The proposed nonparametric model allows for the use of ancillary statistics that yield an unbiased adjusted randomization test. [ABSTRACT FROM AUTHOR]

We examine the use of randomization-based inference for analyzing multiarmed randomized clinical trials, including the application of conditional randomization tests to multiple comparisons. The view is taken that the linkage of the statistical test to the experimental design (randomization procedure) should be recognized. A selected collection of randomization procedures generalized to multiarmed treatment allocation is summarized, and generalizations for two randomization procedures that heretofore were designed for only two treatments are developed. We explain the process of computing the randomization test and conditional randomization test via Monte Carlo simulation, developing an efficient algorithm that makes multiple comparisons possible that would not be possible using a standard algorithm, demonstrate the preservation of type I error rate, and explore the relationship of statistical power to the randomization procedure in the presence of a time trend and outliers. We distinguish between the interpretation of the p-value in the randomization test and in the population test and verify that the randomization test can be approximated by the population test on some occasions. Data from two multiarmed clinical trials from the literature are reanalyzed to illustrate the methodology. [ABSTRACT FROM AUTHOR]

Health informatics applications reduce time intervals in acute coronary syndromes, but their impact on guideline adherence is unknown. This pre–post intervention study compared guideline adherence between telemedically supported (n = 101, April 2014–July 2015) and conventional on-scene care (n = 120, January 2014–March 2014) in acute coronary syndrome. A multivariate logistic regression was performed for dependent variables: adverse events 0 versus 0, p = NA; electrocardiogram 101 versus 120, p = NA; acetylic salicylic acid 91 versus 102, p = 0.21; heparin 92 versus 112, p = 0.99; morphine 96 versus 107, p = 0.33; oxygen 83 versus 102, p = 0.92; glyceroltrinitrate 55 versus 90, p = 0.038; correct destination: 100 versus 119, p = 1.0. The time from ambulance arrival to hospital arrival was prolonged with telemedicine: 48.7 ± 11 min versus 35.5 ± 8.1 min, p < 0.001. Guideline adherence showed no differences except for glyceroltrinitrate. Prolonged time requirements are critical, though explainable. However, this approach enables a timely and high-quality backup strategy if only paramedics are on-scene. [ABSTRACT FROM AUTHOR]

In testing the significance of treatment effects in randomized clinical trials (RCTs), randomization-based inference is distinguished from population-based parametric and nonparametric inference, such as the t-test or permutation tests, taking into account three properties: preservation of type I error rate, relation of power to the randomization procedure, and flexibility in choosing the test statistic. In this paper, we revisit rationale of the properties and provide justification through simulations. We propose that the choice of randomization procedures and the analysis of RCTs can be facilitated by the application of randomization-based inference. [ABSTRACT FROM AUTHOR]

"…The customary test for an observed difference…is based on an enumeration of the probabilities, on the initial hypothesis that two treatments do not differ in their effects,…of all the various results which would occur if the trial were repeated indefinitely with different random samples of the same size as those actually used." -Peter Armitage ("Sequential tests in prophylactic and therapeutic trials" in Quarterly Journal of Medicine, 1954;23(91):255-274). Randomization has been the hallmark of the clinical trial since Sir Bradford Hill adopted it in the 1946 streptomycin trial. An exploration of the early literature yields three rationales, ie, (i) the incorporation of randomization provides unpredictability in treatment assignments, thereby mitigating selection bias; (ii) randomization tends to ensure similarity in the treatment groups on known and unknown confounders (at least asymptotically); and (iii) the act of randomization itself provides a basis for inference when random sampling is not conducted from a population model. Of these three, rationale (iii) is often forgotten, ignored, or left untaught. Today, randomization is a rote exercise, scarcely considered in protocols or medical journal articles. Yet, the literature of the last century is rich with statistical articles on randomization methods and their consequences, authored by some of the pioneers of the biostatistics and statistics world. In this paper, we review some of this literature and describe very simple methods to rectify some of the oversight. We describe how randomization-based inference can be used for virtually any outcome of interest in a clinical trial. Special mention is made of nonstandard clinical trials situations. [ABSTRACT FROM AUTHOR]

The impact of selection bias on the results of clinical trials has been analyzed extensively for trials of two treatments, yet its impact in multi-arm trials is still unknown. In this paper, we investigate selection bias in multi-arm trials by its impact on the type I error probability. We propose two models for selection bias, so-called biasing policies, that both extend the classic guessing strategy by Blackwell and Hodges. We derive the distribution of the F-test statistic under the misspecified outcome model and provide a formula for the type I error probability under selection bias. We apply the presented approach to quantify the influence of selection bias in multi-arm trials with increasing number of treatment groups using a permuted block design for different assumptions and different biasing strategies. Our results confirm previous findings that smaller block sizes lead to a higher proportion of sequences with inflated type I error probability. Astonishingly, our results also show that the proportion of sequences with inflated type I error probability remains constant when the number of treatment groups is increased. Realizing that the impact of selection bias cannot be completely eliminated, we propose a bias adjusted statistical model and show that the power of the statistical test is only slightly deflated for larger block sizes. [ABSTRACT FROM AUTHOR]

Background: Randomization is considered to be a key feature to protect against bias in randomized clinical trials. Randomization induces comparability with respect to known and unknown covariates, mitigates selection bias, and provides a basis for inference. Although various randomization procedures have been proposed, no single procedure performs uniformly best. In the design phase of a clinical trial, the scientist has to decide which randomization procedure to use, taking into account the practical setting of the trial with respect to the potential of bias. Less emphasis has been placed on this important design decision than on analysis, and less support has been available to guide the scientist in making this decision.Methods: We propose a framework that weights the properties of the randomization procedure with respect to practical needs of the research question to be answered by the clinical trial. In particular, the framework assesses the impact of chronological and selection bias on the probability of a type I error. The framework is applied to a case study with a 2-arm parallel group, single center randomized clinical trial with continuous endpoint, with no-interim analysis, 1:1 allocation and no adaptation in the randomization process.Results: In so doing, we derive scientific arguments for the selection of an appropriate randomization procedure and develop a template which is illustrated in parallel by a case study. Possible extensions are discussed.Conclusion: The proposed ERDO framework guides the investigator through a template for the choice of a randomization procedure, and provides easy to use tools for the assessment. The barriers for the thorough reporting and assessment of randomization procedures could be further reduced in the future when regulators and pharmaceutical companies employ similar, standardized frameworks for the choice of a randomization procedure. [ABSTRACT FROM AUTHOR]

Aims: Diabetes distress (DD) is associated with poor health outcomes. Little is known about level of DD in young adults (YAs) with youth-onset type 2 diabetes (T2D) or individual factors associated with DD. Aims were to assess level of DD, and factors associated with high DD, in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. Methods: YAs completed the Diabetes Distress Scale (DDS), PHQ-9 (depressive symptoms) and GAD7 (anxiety), at the end-of-study visit. Factors that may relate to DD were demographics (sex, race/ethnicity, age, education, income), medical history (HbA1c, BMI, complications), and social/economic factors (# persons in household, parent status, healthcare coverage, access to diabetes care provider). Univariate logistic regressions found factors associated with high DD. Multivariate regression controlled for sex, HbA1c, healthcare coverage, anxiety, depressive symptoms and retinopathy. Results: Of 457 YAs, mean age=26.7 yrs, 65% were female, 20% non-Hispanic white, 38% non-Hispanic Black, 20% Hispanic, 62% <$25K income. High DD (DDS ≥2) was reported by 108 (23.6%). Subscales found 39.8% with high "regimen distress," 29.8% with high "emotional burden" (29.8%). Unadjusted results: Sex (p=0.001), HbA1c (p<0.0001), no healthcare coverage (p=0.043), hypertension (p=0.036) and retinopathy (p=0.005) were associated with DD. Moderate-severe depressive symptoms (PHQ-9 ≥10) and anxiety (GAD≥5) were also associated with DD (p<0.0001). In multivariate analyses, female sex, anxiety, HbA1c (p<0.0001 for all), and no healthcare coverage (p=0.022) remained associated with DD. Individual microvascular complications, BMI, depressive symptoms, race/ethnicity, age, education, income, # persons in household, or having a diabetes care provider did not relate to adjusted DD. Conclusion: In young adults with youth-onset T2D, high DD was significantly more likely in females, and those with anxiety, high HbA1c, and without healthcare coverage. Disclosure: P. M. Trief: None. M. S. Rayas: Research Support; Self; National Center for Advancing Translational Sciences. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. D. Uschner: None. M. Tung: None. L. Chao: None. R. M. Farrell: Stock/Shareholder; Self; Tandem Diabetes Care. J. Keady: None. S. A. Macleish: Advisory Panel; Self; Insulet Corporation. M. D. Marcus: Advisory Panel; Self; Weight Watchers International, Inc. Funding: National Institute of Diabetes and Digestive and Kidney Diseases (U01DK61212, U01DK61230, U01DK61239, U01DK61242, U01DK61254) [ABSTRACT FROM AUTHOR]

Background: Medication adherence is poor in young adults (YAs) with youth-onset type 2 diabetes (T2D). Little is known about intrapersonal factors that may relate to adherence. Aims: To assess associations of intrapersonal factors to medication adherence in Treatment Options for Adolescents and Youth (TODAY2) YAs. Methods: Validated measures (attitudes, beliefs, self-efficacy, distress, depression, anxiety) and insulin use surveys, were completed at a TODAY2 visit. Adherence to oral hypoglycemia agents (OHAs) was assessed with 3 monthly unannounced phone pill counts. Those taking ≥ 80% of pills/insulin classified "high-adherent;" < 80% of pills/insulin, "low-adherent." If taking none, reasons were queried and adjudicated (no need vs. low-adherent). Logistic regressions assessed factors associated with OHA/insulin adherence controlling for gender, race/ethnicity, education, duration, healthcare coverage, and HbA1c. Results: Of 381 YAs in iCount (mean age 26 yrs, 68% female, 20% non-Hispanic white, 38% non-Hispanic Black, 36% Hispanic, 64.9% <$25k income, mean HbA1c 9.4%), 224 were taking OHAs. As 12 lacked adherence scores, 212 were in OHA analyses; 70.0% were low-adherent to OHAs. Negative beliefs about medicines were associated with low OHA adherence in univariate, but not multivariate analyses. No measured intrapersonal factor was associated with OHA adherence. Of 192 in insulin analyses, 37.0% were low-adherent to insulin. Low insulin adherence was associated with less belief in seriousness of T2D (p=0.036), more concern that medications are overused (p=0.005) and diabetes distress (p=0.020). Insulin adherence did not relate to other diabetes attitudes or beliefs, depressive symptoms or anxiety. Limitations: small N with high depressive/anxiety symptoms; self-reported insulin use. Conclusion: Poor medication adherence is common in YAs with youth-onset T2D. More needs to be learned about factors associated with adherence to develop tailored, effective interventions. Disclosure: P. M. Trief: None. D. Uschner: None. M. Tung: None. K. Drews: None. S. Kalichman: None. B. Anderson: None. J. D. Bulger: None. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. Funding: National Institute of Diabetes and Digestive and Kidney Diseases (1RO1DK110456-01A1; U01DK61230) [ABSTRACT FROM AUTHOR]