Evaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs.

Many individually randomized group treatment (IRGT) trials randomly assign individuals to study arms but deliver treatments via shared agents, such as therapists, surgeons, or trainers. Post-randomization interactions induce correlations in outcome measures between participants sharing the same agent. Agents can be nested in or crossed with trial arm, and participants may interact with a single agent or with multiple agents. These complications have led to ambiguity in choice of models but there have been no systematic efforts to identify appropriate analytic models for these study designs. To address this gap, we undertook a simulation study to examine the performance of candidate analytic models in the presence of complex clustering arising from multiple membership, single membership, and single agent settings, in both nested and crossed designs and for a continuous outcome. With nested designs, substantial type I error rate inflation was observed when analytic models did not account for multiple membership and when analytic model weights characterizing the association with multiple agents did not match the data generating mechanism. Conversely, analytic models for crossed designs generally maintained nominal type I error rates unless there was notable imbalance in the number of participants that interact with each agent.

Impact of National Institutes of Health and Food and Drug Administration Tobacco Research Funding: A Bibliometrics Analyses.

INTRODUCTION: Conduct bibliometric analyses documenting the output of National Institutes of Health (NIH) tobacco-related and Food and Drug Administration (FDA) tobacco regulatory science (FDA-TRS) research portfolios. AIMS AND METHODS: PubMed identifiers for publications between 2015 and 2020 citing tobacco funding by NIH and/or FDA were imported into NIH iCite generating measures of productivity and influence, including number of citations, journal, relative citation ratios (RCR), and comparison of research influence across Web of Science (WoS) disciplines. Coauthorship and measures of centrality among and between NIH and FDA-supported investigators gauged collaboration. RESULTS: Between FY 2015 and 2020, 8160 publications cited funding from NIH tobacco-related grants, 1776 cited FDA-TRS grants and 496 cited Common funding (ie, both NIH and FDA-TRS funding). The proportion of publications citing NIH grants declined while those citing FDA-TRS or Common funding rose significantly. Publications citing Common funding showed the highest influence (mean RCR = 2.52). Publications citing FDA-TRS funding displayed higher median RCRs than publications citing NIH funding in most WoS categories. Higher translational progress was estimated over time for FDA-TRS and Common publications compared to NIH publications. Authors citing Common funding scored highest across all collaboration measures. CONCLUSIONS: This study demonstrates the high bibliometric output of tobacco research overall. The rise in publications citing FDA-TRS and Common likely reflects increased funding for TRS research. Higher RCRs across WoS subject categories and trends towards human translation among FDA-TRS and Common publications indicate focus on research to inform regulation. This analysis suggests that FDA support for TRS has expanded the field of tobacco control resulting in sustained productivity, influence, and collaboration. IMPLICATIONS: This paper is the first effort to better describe the impact of tobacco research resulting from the addition of FDA funding for TRS in the past decade. The analysis provides impetus for further investigation into the publication topics and their focus which would offer insight into the specific evidence generated on tobacco control and regulation.

NIH Tobacco Research and the Emergence of Tobacco Regulatory Science.

INTRODUCTION: This study explores how the emergence of FDA-funded Tobacco Regulatory Science (TRS) research complements and perhaps influenced the direction of tobacco research supported by NIH. AIMS AND METHODS: New NIH- and FDA-funded tobacco projects awarded in fiscal years (FY) 2011-2020 were identified using internal NIH databases of awarded grants. Project abstracts and research aims were coded by the authors to characterize research domains and tobacco products studied. RESULTS: Between FY 2011 and 2020, NIH funded 1032 and FDA funded 322 new tobacco projects. For the years and grant activity codes studied, the number of new NIH tobacco projects declined while FDA's increased; combined the number of new projects held steady. Much of NIH research included smoking combustibles (43.7%). The most common products in FDA research were cigarettes (74.8%) and e-cigarettes/ENDS (48.1%). Most NIH (58.6%) and FDA (67.7%) projects included research on the determinants of tobacco use. Another area of apparent overlap was health effects (29.5% NIH and 30.1% FDA). Projects unique to NIH included treatment interventions (33.3%), disease pathology/progression (17.8%) and neurobiology (18.9%). A minority of both NIH and FDA projects included populations particularly vulnerable to tobacco product use. CONCLUSIONS: In total, support for new tobacco research supported by NIH and FDA combined remained steady for the time period covered, though there was a concomitant decline in NIH tobacco projects with the increase in FDA-funded TRS projects for the activity codes studied. Despite the apparent overlap in some areas, both NIH and FDA support research that is unique to their respective missions. IMPLICATIONS: NIH continues to support tobacco research that falls within and outside of FDA's regulatory authorities. This research still is needed not only to bolster the evidence base for regulatory decisions at the national and state levels, but also to advance a comprehensive scientific agenda that can inform multiple levels of influence on tobacco control, use and addiction. It will be important to continue monitoring FDA-funded TRS and NIH-funded tobacco research portfolios to ensure that the level of support for and focus of the research is sufficient to address the burden of tobacco-related morbidity and mortality.

Modeling transformational policy pathways on low growth and negative growth scenarios to assess impacts on socioeconomic development and carbon emissions.

Degrowth advocates argue for structural transformations in how economies and societies prioritize material wealth accumulation to reduce the negative effects of future anthropogenic climate change. Degrowth proponents argue that human economic activity could be lessened, and societies transformed to prioritize improved wellbeing, reducing the threat of climate change. This paper explores implications of alternative patterns of economic growth with transformational policy pathways (i.e., redistribution) to assess what effects economic growth and broader policies have on changing patterns of human development across both the Global North and South. Using the International Futures model, this article shows that negative growth and societal transformations in the Global North are possible without dramatically damaging long-term global socioeconomic development, though these interventions do not solve the global climate crisis, reducing future cumulative carbon emissions by 10.5% through 2100. On the other hand, a global negative growth scenario will significantly reduce future cumulative carbon emissions (45%) but also dramatically undermines the pursuit of global development goals, like the elimination of poverty. Even with global policies that significantly increase cash transfers to the poor and retired, dramatically improve income inequality, and eliminate military spending, the Global Negative Growth Big Push scenario leads to an increase of 15 percentage points in global extreme poverty by 2100.

Implementation Research at NHLBI: Methodological and Design Challenges and Lessons Learned from the DECIPHeR Initiative.

NHLBI funded seven projects as part of the Disparities Elimination through Coordinated Interventions to Prevent and Control Heart and Lung Disease Risk (DECIPHeR) Initiative. They were expected to collaborate with community partners to (1) employ validated theoretical or conceptual implementation research frameworks, (2) include implementation research study designs, (3) include implementation measures as primary outcomes, and (4) inform our understanding of mediators and mechanisms of action of the implementation strategy. Several projects focused on late-stage implementation strategies that optimally and sustainably delivered two or more evidence-based multilevel interventions to reduce or eliminate cardiovascular and/or pulmonary health disparities and to improve population health in high-burden communities. Projects that were successful in the three-year planning phase transitioned to a 4-year execution phase. NHLBI formed a Technical Assistance Workgroup during the planning phase to help awardees refine study aims, strengthen research designs, detail analytic plans, and to use valid sample size methods. This paper highlights methodological and study design challenges encountered during this process. Important lessons learned included (1) the need for greater emphasis on implementation outcomes, (2) the need to clearly distinguish between intervention and implementation strategies in the protocol, (3) the need to address clustering due to randomization of groups or clusters, (4) the need to address the cross-classification that results when intervention agents work across multiple units of randomization in the same arm, (5) the need to accommodate time-varying intervention effects in stepped-wedge designs, and (6) the need for data-based estimates of the parameters required for sample size estimation.

Analysis of multiple-period group randomized trials: random coefficients model or repeated measures ANOVA?

BACKGROUND: Multiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time × group variation if such variation was present in the data-generating model. METHODS: Continuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time × group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time × group random effects. The analytic models were then reapplied without specifying random effects for time × group. RESULTS: Results indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time × group variance. CONCLUSION: The time × group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs.

Two weights make a wrong: Cluster randomized trials with variable cluster sizes and heterogeneous treatment effects.

In cluster randomized trials (CRTs), the hierarchical nesting of participants (level 1) within clusters (level 2) leads to two conceptual populations: clusters and participants. When cluster sizes vary and the goal is to generalize to a hypothetical population of clusters, the unit average treatment effect (UATE), which averages equally at the cluster level rather than equally at the participant level, is a common estimand of interest. From an analytic perspective, when a generalized estimating equations (GEE) framework is used to obtain averaged treatment effect estimates for CRTs with variable cluster sizes, it is natural to specify an inverse cluster size weighted analysis so that each cluster contributes equally and to adopt an exchangeable working correlation matrix to account for within-cluster correlation. However, such an approach essentially uses two distinct weights in the analysis (i.e. both cluster size weights and covariance weights) and, in this article, we caution that it will lead to biased and/or inefficient treatment effect estimates for the UATE estimand. That is, two weights "make a wrong" or lead to poor estimation characteristics. These findings are based on theoretical derivations, corroborated via a simulation study, and illustrated using data from a CRT of a colorectal cancer screening program. We show that, an analysis with both an independence working correlation matrix and weighting by inverse cluster size is the only approach that always provides valid results for estimation of the UATE in CRTs with variable cluster sizes.

How many people is the COVID-19 pandemic pushing into poverty? A long-term forecast to 2050 with alternative scenarios.

The COVID-19 pandemic has changed the course of human development. In this manuscript we analyze the long-term effect of COVID-19 on poverty at the country-level across various income thresholds to 2050. We do this by introducing eight quantitative scenarios that model the future of Sustainable Development Goal 1 (SDG1) achievement using alternative assumptions about COVID-19 effects on both economic growth and inequality in the International Futures model. Relative to a scenario without the pandemic (the No COVID scenario), the COVID Base scenario increases global extreme poverty by 73.9 million in 2020 (the range across all scenarios: 43.5 to 155.0 million), 63.6 million in 2030 (range: 9.8 to 167.2 million) and 57.1 million in 2050 (range: 3.1 to 163.0 million). The COVID Base results in seven more countries not meeting the SDG1 target by 2030 that would have achieved the target in a No COVID scenario. The most pessimistic scenario results in 17 more countries not achieving SDG1 compared with a No COVID scenario. The greatest pandemic driven increases in poverty occur in South Asia and sub-Saharan Africa.

The iCook 4-H Study: An Intervention and Dissemination Test of a Youth/Adult Out-of-School Program.

OBJECTIVE: To describe outcomes from intervention and dissemination of iCook 4-H. DESIGN: Five-state, community-based participatory research and a randomized, controlled trial followed by a 5-state, nonrandomized dissemination test of the iCook 4-H curriculum with control and treatment groups. SETTING: Community and university sites. PARTICIPANTS: Youths aged 9-10 years and their adult food preparer; 228 dyads in the intervention and 74 dyads in dissemination. INTERVENTION(S): Theoretical frameworks were Social Cognitive Theory and the experiential 4-H learning model. Six 2-hour, biweekly sessions on cooking, eating, and playing together followed by monthly newsletters and boosters until 24 months, expanded to 8 sessions for dissemination. MAIN OUTCOME MEASURE(S): Youth body mass index (BMI) z-scores, measured height and weight, and youth/adult program outcome evaluations surveys. ANALYSIS: Linear mixed models, group, time, and group × time interaction for BMI z-score and program outcomes changes. Significance levels = P ≤ .05; interaction term significance = P ≤ .10. RESULTS: In intervention, treatment BMI z-scores increased compared with controls based on significant interaction (P = .04). For odds of being overweight or obese at 24 months, there was no significant interaction (P = .18). In dissemination, based on significant interaction, treatment youths increased cooking skills (P = .03) and treatment adults increased cooking together (P = .08) and eating together (P = .08) compared with controls. CONCLUSIONS AND IMPLICATIONS: iCook 4-H program outcomes were positive for mealtime activities of cooking and eating together. The program can be successfully implemented by community educators. The increase in BMI z-scores needs further evaluation for youths in cooking programs.

The iCook 4-H Study: Report on Physical Activity and Sedentary Time in Youth Participating in a Multicomponent Program Promoting Family Cooking, Eating, and Playing Together.

OBJECTIVE: To report physical activity and sedentary time outcomes of youth in iCook 4-H. STUDY DESIGN AND SETTING: iCook 4-H was a 5-state, randomized, control-treatment, family-based childhood obesity prevention intervention promoting cooking, eating, and playing together. PARTICIPANTS AND INTERVENTION: Youth aged 9-10 years and the main preparer of their meals participated in the 12-week program followed by monthly newsletters and biyearly booster sessions until 24 months. MAIN OUTCOME MEASURE(S): A total of 155 youth were fitted with an Actigraph GT3X+ accelerometer, which they wore for 7 days at baseline and 4, 12, and 24 months to measure mean daily minutes per hour of waking wear time for sedentary time (ST), light physical activity (PA) (LPA), moderate PA, vigorous PA, and moderate to vigorous PA. Self-reported PA was assessed using the Block Kids Physical Activity Screener and additional questions querying for the program goal of the frequency of family actively playing together. Linear mixed models were used to determine differences from baseline to 24 months. Significance was set at P ≤ .05. RESULTS: There was a significant (P < .05) group × time interaction for LPA (adjusted interaction B estimate, 95% confidence interval; 0.18 [0.05, 0.30]) and ST (-0.15 [-0.26, -0.04]); ST increased and LPA decreased in the treatment group. There were no differences in other accelerometer-derived PA measures, self-report Block Kids Physical Activity Screener measures, or frequency of family actively playing together at any time point. CONCLUSIONS AND IMPLICATIONS: iCook 4-H was a multicomponent program observing youth aged 9-10 years for 24 months that focused on enhancing cooking skills, mealtime behavior and conversation, and PA through daily family activities. Greater emphasis on developing PA skills, changing environmental factors, and increasing PA both in and after school may be needed.