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Background: One approach to test for differential associations between plant foods with health uses a scoring approach: foods categorized into animal or 'healthy' plant-based or 'unhealthy' plant-based groups to construct a plant-based diet index (PDI), healthy PDI (hPDI), and unhealthy PDI (uPDI). Objective: To evaluate robustness of associations between diet indices and incident coronary heart disease (CHD) risk when recategorizing food groups in indices. Methods: Using REasons for Geographic and Racial Differences in Stroke (REGARDS) data, we replicated a published use of the scoring approach. Using Cox proportional hazards regression, we assessed ramifications of the following on associations between diet indices and CHD risk: 1) reconfiguring foods within and among food groups, using potatoes as an example, 2) leave-one-out analysis for each of 12 plant-based food groups, and 3) agnostically redefining each food group as 'healthy' or 'unhealthy'. Results: Over 153,286 person-years of follow-up, there were 868 cases of CHD. Replication analyses did not reach statistical significance. General patterns of magnitude of hazard ratios (HRs) in replication and reconfiguration models were PDI HRs < hPDI HRs < uPDI HRs for women, and hPDI < PDI < uPDI for men. Five models reconfiguring potatoes resulted in small, varied differences in PDI, hPDI, and uPDI associations. Leave-one-out analyses resulted in greater variation of associations between indices and CHD. In agnostic models, each plant-based food group was classified in indices as 'healthy' and 'unhealthy' with statistically significant beneficial or deleterious associations with CHD. Averaged over 4,096 models, HRs' shifts were small when food groups were moved between 'healthy' and 'unhealthy'. Conclusion: Statistically significant associations between hPDI, uPDI, and PDI and incident CHD were not replicated. Small perturbations of the scoring approach had varied impacts on HRs. Agnostically constructing diet indices demonstrated the potential for guilt (or benefit) by association: any of the food groups we studied could be categorized with others in an index showing beneficial or deleterious associations.
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We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
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Estudios Observacionales como Asunto , Proyectos de Investigación , Humanos , Proyectos de Investigación/normas , Modelos Estadísticos , Interpretación Estadística de DatosRESUMEN
Nally et al. [...].
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In this correspondence, we explain the reasoning for invalidity of the analysis choices by Kolberg et al., and provide the results produced using correct statistical procedures for their study design. Reassuringly, we could verify the original conclusions. That is, results of the corrected statistical models are similar to the results of the original analysis. Regardless of the magnitude of difference that corrected statistical methods make, results and conclusions that are derived from invalid methods are unsubstantiated. By verifying the results, we allow the readers to be assured that the published conclusions in the study by Kolberg et al. now rest on a sound evidential basis.
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Síntomas Afectivos , Demencia , Humanos , Solución de Problemas , Análisis por Conglomerados , Modelos Estadísticos , Demencia/terapiaRESUMEN
Wang et al. [...].
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Aptitud Física , Salud Pública , Preescolar , Humanos , Ensayos Clínicos Controlados Aleatorios como Asunto , Ejercicio Físico , Terapia por EjercicioRESUMEN
We assessed the preference for two behavioural weight loss programs, Diabetes Prevention Program (DPP) and Healthy Weight for Living (HWL) in adults with obesity. A cross-sectional survey was fielded on the Amazon Mechanical Turk. Eligibility criteria included reporting BMI ≥30 and at least two chronic health conditions. Participants read about the programs, selected their preferred program, and answered follow-up questions. The estimated probability of choosing either program was not significantly different from .5 (N = 1005, 50.8% DPP and 49.2% HWL, p = .61). Participants' expectations about adherence, weight loss magnitude, and dropout likelihood were associated with their choice (p < .0001). Non-White participants (p = .040) and those with monthly income greater than $4999 (p = .002) were less likely to choose DPP. Participants who had postgraduate education (p = .007), did not report high serum cholesterol (p = .028), and reported not having tried losing weight before (p = .025) were more likely to choose DPP. Those who chose HWL were marginally more likely to report that being offered two different programs rather than one would likely affect their decision to enrol in one of the two (p = .052). The enrolment into DPP and HWL was balanced, but race, educational attainment, income, previous attempt to lose weight, and serum cholesterol levels had significant associations with the choice of weight loss program.
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Conducta de Elección , Obesidad , Programas de Reducción de Peso , Adulto , Humanos , Colesterol/sangre , Estudios Transversales , Diabetes Mellitus/prevención & control , Escolaridad , Obesidad/prevención & control , Factores Raciales , Factores Socioeconómicos , Programas de Reducción de Peso/estadística & datos numéricos , Masculino , Femenino , Persona de Mediana EdadAsunto(s)
Demencia , Música , Humanos , Cuidadores , Demencia/terapia , Calidad de Vida , Análisis por ConglomeradosAsunto(s)
Culinaria , Conducta Alimentaria , Humanos , Niño , Ingestión de Alimentos , Análisis por Conglomerados , TelevisiónRESUMEN
Ayudhaya et al. examined the effect of Behavioral Activation on daily step count and heart rate variability among older adults with depression in a study labeled a cluster randomized controlled trial (cRCT). However, only one cluster was assigned to either of the study conditions. Such a design would have zero degrees of freedom for inferential testing, because the variation due to cluster membership cannot be estimated apart from the variation due to treatment assignment. Thus, the intervention effect is completely confounded with the cluster effect. The study should be labeled a quasi-experimental study, not a cRCT. Accordingly, the numerical results should be interpreted as associations but not evidence for causal relationships.
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Terapia Conductista , Depresión , Humanos , Anciano , Depresión/terapia , Tailandia , Frecuencia CardíacaRESUMEN
We read the report by Conner and colleagues that tested whether kiwifruit or vitamin C affected measures of vitality [...].
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Actinidia , Ácido Ascórbico , Adulto , Humanos , Distribución Aleatoria , Ensayos Clínicos Controlados Aleatorios como Asunto , Comprimidos , VitaminasRESUMEN
In a published randomized controlled trial, household units were randomized to a nutrient bar supplementation group or a control condition, but the non-independence of observations within the same household (i.e., the clustering effect) was not accounted for in the statistical analyses. Therefore, we reanalyzed the data appropriately by adjusting degrees of freedom using the between-within method, and accounting for household units using linear mixed effect models with random intercepts for family units and subjects nested within family units for each reported outcome. Results from this reanalysis showed that ignoring the clustering and nesting effects in the original analyses had resulted in anticonservative (i.e., too small) time x group interaction p-values. Still, majority of the conclusions remained unchanged.
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Enfermedades Cardiovasculares , Suplementos Dietéticos , Adulto , Humanos , Adolescente , Análisis por Conglomerados , Metaboloma , Familia , NutrientesRESUMEN
BACKGROUND: Cluster randomized controlled trials (cRCTs) are increasingly used but must be analyzed carefully. We conducted a simulation study to evaluate the validity of a parametric bootstrap (PB) approach with respect to the empirical type I error rate for a cRCT with binary outcomes and a small number of clusters. METHODS: We simulated a case study with a binary (0/1) outcome, four clusters, and 100 subjects per cluster. To compare the validity of the test with respect to error rate, we simulated the same experiment with K=10, 20, and 30 clusters, each with 2,000 simulated datasets. To test the null hypothesis, we used a generalized linear mixed model including a random intercept for clusters and obtained p-values based on likelihood ratio tests (LRTs) using the parametric bootstrap method as implemented in the R package "pbkrtest". RESULTS: The PB test produced error rates of 9.1%, 5.5%, 4.9%, and 5.0% on average across all ICC values for K=4, K=10, K=20, and K=30, respectively. The error rates were higher, ranging from 9.1% to 36.5% for K=4, in the models with singular fits (i.e., ignoring clustering) because the ICC was estimated to be zero. CONCLUSION: Using the parametric bootstrap for cRCTs with a small number of clusters results in inflated error rates and is not valid.