Confronting challenges in intervention research with ethnically diverse older adults: the USC Well Elderly II Trial

Jackson, Jeanne; Mandel, Deborah; Blanchard, Jeanine; Carlson, Michael; Cherry, Barbara; Azen, Stanley P.; Chou, Chih Ping; Jordan‐Marsh, Maryalice; Forman, Todd; White, Brett; Granger, Douglas A.; Knight, Bob G.; Clark, Florence

doi:10.1177/1740774508101191

Cited by 60 publications

(52 citation statements)

References 46 publications

(51 reference statements)

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“…Data from the Well Elderly 2 study (Clark et al, 2011;Jackson et al, 2009) are used to illustrate that the choice between M 2 and M 3 can make a practical difference. A general goal in the Well Elderly 2 study was to assess the efficacy of an intervention strategy aimed at improving the physical and emotional health of older adults.…”

Section: Illustrationsmentioning

confidence: 99%

Robust ANCOVA: Confidence intervals that have some specified simultaneous probability coverage when there is curvature and two covariates

Wilcox

2017

J. Mod. App. Stat. Meth.

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Consider the commonly occurring situation where the goal is to compare two independent groups and there are two covariates. Let Mj(X) be some conditional measure of location for the j th group associated with some random variable Y given X = (X1, X2). The goal is to H0: M1(X) = M2(X) for each X  Ω in a manner that controls the probability of one or more Type I errors. An extant technique (method M1 here) addresses this goal without making any parametric assumption about Mj(X). However, a practical concern is that it does not provide enough detail regarding where the regression surfaces differ, due to using a very small number of covariate points, which can result in relatively low power. Method M2 was proposed for testing the global hypothesis H0: M1(X) = M2(X) for all X  Ω, which offers a distinct power advantage over method M1. It uses the deepest half of the covariate points rather than small number of points used by method M1. However, method M2 does not provide any details about which covariate points yield a significant result. A multiple comparison procedure is proposed that deals with this shortcoming of method M2, and simultaneously it can provide higher power than method M1.

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Section: Illustrationsmentioning

confidence: 99%

Robust ANCOVA: Confidence intervals that have some specified simultaneous probability coverage when there is curvature and two covariates

Wilcox

2017

J. Mod. App. Stat. Meth.

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“…Motivated by data stemming from the Well II study (Jackson et al 2009), this paper examines the impact of tied values on the probability of a Type I error when testing hypotheses via various robust regression estimators. Many of the dependent variables in the Well Elderly study were the sum of Likert scales.…”

Section: Introductionmentioning

confidence: 99%

Robust Regression Estimators When There are Tied Values

Wilcox

Clark

2013

J. Mod. App. Stat. Meth.

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It is well known that when using the ordinary least squares regression estimator, outliers among the dependent variable can result in relatively poor power. Many robust regression estimators have been derived that address this problem, but the bulk of the results assume that the dependent variable is continuous. It is demonstrated that when there are tied values, several robust regression estimators can perform poorly in terms of controlling the Type I error probability, even with a large sample size. The presence of tied values does not necessarily mean that they perform poorly, but there is the issue of whether there is a robust estimator that performs reasonably well in situations where other estimators do not. The main result is that a modification of the Theil-Sen estimator achieves this goal. Results on the small-sample efficiency of the modified Theil-Sen estimator are reported as well. Data from the Well Elderly 2 Study, which motivated this study, are used to illustrate that the modified Theil-Sen estimator can make a practical difference.

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“…The beneficial effects of cognitive preservation may not be obvious enough to serve as a reinforcer for continuous practice. Intervention studies on lifestyle modification for cardiovascular health and chronic diseases suggested that factors such as peer support, age, health status, self-efficacy, cognitive and mental conditions were facilitators and barriers to intervention (Jackson et al, 2009;Leijon et al, 2011;Griffo et al, 2012). It is also recognized that positive effects may cease after intervention stops, especially when supervised practice is no longer available (Lam et al, 2010).…”

Section: Barriers To Practicementioning

confidence: 99%

Maintaining long-term adherence to lifestyle interventions for cognitive health in late life

Lam¹,

Cheng

2012

Int. Psychogeriatr.

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Long life expectancy is associated with a higher chance of experiencing cognitive impairment in a lifespan. It is well recognized that multiple factors play important roles in the maintenance of cognitive health when one gets older. While the mechanisms of neurodegeneration are complex, it is encouraging to note that simple healthy lifestyles maintained over time may modulate the rate of degeneration or buffer its effects when it becomes inevitable.

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Confronting challenges in intervention research with ethnically diverse older adults: the USC Well Elderly II Trial

Abstract: Background-Community-dwelling older adults are at risk for declines in physical health, cognition, and psychosocial well-being. However, their enactment of active and health-promoting lifestyles can reduce such declines.

Cited by 60 publications

References 46 publications

Robust ANCOVA: Confidence intervals that have some specified simultaneous probability coverage when there is curvature and two covariates

Robust ANCOVA: Confidence intervals that have some specified simultaneous probability coverage when there is curvature and two covariates

Robust Regression Estimators When There are Tied Values

Maintaining long-term adherence to lifestyle interventions for cognitive health in late life

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