Randomized trial of DVD, telephone, and usual care for increasing mammography adherence

Champion, Victoria L.; Rawl, Susan M.; Bourff, Sara A.; Champion, Kristen M.; Smith, Lisa; Buchanan, Adam H.; Fish, Laura J.; Monahan, Patrick O.; Stump, Timothy E.; Springston, Jeffery K.; Gathirua-Mwangi, Wambui G.; Skinner, Celette Sugg

doi:10.1177/1359105314542817

Cited by 23 publications

(23 citation statements)

References 29 publications

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“…Indeed, previous studies have shown that mere measurement may promote health behaviors(25), and a number of randomized intervention trials have found changes in cancer screening outcomes among individuals in comparison groups who completed data collection but did not receive tailored interventions. (16;26;27)…”

Section: Discussionmentioning

confidence: 99%

Impact of Risk Assessment and Tailored versus Nontailored Risk Information on Colorectal Cancer Testing in Primary Care: A Randomized Controlled Trial

Skinner

Halm

Bishop

et al. 2015

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background Colorectal cancer (CRC) screening is effective but underused. Guidelines for which tests are recommended and at what intervals depend on specific risks. We developed a tablet-based Cancer Risk Intake System (CRIS) that asks questions about risk prior to appointments and generates tailored printouts for patients and physicians summarizing and matching risk factors with guideline-based recommendations. Methods Randomized controlled trial among patients who: (1) used CRIS and they and their physicians received tailored printouts; (2) used CRIS to answer questions but received standard information about cancer screening while their physicians received a standard electronic chart prompt indicating they were age-eligible but not currently adherent for CRC screening; or (3) comprised a no-contact group that neither used CRIS nor received any information while their physicians received the standard prompt. Participation in testing was assessed via electronic medical record at 12 months. Results Participation in any CRC testing was three times higher for those who used the CRIS and received any printed materials, compared to no-contact controls (47% v. 16%; p < 0.0001). Among CRIS users ages 50 and older, participation in any testing was higher in the tailored group (53% v. 44%, p=0.023). Conclusion Use of CRIS and receipt of any information facilitated participation in testing. There was more testing participation in the CRIS-tailored than nontailored group. Impact Asking patients questions about their specific risk factors and giving them and their providers’ information just prior to an appointment may increase participation in CRC testing. Tailoring the information has some added benefit.

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Section: Discussionmentioning

confidence: 99%

Impact of Risk Assessment and Tailored versus Nontailored Risk Information on Colorectal Cancer Testing in Primary Care: A Randomized Controlled Trial

Skinner

Halm

Bishop

et al. 2015

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…Although tailored interventions are efficacious, most studies have not tailored on a comprehensive set of variables that include baseline stage of change, demographics, and belief variables (22,23). With rapid advances in technology, our ability to develop phone or Web-based messages tailored to a larger set of variables is possible.…”

Section: Introductionmentioning

confidence: 99%

A Randomized Trial to Compare a Tailored Web-Based Intervention and Tailored Phone Counseling to Usual Care for Increasing Colorectal Cancer Screening

Champion

Christy

Rakowski

et al. 2018

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…The good news is that regular mammography screening can help to reduce mortality. Champion et al (2014) did a Computer and Phone (CAPE) study including two tailored intervention methods, mailed DVD (abbreviated as DVD) and telephone counseling (abbreviated as TC). A CAPE randomized controlled trial was conducted to determine whether the two interventions were more efficacious than the usual care (abbreviated as UC) method at promoting mammography screening among women who are non-adherent to breast cancer screening guidelines at baseline.…”

Section: Discussionmentioning

confidence: 99%

Nonparametric Estimation of Conditional Expectation with Auxiliary Information and Dimension Reduction

Xie¹,

Shao

2020

Journal of the American Statistical Association

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Nonparametric estimation of the conditional expectation E(Y |U ) of an outcome Y given a covariate vector U is of primary importance in many statistical applications such as prediction and personalized medicine. In some problems, there is an additional auxiliary variable Z in the training dataset used to construct estimators, but Z is not available for future prediction or selecting patient treatment in personalized medicine. For example, in the training dataset longitudinal outcomes are observed, but only the last outcome Y is concerned in the future prediction or analysis. The longitudinal outcomes other than the last point is then the variable Z that is observed and related with both Y and U . Previous work on how to make use of Z in the estimation of E(Y |U ) mainly focused on using Z in the construction of a linear function of U to reduce covariate dimension for better estimation. Using E(Y |U ) = E{E(Y |U, Z)|U }, we propose a two-step estimation of inner and outer expectations, respectively, with sufficient dimension reduction for kernel estimation in both steps. The information from Z is utilized not only in dimension reduction, but also directly in the estimation. Because of the existence of different ways for dimension reduction, we construct two estimators that may improve the estimator without using Z. The improvements are shown in the convergence rate of estimators as the sample size increases to infinity as well as in the finite sample simulation performance. A real data analysis about the selection of mammography intervention is presented for illustration.In many statistical applications, a key step is to estimate the conditional expectation of Y givenwhere Y is a response of interest, U is a vector of covariates, and u 0 is a given specific value of U . Apparently, the prediction of a future Y at U = u 0 is an example. Another example is in the area of personalized medicine in which we would like to maximize the condition expectation E(Y |U = u 0 , a) over several treatment options a = 1, ..., k (Qian and Murphy, 2011), where u 0 is the vector of a future patient's prognostic factors and demographic variables, and Y is his or her future outcome. Larger (or smaller) Y means better outcome. Because parametric modeling of ψ(u 0 ) is difficult in many applications such as the personalized medicine problems, nonparametric kernel estimation of ψ(u 0 ) (Nadaraya, 1964;Watson, 1964) has been widely considered and used.As shown in Theorem 2.2.2 of Bierens (1987), the optimal convergence rate of a kernel estimator is n −m/(2m+p) , where m is the order of kernel and p is the dimension of U . When p is not small, it is crucial to search for a matrix B with the smallest possible column dimension d 0 < p such thatwhere B T is the transpose of B, and hence the optimal convergence rate is improved to n −m/(2m+d 0 ) . This is usually achieved by using the training data to estimate a B with smallest column dimension such that Y ⊥ ⊥ U | B T U , i.e., Y and U are independent conditional on B T U , which is referred t...

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Randomized trial of DVD, telephone, and usual care for increasing mammography adherence

Cited by 23 publications

References 29 publications

Impact of Risk Assessment and Tailored versus Nontailored Risk Information on Colorectal Cancer Testing in Primary Care: A Randomized Controlled Trial

Impact of Risk Assessment and Tailored versus Nontailored Risk Information on Colorectal Cancer Testing in Primary Care: A Randomized Controlled Trial

A Randomized Trial to Compare a Tailored Web-Based Intervention and Tailored Phone Counseling to Usual Care for Increasing Colorectal Cancer Screening

Nonparametric Estimation of Conditional Expectation with Auxiliary Information and Dimension Reduction

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