## Search Results:

### Author: *Roderick J Little* (46)

### Nov

2017

## Medical-encounter mental health diagnoses, non-fatal injury and polypharmacy indicators of risk for accident death in the US Army enlisted soldiers, 2004-2009.

*Prev Med 2017 Nov 17. Epub 2017 Nov 17.*

Lisa Lewandowski-Romps, Heather M Schroeder, Patricia A Berglund, Lisa J Colpe, Kenneth Cox, Keith Hauret, Jeffrey D Hay, Bruce Jones, Roderick J A Little, Colter Mitchell, Michael Schoenbaum, Paul Schulz, Murray B Stein, Robert J Ursano, Steven G Heeringa,

Accidents are a leading cause of deaths in U.S. active duty personnel.

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### Dec

2017

## Correlation of Peripheral Immunity With Rapid Amyotrophic Lateral Sclerosis Progression.

*JAMA Neurol 2017 Dec;74(12):1446-1454*

Benjamin J Murdock, Tingting Zhou, Samy R Kashlan, Roderick J Little, Stephen A Goutman, Eva L Feldman

Amyotrophic lateral sclerosis (ALS) has an immune component, but previous human studies have not examined immune changes over time.

To assess peripheral inflammatory markers in participants with ALS and healthy control individuals and to track immune changes in ALS and determine whether these changes correlate with disease progression.

In this longitudinal cohort study, leukocytes were isolated from peripheral blood samples from 35 controls and 119 participants with ALS at the ALS Clinic of the University of Michigan, Ann Arbor, from June 18, 2014, through May 26, 2016.

To assess peripheral inflammatory markers in participants with ALS and healthy control individuals and to track immune changes in ALS and determine whether these changes correlate with disease progression.

In this longitudinal cohort study, leukocytes were isolated from peripheral blood samples from 35 controls and 119 participants with ALS at the ALS Clinic of the University of Michigan, Ann Arbor, from June 18, 2014, through May 26, 2016.

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### Jun

2016

## The treatment of missing data in a large cardiovascular clinical outcomes study.

*Clin Trials 2016 Jun 17;13(3):344-51. Epub 2016 Feb 17.*

Roderick J Little, Julia Wang, Xiang Sun, Hong Tian, Eun-Young Suh, Michael Lee, Troy Sarich, Leonard Oppenheimer, Alexei Plotnikov, Janet Wittes, Nancy Cook-Bruns, Paul Burton, C Michael Gibson, Surya Mohanty

The potential impact of missing data on the results of clinical trials has received heightened attention recently. A National Research Council study provides recommendations for limiting missing data in clinical trial design and conduct, and principles for analysis, including the need for sensitivity analyses to assess robustness of findings to alternative assumptions about the missing data. A Food and Drug Administration advisory committee raised missing data as a serious concern in their review of results from the ATLAS ACS 2 TIMI 51 study, a large clinical trial that assessed rivaroxaban for its ability to reduce the risk of cardiovascular death, myocardial infarction or stroke in patients with acute coronary syndrome.

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### Jul

2016

## Analyzing clinical trial outcomes based on incomplete daily diary reports.

*Stat Med 2016 Jul 16;35(17):2894-906. Epub 2016 Feb 16.*

Neal Thomas, Ofer Harel, Roderick J A Little

A case study is presented assessing the impact of missing data on the analysis of daily diary data from a study evaluating the effect of a drug for the treatment of insomnia. The primary analysis averaged daily diary values for each patient into a weekly variable. Following the commonly used approach, missing daily values within a week were ignored provided there was a minimum number of diary reports (i.

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### Jun

2015

## Discussion of "On Bayesian Estimation of Marginal Structural Models".

*Biometrics 2015 Jun 13;71(2):288-91. Epub 2015 Mar 13.*

Michael R Elliott, Roderick J Little

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### Apr

2015

## Missing not at random models for masked clinical trials with dropouts.

*Clin Trials 2015 Apr 27;12(2):139-48. Epub 2015 Jan 27.*

Shan Kang, Roderick J Little, Niko Kaciroti

Missing data are an unavoidable problem in clinical trials. Most existing missing data approaches assume the missing data are missing at random. However, the missing at random assumption is often questionable when the real causes of missing data are not well known and cannot be tested from observed data.

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### Jul

2015

## Subsample ignorable likelihood for accelerated failure time models with missing predictors.

*Lifetime Data Anal 2015 Jul 5;21(3):457-69. Epub 2014 Aug 5.*

Nanhua Zhang, Roderick J Little

Missing values in predictors are a common problem in survival analysis. In this paper, we review estimation methods for accelerated failure time models with missing predictors, and apply a new method called subsample ignorable likelihood (IL) Little and Zhang (J R Stat Soc 60:591-605, 2011) to this class of models. The approach applies a likelihood-based method to a subsample of observations that are complete on a subset of the covariates, chosen based on assumptions about the missing data mechanism.

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### Jan

2014

## Standards should be applied in the prevention and handling of missing data for patient-centered outcomes research: a systematic review and expert consensus.

*J Clin Epidemiol 2014 Jan;67(1):15-32*

Tianjing Li, Susan Hutfless, Daniel O Scharfstein, Michael J Daniels, Joseph W Hogan, Roderick J A Little, Jason A Roy, Andrew H Law, Kay Dickersin

To recommend methodological standards in the prevention and handling of missing data for primary patient-centered outcomes research (PCOR).

We searched National Library of Medicine Bookshelf and Catalog as well as regulatory agencies' and organizations' Web sites in January 2012 for guidance documents that had formal recommendations regarding missing data. We extracted the characteristics of included guidance documents and recommendations.

We searched National Library of Medicine Bookshelf and Catalog as well as regulatory agencies' and organizations' Web sites in January 2012 for guidance documents that had formal recommendations regarding missing data. We extracted the characteristics of included guidance documents and recommendations.

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### May

2013

## Regression calibration is valid when properly applied.

*Epidemiology 2013 May;24(3):467-8*

Ying Guo, Roderick J Little

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### Dec

2012

## Bayesian inference for finite population quantiles from unequal probability samples.

*Surv Methodol 2012 Dec 19;38(2):203-214. Epub 2012 Dec 19.*

Qixuan Chen, Michael R Elliott, Roderick J A Little

This paper develops two Bayesian methods for inference about finite population quantiles of continuous survey variables from unequal probability sampling. The first method estimates cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained by inverting the estimated distribution function.

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### Oct

2012

## The prevention and treatment of missing data in clinical trials.

*N Engl J Med 2012 Oct;367(14):1355-60*

Roderick J Little, Ralph D'Agostino, Michael L Cohen, Kay Dickersin, Scott S Emerson, John T Farrar, Constantine Frangakis, Joseph W Hogan, Geert Molenberghs, Susan A Murphy, James D Neaton, Andrea Rotnitzky, Daniel Scharfstein, Weichung J Shih, Jay P Siegel, Hal Stern

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### Dec

1969

## Rooting gene trees without outgroups: EP rooting.

*Genome Biol Evol 2012 16;4(8):709-19. Epub 2012 May 16.*

Janet S Sinsheimer, Roderick J A Little, James A Lake

Gene sequences are routinely used to determine the topologies of unrooted phylogenetic trees, but many of the most important questions in evolution require knowing both the topologies and the roots of trees. However, general algorithms for calculating rooted trees from gene and genomic sequences in the absence of gene paralogs are few. Using the principles of evolutionary parsimony (EP) (Lake JA.

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### Jan

2012

## On using summary statistics from an external calibration sample to correct for covariate measurement error.

*Epidemiology 2012 Jan;23(1):165-74*

Ying Guo, Roderick J Little, Daniel S McConnell

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed.

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### Sep

2012

## A pseudo-Bayesian shrinkage approach to regression with missing covariates.

*Biometrics 2012 Sep 7;68(3):933-42. Epub 2011 Dec 7.*

Nanhua Zhang, Roderick J Little

We consider the linear regression of outcome Y on regressors W and Z with some values of W missing, when our main interest is the effect of Z on Y, controlling for W. Three common approaches to regression with missing covariates are (i) complete-case analysis (CC), which discards the incomplete cases, and (ii) ignorable likelihood methods, which base inference on the likelihood based on the observed data, assuming the missing data are missing at random (Rubin, 1976b), and (iii) nonignorable modeling, which posits a joint distribution of the variables and missing data indicators. Another simple practical approach that has not received much theoretical attention is to drop the regressor variables containing missing values from the regression modeling (DV, for drop variables).

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### Oct

2011

## Evaluation of a brief tailored motivational intervention to prevent early childhood caries.

*Community Dent Oral Epidemiol 2011 Oct 29;39(5):433-48. Epub 2011 Mar 29.*

Amid I Ismail, Steven Ondersma, Jenefer M Willem Jedele, Roderick J Little, James M Lepkowski

This pragmatic randomized trial evaluated the effectiveness of a tailored educational intervention on oral health behaviors and new untreated carious lesions in low-income African-American children in Detroit, Michigan.

Participating families were recruited in a longitudinal study of the determinants of dental caries in 1021 randomly selected children (0-5 years) and their caregivers. The families were examined at baseline in 2002-2004 (Wave I), 2004-2005 (Wave II) and 2007 (Wave III).

Participating families were recruited in a longitudinal study of the determinants of dental caries in 1021 randomly selected children (0-5 years) and their caregivers. The families were examined at baseline in 2002-2004 (Wave I), 2004-2005 (Wave II) and 2007 (Wave III).

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### May

2010

## Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework.

*J R Stat Soc Ser C Appl Stat 2010 May;59(3):513-531*

Qi Long, Roderick J A Little, Xihong Lin

Data analysis for randomized trials including multi-treatment arms is often complicated by subjects who do not comply with their treatment assignment. We discuss here methods of estimating treatment efficacy for randomized trials involving multi-treatment arms subject to non-compliance. One treatment effect of interest in the presence of non-compliance is the complier average causal effect (CACE) (Angrist et al.

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### Dec

2011

## A shrinkage approach for estimating a treatment effect using intermediate biomarker data in clinical trials.

*Biometrics 2011 Dec 31;67(4):1434-41. Epub 2011 May 31.*

Yun Li, Jeremy M G Taylor, Roderick J A Little

In clinical trials, a biomarker (S ) that is measured after randomization and is strongly associated with the true endpoint (T) can often provide information about T and hence the effect of a treatment (Z ) on T. A useful biomarker can be measured earlier than T and cost less than T. In this article, we consider the use of S as an auxiliary variable and examine the information recovery from using S for estimating the treatment effect on T, when S is completely observed and T is partially observed.

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### Aug

2011

## Regression analysis with covariates that have heteroscedastic measurement error.

*Stat Med 2011 Aug 17;30(18):2278-94. Epub 2011 May 17.*

Ying Guo, Roderick J Little

We consider the estimation of the regression of an outcome Y on a covariate X, where X is unobserved, but a variable W that measures X with error is observed. A calibration sample that measures pairs of values of X and W is also available; we consider calibration samples where Y is measured (internal calibration) and not measured (external calibration). One common approach for measurement error correction is Regression Calibration (RC), which substitutes the unknown values of X by predictions from the regression of X on W estimated from the calibration sample.

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### Jun

2011

## A method for longitudinal prospective evaluation of markers for a subsequent event.

*Am J Epidemiol 2011 Jun 13;173(12):1380-7. Epub 2011 May 13.*

Roderick J Little, Matheos Yosef, Bin Nan, Siobán D Harlow

In this paper, the authors describe a simple method for making longitudinal comparisons of alternative markers of a subsequent event. The method is based on the aggregate prediction gain from knowing whether or not a marker has occurred at any particular age. An attractive feature of the method is the exact decomposition of the measure into 2 components: 1) discriminatory ability, which is the difference in the mean time to the subsequent event for individuals for whom the marker has and has not occurred, and 2) prevalence factor, which is related to the proportion of individuals who are positive for the marker at a particular age.

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### May

2011

## Bias due to left truncation and left censoring in longitudinal studies of developmental and disease processes.

*Am J Epidemiol 2011 May 21;173(9):1078-84. Epub 2011 Mar 21.*

Kevin C Cain, Siobán D Harlow, Roderick J Little, Bin Nan, Matheos Yosef, John R Taffe, Michael R Elliott

In longitudinal studies of developmental and disease processes, participants are followed prospectively with intermediate milestones identified as they occur. Frequently, studies enroll participants over a range of ages including ages at which some participants' milestones have already passed. Ages at milestones that occur prior to study entry are left censored if individuals are enrolled in the study or left truncated if they are not.

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### Nov

2010

## Longitudinal Image Analysis of Tumor/Healthy Brain Change in Contrast Uptake Induced by Radiation.

*J R Stat Soc Ser C Appl Stat 2010 Nov;59(5):821-838*

Xiaoxi Zhang, Timothy D Johnson, Roderick J A Little, Yue Cao

This work is motivated by a quantitative Magnetic Resonance Imaging study of the differential tumor/healthy tissue change in contrast uptake induced by radiation. The goal is to determine the time in which there is maximal contrast uptake (a surrogate for permeability) in the tumor relative to healthy tissue. A notable feature of the data is its spatial heterogeneity.

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### Nov

2010

## Engagement and retention: measuring breadth and depth of participant use of an online intervention.

*J Med Internet Res 2010 Nov 18;12(4):e52. Epub 2010 Nov 18.*

Mick P Couper, Gwen L Alexander, Nanhua Zhang, Roderick J A Little, Noel Maddy, Michael A Nowak, Jennifer B McClure, Josephine J Calvi, Sharon J Rolnick, Melanie A Stopponi, Christine Cole Johnson

The Internet provides us with tools (user metrics or paradata) to evaluate how users interact with online interventions. Analysis of these paradata can lead to design improvements.

The objective was to explore the qualities of online participant engagement in an online intervention.

The objective was to explore the qualities of online participant engagement in an online intervention.

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### Jul

2010

## A multiple imputation approach to disclosure limitation for high-age individuals in longitudinal studies.

*Stat Med 2010 Jul;29(17):1769-78*

Di An, Roderick J A Little, James W McNally

Disclosure limitation is an important consideration in the release of public use data sets. It is particularly challenging for longitudinal data sets, since information about an individual accumulates with repeated measures over time. Research on disclosure limitation methods for longitudinal data has been very limited.

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### Jul

2010

## Opening the black box of biomarker measurement error.

*Epidemiology 2010 Jul;21 Suppl 4:S1-3*

Enrique F Schisterman, Roderick J Little

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### Jun

2010

## Statistical aspects of The National Children's Study. Discussion.

*Stat Med 2010 Jun;29(13):1388-90*

Roderick J Little

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### Jul

2010

## How well quantified is the limit of quantification?

*Epidemiology 2010 Jul;21 Suppl 4:S10-6*

Ying Guo, Ofer Harel, Roderick J Little

Raw data on the relationship between known and measured values of an analyte are collected and analyzed to determine the limit of quantification (LOQ) of an assay. In most LOQ problems, the researcher is given an observed value for the marker of interest if this value is greater than the LOQ, and a missing value (

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### Jun

2010

## Bayesian penalized spline model-based inference for finite population proportion in unequal probability sampling.

*Surv Methodol 2010 Jun 29;36(1):23-34. Epub 2010 Jun 29.*

Qixuan Chen, Michael R Elliott, Roderick J A Little

We propose a Bayesian Penalized Spline Predictive (BPSP) estimator for a finite population proportion in an unequal probability sampling setting. This new method allows the probabilities of inclusion to be directly incorporated into the estimation of a population proportion, using a probit regression of the binary outcome on the penalized spline of the inclusion probabilities. The posterior predictive distribution of the population proportion is obtained using Gibbs sampling.

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### Dec

1969

## A Bayesian Image Analysis of Radiation Induced Changes in Tumor Vascular Permeability.

*Bayesian Anal 2010 ;5(1):189-212*

Xiaoxi Zhang, Timothy D Johnson, Roderick J A Little, Yue Cao

This work is motivated by a quantitative Magnetic Resonance Imaging study of the relative change in tumor vascular permeability during the course of radiation therapy. The differences in tumor and healthy brain tissue physiology and pathology constitute a notable feature of the image data-spatial heterogeneity with respect to its contrast uptake profile (a surrogate for permeability) and radiation induced changes in this profile. To account for these spatial aspects of the data, we employ a Gaussian hidden Markov random field (MRF) model.

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### Apr

2010

## A Review of Hot Deck Imputation for Survey Non-response.

*Int Stat Rev 2010 Apr;78(1):40-64*

Rebecca R Andridge, Roderick J A Little

Hot deck imputation is a method for handling missing data in which each missing value is replaced with an observed response from a "similar" unit. Despite being used extensively in practice, the theory is not as well developed as that of other imputation methods. We have found that no consensus exists as to the best way to apply the hot deck and obtain inferences from the completed data set.

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### Feb

2010

## An evaluation of asthma interventions for preteen students.

*J Sch Health 2010 Feb;80(2):80-7*

Noreen M Clark, Smita Shah, Julia A Dodge, Lara J Thomas, Rebecca R Andridge, Roderick J A Little

Asthma is a serious problem for low-income preteens living in disadvantaged communities. Among the chronic diseases of childhood and adolescence, asthma has the highest prevalence and related health care use. School-based asthma interventions have proven successful for older and younger students, but results have not been demonstrated for those in middle school.

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### Jul

2010

## Estimation of background serum 2,3,7,8-TCDD concentrations by using quantile regression in the UMDES and NHANES populations.

*Epidemiology 2010 Jul;21 Suppl 4:S51-7*

Qixuan Chen, David H Garabrant, Elizabeth Hedgeman, Roderick J A Little, Michael R Elliott, Brenda Gillespie, Biling Hong, Shih-Yuan Lee, James M Lepkowski, Alfred Franzblau, Peter Adriaens, Avery H Demond, Donald G Patterson

The goal of the present study was to quantify the population-based background serum concentrations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) by using data from the reference population of the 2005 University of Michigan Dioxin Exposure Study (UMDES) and the 2003-2004 National Health and Nutrition Examination Survey (NHANES).

Multiple imputation was used to impute the serum TCDD concentrations below the limit of detection by combining the 2 data sources. The background mean, quartiles, and 95th percentile serum TCDD concentrations were estimated by age and sex by using linear and quantile regressions for complex survey data.

Multiple imputation was used to impute the serum TCDD concentrations below the limit of detection by combining the 2 data sources. The background mean, quartiles, and 95th percentile serum TCDD concentrations were estimated by age and sex by using linear and quantile regressions for complex survey data.

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### Nov

2009

## The existence of cognitive plateaus in Alzheimer's disease.

*Alzheimers Dement 2009 Nov;5(6):470-8*

Andrea C Bozoki, Hyonggin An, Eva S Bozoki, Roderick J Little

The objective of this study was to evaluate the existence of cognitive plateaus in some individuals during the course of Alzheimer's disease (AD).

Data came from the historical patient group collected via the Consortium to Establish a Registry for Alzheimer's Disease (CERAD, Duke University, 1988-1996). Data reduction was performed by using principal components analysis to derive a single cognitive measure (F1), followed by application of a novel plateau-searching algorithm to individual patient data, looking for stable periods of 3 years or longer.

Data came from the historical patient group collected via the Consortium to Establish a Registry for Alzheimer's Disease (CERAD, Duke University, 1988-1996). Data reduction was performed by using principal components analysis to derive a single cognitive measure (F1), followed by application of a novel plateau-searching algorithm to individual patient data, looking for stable periods of 3 years or longer.

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### Dec

1969

## The Use of Sample Weights in Hot Deck Imputation.

*J Off Stat 2009 ;25(1):21-36*

Rebecca R Andridge, Roderick J Little

A common strategy for handling item nonresponse in survey sampling is hot deck imputation, where each missing value is replaced with an observed response from a "similar" unit. We discuss here the use of sampling weights in the hot deck. The naive approach is to ignore sample weights in creation of adjustment cells, which effectively imputes the unweighted sample distribution of respondents in an adjustment cell, potentially causing bias.

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### Jun

2009

## Mixed-effect hybrid models for longitudinal data with nonignorable dropout.

*Biometrics 2009 Jun;65(2):478-86*

Ying Yuan, Roderick J A Little

Selection models and pattern-mixture models are often used to deal with nonignorable dropout in longitudinal studies. These two classes of models are based on different factorizations of the joint distribution of the outcome process and the dropout process. We consider a new class of models, called mixed-effect hybrid models (MEHMs), where the joint distribution of the outcome process and dropout process is factorized into the marginal distribution of random effects, the dropout process conditional on random effects, and the outcome process conditional on dropout patterns and random effects.

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### Jun

2009

## Meta-analysis of studies with missing data.

*Biometrics 2009 Jun 18;65(2):487-96. Epub 2008 May 18.*

Ying Yuan, Roderick J A Little

Consider a meta-analysis of studies with varying proportions of patient-level missing data, and assume that each primary study has made certain missing data adjustments so that the reported estimates of treatment effect size and variance are valid. These estimates of treatment effects can be combined across studies by standard meta-analytic methods, employing a random-effects model to account for heterogeneity across studies. However, we note that a meta-analysis based on the standard random-effects model will lead to biased estimates when the attrition rates of primary studies depend on the size of the underlying study-level treatment effect.

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### Sep

2008

## The effect of patient choice of intervention on health outcomes.

*Contemp Clin Trials 2008 Sep 20;29(5):679-86. Epub 2008 Apr 20.*

Noreen M Clark, Nancy K Janz, Julia A Dodge, Lori Mosca, Xihong Lin, Qi Long, Roderick J Little, John R C Wheeler, Steven Keteyian, Jersey Liang

Patient preference may influence intervention effects, but has not been extensively studied. Randomized controlled design (N=1075) assessed outcomes when women (60 years+) were given a choice of two formats of a program to enhance heart disease management.

Randomization to "no choice" or "choice" study arms.

Randomization to "no choice" or "choice" study arms.

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### Jun

2009

## A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance.

*Biometrics 2009 Jun 28;65(2):640-9. Epub 2008 May 28.*

Roderick J Little, Qi Long, Xihong Lin

We consider the analysis of clinical trials that involve randomization to an active treatment (T = 1) or a control treatment (T = 0), when the active treatment is subject to all-or-nothing compliance. We compare three approaches to estimating treatment efficacy in this situation: as-treated analysis, per-protocol analysis, and instrumental variable (IV) estimation, where the treatment effect is estimated using the randomization indicator as an IV. Both model- and method-of-moment based IV estimators are considered.

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### May

2008

## Web-based smoking-cessation programs: results of a randomized trial.

*Am J Prev Med 2008 May;34(5):373-81*

Victor J Strecher, Jennifer B McClure, Gwen L Alexander, Bibhas Chakraborty, Vijay N Nair, Janine M Konkel, Sarah M Greene, Linda M Collins, Carola C Carlier, Cheryl J Wiese, Roderick J Little, Cynthia S Pomerleau, Ovide F Pomerleau

Initial trials of web-based smoking-cessation programs have generally been promising. The active components of these programs, however, are not well understood. This study aimed to (1) identify active psychosocial and communication components of a web-based smoking-cessation intervention and (2) examine the impact of increasing the tailoring depth on smoking cessation.

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### Dec

2007

## EMG variance during polysomnography as an assessment for REM sleep behavior disorder.

*Sleep 2007 Dec;30(12):1771-8*

Joseph W Burns, Flavia B Consens, Roderick J Little, Karen J Angell, Sid Gilman, Ronald D Chervin

In a previous study, we validated a polysomnographic assessment for REM sleep behavior disorder (RBD). The method proved to be reliable but required slow, labor-intensive visual scoring of surface electromyogram (EMG) activity. We therefore developed a computerized metric to assess EMG variance and compared the results to those previously published for visual scoring, bed partner-rated RBD symptom scores, and clinical assessments by sleep medicine specialists.

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### Jan

2008

## QUANTITATIVE MAGNETIC RESONANCE IMAGE ANALYSIS VIA THE EM ALGORITHM WITH STOCHASTIC VARIATION.

*Ann Appl Stat 2008 Jan;2(2):736-735*

Xiaoxi Zhang, Timothy D Johnson, Roderick J A Little, Yue Cao

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response.

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### Jan

2008

## A hot-deck multiple imputation procedure for gaps in longitudinal data on recurrent events.

*Stat Med 2008 Jan;27(1):103-20*

Roderick J Little, Matheos Yosef, Kevin C Cain, Bin Nan, Siobán D Harlow

We consider the analysis of longitudinal data sets that include times of recurrent events, where interest lies in variables that are functions of the number of events and the time intervals between events for each individual, and where some cases have gaps when the information was not recorded. Discarding cases with gaps results in a loss of the recorded information in those cases. Other strategies such as simply splicing together the intervals before and after the gap potentially lead to bias.

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### Dec

2007

## Parametric and semiparametric model-based estimates of the finite population mean for two-stage cluster samples with item nonresponse.

*Biometrics 2007 Dec 8;63(4):1172-80. Epub 2007 May 8.*

Ying Yuan, Roderick J A Little

This article concerns item nonresponse adjustment for two-stage cluster samples. Specifically, we focus on two types of nonignorable nonresponse: nonresponse depending on covariates and underlying cluster characteristics, and depending on covariates and the missing outcome. In these circumstances, standard weighting and imputation adjustments are liable to be biased.

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### Dec

1969

## Factors that influence successful training and faculty career development in hematology/oncology patient-oriented clinical research.

*J Cancer Educ 2005 ;20(2):72-8*

Scott D Gitlin, Zheng Yuan, Roderick J Little, Robert F Todd Iii

We wanted to identify what factors promote career development in patient-oriented clinical research (POCR).

We used a survey questionnaire covering areas relevant to the training of subspecialty fellows and the career development of POCR faculty.

Pursuit of an academic career after fellowship correlated with completion of a clinical project, availability of a clinical research training program, opportunity to earn academic credit, faculty mentorship, and obtaining independent career development funding.

We used a survey questionnaire covering areas relevant to the training of subspecialty fellows and the career development of POCR faculty.

Pursuit of an academic career after fellowship correlated with completion of a clinical project, availability of a clinical research training program, opportunity to earn academic credit, faculty mentorship, and obtaining independent career development funding.

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### Sep

2004

## An extended general location model for causal inferences from data subject to noncompliance and missing values.

*Biometrics 2004 Sep;60(3):598-607*

Yahong Peng, Roderick J A Little, Trivellore E Raghunathan

Noncompliance is a common problem in experiments involving randomized assignment of treatments, and standard analyses based on intention-to-treat or treatment received have limitations. An attractive alternative is to estimate the Complier-Average Causal Effect (CACE), which is the average treatment effect for the subpopulation of subjects who would comply under either treatment (Angrist, Imbens, and Rubin, 1996, Journal of American Statistical Association 91, 444-472). We propose an extended general location model to estimate the CACE from data with noncompliance and missing data in the outcome and in baseline covariates.

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### Apr

2004

## Change in estradiol and follicle-stimulating hormone across the early menopausal transition: effects of ethnicity and age.

*J Clin Endocrinol Metab 2004 Apr;89(4):1555-61*

John F Randolph, MaryFran Sowers, Irina V Bondarenko, Siobán D Harlow, Judith L Luborsky, Roderick J Little

Serum reproductive hormone concentrations were measured longitudinally in a community-based, multiethnic population of midlife women to assess whether ethnic differences exist in the patterns of change in estradiol (E2) and FSH and, if so, whether these differences are explained by host characteristics. We studied 3257 participants from seven clinical sites in the Study of Women's Health Across the Nation (SWAN) who were aged 42-52 yr at baseline and self-identified as African American (28.2%), Caucasian (47.

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### May

2003

## On weighting the rates in non-response weights.

*Stat Med 2003 May;22(9):1589-99*

Roderick J Little, Sonya Vartivarian

A basic estimation strategy in sample surveys is to weight units inversely proportional to the probability of selection and response. Response weights in this method are usually estimated by the inverse of the sample-weighted response rate in an adjustment cell, that is, the ratio of the sum of the sampling weights of respondents in a cell to the sum of the sampling weights for respondents and non-respondents in that cell. We show by simulations that weighting the response rates by the sampling weights to adjust for design variables is either incorrect or unnecessary.

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