Paper published: Allergies, atopy, immune-related factors and childhood rhabdomyosarcoma: A report from the Children’s Oncology Group

Allergies, atopy, immune-related factors and childhood rhabdomyosarcoma: A report from the Children’s Oncology Group Lupo, PJ, R Zhou, SX Skapek, DS Hawkins, LG Specor, ME Scheurer, B Melin, K Papworth, EB Erhardt, and S Grufferman International Journal of Cancer 134 (2). pdf, pp. 431–436 Online: August 1, 2013 http://onlinelibrary.wiley.com/doi/10.1002/ijc.28363/abstract DOI: 10.1002/ijc.28363 Abstract Rhabdomyosarcoma (RMS) is a highly malignant tumor of developing muscle that can occur anywhere in the body. Due to its rarity, relatively little is known about the epidemiology of RMS. Atopic disease is hypothesized to be protective against several malignancies; however, to our knowledge, there have been no assessments of atopy and childhood RMS. Therefore, we explored this association in a case-control study of 322 childhood RMS cases and 322 pair-matched controls. Cases were enrolled in a trial run by the Intergroup Rhabdomyosarcoma Study Group. Controls were matched to cases on race, sex and age. The following atopic conditions were assessed: allergies, asthma, eczema and hives; in addition, we examined other immune-related factors: birth order, day-care attendance and breastfeeding. Conditional logistic-regression models were used to calculate an odds ratio (OR) and 95% confidence interval (CI) for each exposure, adjusted for age, race, sex, household income and parental education. As the two most common histologic types of RMS are embryonal (n = 215) and alveolar (n = 66), we evaluated effect heterogeneity of these exposures. Allergies (OR = 0.60, 95% CI: 0.41–0.87), hives (OR = 0.61, 95% CI: 0.38–0.97), day-care attendance (OR = 0.48, 95% CI: 0.32–0.71) and breastfeeding for ≥ 12 months (OR = 0.36, 95% CI: 0.18–0.70) were inversely associated with childhood RMS. These exposures did not display significant effect heterogeneity between histologic types (p > 0.52 for all exposures). This is the first study indicating that atopic exposures may be protective against childhood RMS, suggesting additional studies are needed to evaluate the immune system’s role in the development of this tumor.
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Paper published: Negotiating for improved reimbursement for Dialectical Behavior Therapy: A successful project

Negotiating for improved reimbursement for Dialectical Behavior Therapy: A successful project Cedar R. Koons, Beth O’Rourke, Barbara Carter, Erik B. Erhardt Cognitive and Behavioral Practice Received: 25 May 2012 Accepted: 18 January 2013 Online: 1 March 2013 DOI: 10.1016/j.cbpra.2013.01.003 Abstract Dialectical Behavior Therapy (DBT) is an evidence-based treatment for borderline personality disorder that has been widely disseminated to many outpatient treatment settings. Many practitioners depend on third-party payers to fund treatment delivery. DBT requires additional clinical services not often included in outpatient therapy, including a weekly skills group led by 2 clinicians, and the requirement for clinicians to attend weekly consultation team and provide intersession contact for coaching. Standard outpatient insurance rates for individual and group sessions do not provide adequate reimbursement for the additional services of DBT. This paper describes how two DBT team leaders collaborated to obtain improved reimbursement for their programs. The two teams met with insurers, educated them about DBT, and showed outcomes from their programs to achieve large increases in reimbursement rates. The paper includes client outcome data from both programs.
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TV: KOB4, Police cadet test scores under investigation

Tonight KOB-TV4 aired the NM Law Enforcement Academy “Police cadet test scores under investigation” story on the Eyewitness News 4 at 10 P.M., for which I gave a short interview to Gadi Schwartz using a plot I created from the test score data. I gave the information and interview out of a personal desire to be helpful and was not acting on the University’s behalf. I did not speculate on the cause for the outlying class’s scores. I value the men and women who risk their lives daily serving our communities.
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Plot improved: NM Registered voters 2008

Showing party affiliation by age group can be made more informative by representing voter power with census data.  Note that in the “before” plot, years 60+ appear to be almost half the plot width while in the “after” plot we see that 60+ only represent 25% of the voting pool. Before After R code to create the “after” plot follows.
# Erik B. Erhardt
# 4/28/2012

# Recreating this plot as a Marimekko mosaic chart
# NM Registered Voters - Party by Age Line Chart (Oct 2008)
# http://rpinc.com/wb/media/reports/Party%20by%20age%20line%20chart%20-%202008-10.pdf

# Census population sizes
# NM population numbers
# http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_SF1_QTP1&prodType=table

ages <- c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54",
          "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-99")
pop.ages <- c(149861,142370,139678,127567,123303,125220,144839,147170,
              136799,120137, 87890, 65904, 50230, 36238, 21622, 10371)

age <- seq(18,99)
pop <- c(rep(pop.ages/5,each=5)[c(4:75)], rep(pop.ages[length(pop.ages)]/10,10))
pop.prop <- pop/sum(pop)

# datathief http://rpinc.com/wb/media/reports/Party%20by%20age%20line%20chart%20-%202008-10.pdf

dem <- c(0.46,0.46,0.45,0.44,0.40,0.41,0.42,0.43,0.43,0.44,0.46
        ,0.46,0.46,0.46,0.47,0.48,0.48,0.48,0.48,0.48,0.48,0.48
        ,0.48,0.48,0.49,0.49,0.49,0.49,0.49,0.49,0.49,0.50,0.50
        ,0.51,0.52,0.53,0.52,0.53,0.54,0.55,0.55,0.55,0.55,0.54
        ,0.54,0.55,0.54,0.54,0.53,0.55,0.55,0.55,0.55,0.55,0.55
        ,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.58,0.58,0.57,0.57
        ,0.57,0.57,0.56,0.58,0.56,0.58,0.58,0.58,0.59,0.59,0.61
        ,0.59,0.62,0.61,0.62,0.60)

rep <- c(0.24,0.25,0.26,0.28,0.28,0.27,0.27,0.27,0.27,0.27,0.26
        ,0.27,0.27,0.28,0.28,0.28,0.29,0.30,0.31,0.31,0.32,0.32
        ,0.33,0.33,0.33,0.33,0.34,0.34,0.34,0.35,0.35,0.35,0.34
        ,0.34,0.34,0.33,0.33,0.33,0.32,0.31,0.31,0.31,0.31,0.32
        ,0.33,0.32,0.34,0.34,0.35,0.34,0.35,0.35,0.35
        ,0.35,0.35,0.35,0.35,0.36,0.36,0.36,0.36,0.35,0.34,0.34
        ,0.35,0.35,0.34,0.34,0.36,0.35,0.36,0.35,0.34,0.35,0.34
        ,0.34,0.33,0.34,0.33,0.32,0.30,0.31)

dts  <- c(0.26,0.25,0.25,0.24,0.30,0.29,0.28
        ,0.28,0.26,0.26,0.24,0.24,0.23,0.23,0.21,0.20,0.19,0.19
        ,0.18,0.18,0.17,0.17,0.16,0.16,0.15,0.15,0.14,0.15,0.14
        ,0.14,0.13,0.13,0.13,0.12,0.12,0.12,0.12,0.12,0.12,0.12
        ,0.12,0.12,0.12,0.12,0.11,0.11,0.10,0.10,0.11,0.10,0.09
        ,0.09,0.09,0.08,0.08,0.08,0.08,0.07,0.07,0.07,0.07,0.07
        ,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.06,0.06
        ,0.07,0.07,0.07,0.06,0.06,0.04,0.06,0.05,0.06)

other <- c(0.05,0.05,0.05,0.05,0.03,0.03,0.04,0.04,0.04,0.04,0.04
          ,0.04,0.04,0.04,0.04,0.04,0.04,0.03,0.03,0.03,0.03,0.03
          ,0.03,0.03,0.03,0.03,0.03,0.02,0.03,0.03,0.03,0.03,0.03
          ,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03
          ,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
          ,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.01
          ,0.02,0.02,0.01,0.01,0.02,0.01,0.01,0.02,0.01,0.02,0.01
          ,0.01,0.01,0.00,0.01,0.02)

all <- data.frame(dem, rep, dts, other)
rowSums(all)

# correct rounding errors from datathief
for (i in 1:length(age)) {
  all[i,] <- all[i,]/sum(all[i,]);
}
rowSums(all)

## getting data list above
# x <- scan()
# [datathief numbers]
#
# round(matrix(x,ncol=2,byrow=TRUE)[,2],2)
# plot(round(matrix(x,ncol=2,byrow=TRUE)[,1],0))

# following example from http://learnr.wordpress.com/2009/03/29/ggplot2_marimekko_mosaic_chart/

################################################################################
df <- data.frame(
          segment = age
        , segpct = pop.prop * 100
        , Other = all$other * 100
        , DTS  = all$dts    * 100
        , Rep = all$rep     * 100
        , Dem = all$dem     * 100
      )

df$xmax <- cumsum(df$segpct)
df$xmin <- df$xmax - df$segpct
df$segpct <- NULL

library(ggplot2)
library(reshape)

dfm <- melt(df, id = c("segment", "xmin", "xmax"))

dfm1 <- ddply(dfm , .(segment), transform, ymax = cumsum(value))
dfm1 <- ddply(dfm1, .(segment), transform, ymin = ymax - value)

dfm1$xtext <- with(dfm1, xmin + (xmax - xmin)/2)
dfm1$ytext <- with(dfm1, ymin + (ymax - ymin)/2)

dfm1$segmentlabel <- rep("",length(dfm1$segment))
ss <- ((dfm1$segment %% 5)==0); # every 5 years, display age
dfm1$segmentlabel[ss] <- dfm1$segment[ss]
dfm1$segmentlabel[(dfm1$segment==18)] <- "age"

p <- ggplot(dfm1, aes(ymin = ymin, ymax = ymax, xmin = xmin, xmax = xmax, fill = variable))

p <- p + geom_rect(colour = I("grey"), alpha=0.75, size=.01) +
      xlab("Percentage age distribution") +
      ylab("Percent registered voter for party by age") +
      labs(title="NM Registered Voters - Party by Age (Oct 2008)")

p <- p + geom_text(aes(x = xtext, y = ytext,
     label = ifelse(segment == 20, paste(variable), " ")), size = 3.5)

p <- p + geom_text(aes(x = xtext, y = -3, label = paste(dfm1$segmentlabel)), size = 3)
p

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Paper Published: Tracking whole-brain connectivity dynamics in the resting-state

Tracking Whole-Brain Connectivity Dynamics in the Resting State Elena A. Allen, Eswar Damaraju, Sergey M. Plis, Erik B. Erhardt, Tom Eichele, and Vince D. Calhoun Cerebral Cortex Received: July 24, 2012 Accepted: October 15, 2012 Online: November 11, 2012 http://cercor.oxfordjournals.org/content/early/2012/11/09/cercor.bhs352.abstract doi: 10.1093/cercor/bhs352 Abstract Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.
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Invited talks: Neuroimaging and Statistics at Wright State University, Dayton, OH

I just returned from a fun event-filled couple days at Wright State Univeristy in Dayton, Ohio, visiting statistician Harry Khamis.  Harry invited me to give two talks on Friday, November 2nd, 2012: one in Statistics and a second in Neuroscience, arranged by Thomas N. Hangartner.  Harry was the model host; I always felt taken care of, my needs met. I was excited to meet two people from my talks who could use the methods I presented. Prof Nasser H Kashou develops models for HRF functions, which the SimTB might be helpful for. Prof Yvonne Vadeboncoeur uses stable isotopes to study freshwater ecosystems, and we had some exciting discussion about collaborative opportunities. The links to the papers the talks draw on are at the bottom. My morning neuroimaging talk (10:15) in the Department of Biomedical, Industrial & Human Factors Engineering (BIE) included two-and-one-half topics: SimTB, subject variability with GICA, and a little data visualization.
Title Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study Abstract A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it affected by model order? (3) How should we analyze component features to maximize sensitivity to intersubject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data. mialab.mrn.org/software/simtb
My afternoon statistics talk (3:00) in the Department of Mathematics and Statistics to a packed room (they had to bring in additional chairs!) included work that extends my published stable isotope sourcing work.
Title An extended Bayesian stable isotope mixing model for trophic level inference Abstract You are what and where you eat on the food web. We developed an extended Bayesian mixing model to jointly infer organic matter utilization and isotopic enrichment of organic matter sources in order to infer the trophic levels of several numerically abundant fish species (consumers) present in Apalachicola Bay, FL, USA. Bayesian methods apply for arbitrary numbers of isotopes and diet sources but existing models are somewhat limited as they assume that trophic fractionation is estimated without error or that isotope ratios are uncorrelated. The model uses stable isotope ratios of carbon, nitrogen, and sulfur, isotopic fractionations, elemental concentrations, elemental assimilation efficiencies, as well as prior information (expert opinion) to inform the diet and trophic level parameters. The model appropriately accounts for uncertainly and prior information at all levels of the analysis.
Neuroscience talk Summary of both SimTB papers. SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability Erik B. Erhardt, Elena A. Allen, Yonghua Wei, Tom Eichele, Vince D. Calhoun NeuroImage 59 (2012), pp. 4160-4167 http://www.sciencedirect.com/science/article/pii/S105381191101370X Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study Elena A. Allen, Erik B. Erhardt, Yonghua Wei, Tom Eichele, Vince D. Calhoun NeuroImage http://www.sciencedirect.com/science/article/pii/S1053811911011712 Data visualization in the neurosciences: overcoming the curse of dimensionality Elena A. Allen, Erik B. Erhardt, Vince D. Calhoun Neuron www.cell.com/neuron/retrieve/pii/S089662731200428X Statistics talk A Bayesian framework for stable isotope mixing models Erik B. Erhardt and Edward J. Bedrick Environmental and Ecological Statistics http://www.springerlink.com/content/vg4v62j8717671p3/ Bio Erik Barry Erhardt, PhD, is an Assistant Professor of Statistics at the University of New Mexico Department of Mathematics and Statistics, where he serves as Director of the statistics consulting clinic. His research interests include Bayesian and frequentist statistical methods for stable isotope sourcing and brain imaging. Erik is a Howard Hughes Medical Institute Interfaces Scholar collaborating in interdisciplinary research and consulting. StatAcumen.com
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Paper published: Bayesian Simultaneous Intervals for Small Areas: An Application to Variation in Maps

Bayesian Simultaneous Intervals for Small Areas: An Application to Variation in Maps Erik Barry Erhardt, Balgobin Nandram, Jai Won Choi International Journal of Statistics and Probability Vol 1, No 2, pp. 229–243 Received: September 19, 2012 Accepted: October 24, 2012 Online: October 29, 2012 http://www.ccsenet.org/journal/index.php/ijsp/article/view/20714 doi:10.5539/ijsp.v1n2p229 Abstract Bayesian inference about small areas is of considerable current interest, and simultaneous intervals for the parameters for the areas are needed because these parameters are correlated. This is not usually pursued because with many areas the problem becomes difficult. We describe a method for finding simultaneous credible intervals for a relatively large number of parameters, each corresponding to a single area. Our method is model based, it uses a hierarchical Bayesian model, and it starts with either the 100(1-alpha)% (e.g., alpha=0.05 for 95%) credible interval or highest posterior density (HPD) interval for each area. As in the construction of the HPD interval, our method is the result of the solution of two simultaneous equations, an equation that accounts for the probability content, 100(1-alpha)% of all the intervals combined, and an equation that contains an optimality condition like the “equal ordinates” condition in the HPD interval. We compare our method with one based on a nonparametric method, which as expected under a parametric model, does not perform as well as ours, but is a good competitor. We illustrate our method and compare it with the nonparametric method using an example on disease mapping which utilizes a standard Poisson regression model.
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Paper published: A Bayesian framework for stable isotope mixing models

A Bayesian framework for stable isotope mixing models Erik B. Erhardt and Edward J. Bedrick Environmental and Ecological Statistics Submitted 19 February 2011 Accepted 28 September 2012 Online 23 October 2012 http://www.springerlink.com/content/vg4v62j8717671p3/ DOI 10.1007/s10651-012-0224-1 Abstract: Stable isotope sourcing is used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets and plant nutrient use. Statistical methods for inference on the diet proportions using stable isotopes have focused on the linear mixing model. Existing frequentist methods provide inferences when the diet proportion vector can be uniquely solved for in terms of the isotope ratios. Bayesian methods apply for arbitrary numbers of isotopes and diet sources but existing models are somewhat limited as they assume that trophic fractionation or discrimination is estimated without error or that isotope ratios are uncorrelated. We present a Bayesian model for the estimation of mean diet that accounts for uncertainty in source means and discrimination and allows correlated isotope ratios. This model is easily extended to allow the diet proportion vector to depend on covariates, such as time. Two data sets are used to illustrate the methodology. Code is available for selected analyses.
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Pinewoods Swing into Summer, 2012, a better Hambo workshop

I’ve been lucky! I was invited to teach couples dance at five of the six dance and music camps in my summer schedule (AZ, NM, MA, CA, MA, AZ) and was invited to give a day-long waltz workshop in Tucson, AZ!  I can hardly believe it.  To Eric Black and Diane Zingale, who both believed in me to lead couples dance at AmWeek 2011 and got me started (and invited me back for AmWeek 2012), I have much gratitude!  Thanks also to all the organizers (Deb Comly, Lisa Bertelli, Chuck Gordon, and Eric Black) who trusted me to create a fun and engaging learning experience.

A better Hambo workshop

Chuck Gordon invited me to teach Hambo with Heather Carmichael at Pinewoods, Swing into Summer. The two one-hour sessions, and the support of several dance angels (experienced hambo-ers), absolutely helped the success of the workshop. My experience is that a single one-hour workshop (or even 1:15) isn’t quite enough to get the dance into the feet of the dancers.  But one hour to get solid on the components and their synthesis (Dreyfus model levels 2 or 3, advanced beginner or competency), a night of rest and maybe practicing the turn in free moments, then a second day of dancing over and over with many dancers gives enough time and thought to ascend to level 4 (proficiency).  I think even two 45-minute session are preferable to one 1:30 workshop or even a single 2-hour workshop. From the experience, I still feel engaged, joyful, and inspired because of the direct and special way I could connect in the community, watch the dancers grow in the two hours, and facilitate the mutual nurturing and compassion between the dancers as many went from “what’s hambo?” to, “let’s dance”! Heather and I thank Emily Troll, Mary Lea, and Julie Vallimont who played a series of lovely tunes as we all got to dance as a mixer for a solid 40 minutes of the second hour — my most successful hambo workshop!
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Acumen in Statistics