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1.  Questionnaire and laboratory measures of eating behavior: Associations with energy intake and BMI in a community sample of working adults 
Appetite  2013;72:50-58.
The present research compared a self-report measure of usual eating behaviors with two laboratory-based behavioral measures of food reward and food preference.
Eating behaviors were measured among 233 working adults. A self-report measure was the Three Factor Eating Questionnaire (TFEQ) Restraint, Disinhibition and Hunger subscales. Laboratory measures were the (RVF) and Explicit Liking (EL) and Implicit Wanting (IW) for high fat food. Outcome measures were body mass index (BMI), and energy intake measured using three 24-hour dietary recalls.
Significant bivariate associations were observed between each of the eating behavior measures and energy intake, but only Disinhibition and Hunger were associated with BMI. Multiple regression results showed RVF and EL and IW predicted energy intake independent of the TFEQ scales but did not predict BMI.
Laboratory and self-report measures capture unique aspects of individual differences in eating behaviors that are associated with energy intake.
PMCID: PMC3893825  PMID: 24096082
2.  Portion size effects on weight gain in a free living setting 
Obesity (Silver Spring, Md.)  2014;22(6):1400-1405.
Examine the effect of weekday exposure over six months to different lunch sizes on energy intake and body weight in a free-living sample of working adults.
Design and Methods
Adults (n=233) were randomly assigned to one of three lunch size groups (400 kcal; 800 kcal; 1600 kcal) or to a no-free lunch control group for six months. Weight and energy intake were measured at baseline, and months 1, 3, and 6.
Lunch energy was significantly higher in the 800 and 1600 kcal groups compared to the 400 kcal group (p < 0.0001). Total energy was significantly higher for the 1600 kcal group compared to the 400 and 800 kcal groups (p = 0.02). Body weight change at six months did not significantly differ at the 5% level by experimental group (1600 kcal group: +1.1 kg (sd=0.44); 800 kcal group: −0.1 kg (sd=0.42); 400 kcal group: −0.1 kg (sd=0.43); control group: 1.1 (sd=0.42); p=.07). Weight gain over time was significant in the 1600 kcal box lunch group (p < 0.05).
Weekday exposure for six months to a 1600 kcal lunch caused significant increases in total energy intake and weight gain.
PMCID: PMC4037334  PMID: 24510841
portion size; energy intake; weight gain
3.  Hard, harder, hardest: principal stratification, statistical identifiability, and the inherent difficulty of finding surrogate endpoints 
In many areas of clinical investigation there is great interest in identifying and validating surrogate endpoints, biomarkers that can be measured a relatively short time after a treatment has been administered and that can reliably predict the effect of treatment on the clinical outcome of interest. However, despite dramatic advances in the ability to measure biomarkers, the recent history of clinical research is littered with failed surrogates. In this paper, we present a statistical perspective on why identifying surrogate endpoints is so difficult. We view the problem from the framework of causal inference, with a particular focus on the technique of principal stratification (PS), an approach which is appealing because the resulting estimands are not biased by unmeasured confounding. In many settings, PS estimands are not statistically identifiable and their degree of non-identifiability can be thought of as representing the statistical difficulty of assessing the surrogate value of a biomarker. In this work, we examine the identifiability issue and present key simplifying assumptions and enhanced study designs that enable the partial or full identification of PS estimands. We also present example situations where these assumptions and designs may or may not be feasible, providing insight into the problem characteristics which make the statistical evaluation of surrogate endpoints so challenging.
PMCID: PMC4171402  PMID: 25342953
Surrogate endpoint; Principal stratification; Causal inference; Statistical identifiability
4.  Adjudicated Morbidity and Mortality Outcomes by Age among Individuals with HIV Infection on Suppressive Antiretroviral Therapy 
PLoS ONE  2014;9(4):e95061.
Non-AIDS conditions such as cardiovascular disease and non-AIDS defining cancers dominate causes of morbidity and mortality among persons with HIV on suppressive combination antiretroviral therapy. Accurate estimates of disease incidence and of risk factors for these conditions are important in planning preventative efforts.
With use of medical records, serious non-AIDS events, AIDS events, and causes of death were adjudicated using pre-specified criteria by an Endpoint Review Committee in two large international trials. Rates of serious non-AIDS which include cardiovascular disease, end-stage renal disease, decompensated liver disease, and non-AIDS cancer, and other serious (grade 4) adverse events were determined, overall and by age, over a median follow-up of 4.3 years for 3,570 participants with CD4+ cell count ≥300 cells/mm3 who were taking antiretroviral therapy and had an HIV RNA level ≤500 copies/mL. Cox models were used to examine the effect of age and other baseline factors on risk of a composite outcome of all-cause mortality, AIDS, or serious non-AIDS.
Five-year Kaplan-Meier estimates of the composite outcome, overall and by age were 8.3% (overall), 3.6% (<40), 8.7% (40–49) and 16.1% (≥50), respectively (p<0.001). In addition to age, smoking and higher levels of interleukin-6 and D-dimer were significant predictors of the composite outcome. The composite outcome was dominated by serious non-AIDS events (overall 65% of 277 participants with a composite event). Most serious non-AIDS events were due to cardiovascular disease and non-AIDS cancers.
To date, few large studies have carefully collected data on serious non-AIDS outcomes. Thus, reliable estimates of event rates are scarce. Data cited here, from a geographically diverse cohort, will be useful for planning studies of interventions aimed at reducing rates of serious non-AIDS events among people with HIV.
PMCID: PMC3984283  PMID: 24728071
5.  Design and Estimation for Evaluating Principal Surrogate Markers in Vaccine Trials 
Biometrics  2013;69(2):301-309.
In vaccine research, immune biomarkers that can reliably predict a vaccine’s effect on the clinical endpoint (i.e., surrogate markers) are important tools for guiding vaccine development. This paper addresses issues on optimizing two-phase sampling study design for evaluating surrogate markers in a principal surrogate framework, motivated by the design of a future HIV vaccine trial. To address the problem of missing potential outcomes in a standard trial design, novel trial designs have been proposed that utilize baseline predictors of the immune response biomarker(s) and/or augment the trial by vaccinating uninfected placebo recipients at the end of the trial and measuring their immune biomarkers. However, inefficient use of the augmented information can lead to counterintuitive results on the precision of estimation. To remedy this problem, we propose a pseudo-score type estimator suitable for the augmented design and characterize its asymptotic properties. This estimator has superior performance compared with existing estimators and allows calculation of analytical variances useful for guiding study design. Based on the new estimator we investigate in detail the problem of optimizing the sampling scheme of a biomarker in a vaccine efficacy trial for efficiently estimating its surrogate effect, as characterized by the vaccine efficacy curve (a causal effect predictiveness curve) and by the predicted overall vaccine efficacy using the biomarker.
PMCID: PMC3713795  PMID: 23409839
Closeout placebo vaccination; Estimated likelihood; Immune correlate; Principal surrogate; Pseudo-score; Two-phase sampling design
6.  Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials 
Biometrics  2010;66(4):1153-1161.
Given a randomized treatment Z, a clinical outcome Y, and a biomarker S measured some fixed time after Z is administered, we may be interested in addressing the surrogate endpoint problem by evaluating whether S can be used to reliably predict the effect of Z on Y. Several recent proposals for the statistical evaluation of surrogate value have been based on the framework of principal stratification. In this paper, we consider two principal stratification estimands: joint risks and marginal risks. Joint risks measure causal associations of treatment effects on S and Y, providing insight into the surrogate value of the biomarker, but are not statistically identifiable from vaccine trial data. While marginal risks do not measure causal associations of treatment effects, they nevertheless provide guidance for future research, and we describe a data collection scheme and assumptions under which the marginal risks are statistically identifiable. We show how different sets of assumptions affect the identifiability of these estimands; in particular, we depart from previous work by considering the consequences of relaxing the assumption of no individual treatment effects on Y before S is measured. Based on algebraic relationships between joint and marginal risks, we propose a sensitivity analysis approach for assessment of surrogate value, and show that in many cases the surrogate value of a biomarker may be hard to establish, even when the sample size is large.
PMCID: PMC3597127  PMID: 20105158
Estimated likelihood; Identifiability; Principal stratification; Sensitivity analysis; Surrogate endpoint; Vaccine trials
7.  Commentary on “Principal Stratification — a Goal or a Tool?” by Judea Pearl 
This commentary takes up Pearl's welcome challenge to clearly articulate the scientific value of principal stratification estimands that we and colleagues have investigated, in the area of randomized placebo-controlled preventive vaccine efficacy trials, especially trials of HIV vaccines. After briefly arguing that certain principal stratification estimands for studying vaccine effects on post-infection outcomes are of genuine scientific interest, the bulk of our commentary argues that the “causal effect predictiveness” (CEP) principal stratification estimand for evaluating immune biomarkers as surrogate endpoints is not of ultimate scientific interest, because it evaluates surrogacy restricted to the setting of a particular vaccine efficacy trial, but is nevertheless useful for guiding the selection of primary immune biomarker endpoints in Phase I/II vaccine trials and for facilitating assessment of transportability/bridging surrogacy.
PMCID: PMC3204668  PMID: 22049267
principal stratification; causal inference; vaccine trial
8.  MRKAd5 HIV-1 Gag/Pol/Nef Vaccine-Induced T-Cell Responses Inadequately Predict Distance of Breakthrough HIV-1 Sequences to the Vaccine or Viral Load 
PLoS ONE  2012;7(8):e43396.
The sieve analysis for the Step trial found evidence that breakthrough HIV-1 sequences for MRKAd5/HIV-1 Gag/Pol/Nef vaccine recipients were more divergent from the vaccine insert than placebo sequences in regions with predicted epitopes. We linked the viral sequence data with immune response and acute viral load data to explore mechanisms for and consequences of the observed sieve effect.
Ninety-one male participants (37 placebo and 54 vaccine recipients) were included; viral sequences were obtained at the time of HIV-1 diagnosis. T-cell responses were measured 4 weeks post-second vaccination and at the first or second week post-diagnosis. Acute viral load was obtained at RNA-positive and antibody-negative visits.
Vaccine recipients had a greater magnitude of post-infection CD8+ T cell response than placebo recipients (median 1.68% vs 1.18%; p = 0·04) and greater breadth of post-infection response (median 4.5 vs 2; p = 0·06). Viral sequences for vaccine recipients were marginally more divergent from the insert than placebo sequences in regions of Nef targeted by pre-infection immune responses (p = 0·04; Pol p = 0·13; Gag p = 0·89). Magnitude and breadth of pre-infection responses did not correlate with distance of the viral sequence to the insert (p>0·50). Acute log viral load trended lower in vaccine versus placebo recipients (estimated mean 4·7 vs 5·1) but the difference was not significant (p = 0·27). Neither was acute viral load associated with distance of the viral sequence to the insert (p>0·30).
Despite evidence of anamnestic responses, the sieve effect was not well explained by available measures of T-cell immunogenicity. Sequence divergence from the vaccine was not significantly associated with acute viral load. While point estimates suggested weak vaccine suppression of viral load, the result was not significant and more viral load data would be needed to detect suppression.
PMCID: PMC3428369  PMID: 22952672

Results 1-8 (8)