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  • Publication
    Surrogate Markers for Time-Varying Treatments and Outcomes
    (2015-08-01) Hsu, Jesse Y; Kennedy, Edward H; Roy, Jason A; Stephens-Shields, Alisa J; Small, Dylan S; Joffe, Marshall M
    BACKGROUND: A surrogate marker is a variable commonly used in clinical trials to guide treatment decisions when the outcome of ultimate interest is not available. A good surrogate marker is one where the treatment effect on the surrogate is a strong predictor of the effect of treatment on the outcome. We review the situation when there is one treatment delivered at baseline, one surrogate measured at one later time point, and one ultimate outcome of interest and discuss new issues arising when variables are time-varying. METHODS: Most of the literature on surrogate markers has only considered simple settings with one treatment, one surrogate, and one outcome of interest at a fixed time point. However, more complicated time-varying settings are common in practice. In this article, we describe the unique challenges in two settings, time-varying treatments and time-varying surrogates, while relating the ideas back to the causal-effects and causal-association paradigms. CONCLUSION: In addition to discussing and extending popular notions of surrogacy to time-varying settings, we give examples illustrating that one can be misled by not taking into account time-varying information about the surrogate or treatment. We hope this article has provided some motivation for future work on estimation and inference in such settings.
  • Publication
    Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior
    (2009-10-01) Hui, Sam K; Bradlow, Eric T; Fader, Peter S
    We examine three sets of established behavioral hypotheses about consumers' in-store behavior using field data on grocery store shopping paths and purchases. Our results provide field evidence for the following empirical regularities. First, as consumers spend more time in the store, they become more purposeful—they are less likely to spend time on exploration and more likely to shop/buy. Second, consistent with “licensing” behavior, after purchasing virtue categories, consumers are more likely to shop at locations that carry vice categories. Third, the presence of other shoppers attracts consumers toward a store zone but reduces consumers' tendency to shop there.
  • Publication
    What Makes Negotiators Happy? The Differential Effects of Internal and External Social Comparisons on Negotiator Satisfaction
    (2004-11-01) Novemsky, Nathan; Schweitzer, Maurice E
    This paper examines the role of internal and external social comparisons in negotiator satisfaction. Internal comparisons involve another party to the negotiation (e.g., buyer compared to seller), while external comparisons focus on someone outside of the negotiation (e.g., buyer compared to other buyers). Negotiator satisfaction can influence a range of post-negotiation behavior, but relatively little is known about what makes negotiators more or less satisfied. In many contexts negotiators receive little objective feedback and lack benchmarks against which to judge their outcome. Prior work has modeled negotiator satisfaction as a function of utility maximization, expectancy disconfirmation, and internal social comparisons (social utility). In this paper we identify another particularly important driver of negotiator satisfaction, external social comparisons. Across five studies we demonstrate that external social comparisons affect satisfaction and that the effects of external social comparisons are qualitatively different from those of internal social comparisons. In particular, we find that downward external social comparisons increase satisfaction, while downward internal social comparisons decrease satisfaction. These results inform important prescriptions, and we discuss implications of these results for managing negotiator satisfaction.
  • Publication
    Large-Scale Multiple Testing of Correlations
    (2016-05-05) Cai, T. Tony; Liu, Weidong
    Multiple testing of correlations arises in many applications including gene coexpression network analysis and brain connectivity analysis. In this article, we consider large-scale simultaneous testing for correlations in both the one-sample and two-sample settings. New multiple testing procedures are proposed and a bootstrap method is introduced for estimating the proportion of the nulls falsely rejected among all the true nulls. We investigate the properties of the proposed procedures both theoretically and numerically. It is shown that the procedures asymptotically control the overall false discovery rate and false discovery proportion at the nominal level. Simulation results show that the methods perform well numerically in terms of both the size and power of the test and it significantly outperforms two alternative methods. The two-sample procedure is also illustrated by an analysis of a prostate cancer dataset for the detection of changes in coexpression patterns between gene expression levels. Supplementary materials for this article are available online.
  • Publication
    Strong Control of the Familywise Error Rate in Observational Studies that Discover Effect Modification by Exploratory Methods
    (2015-12-01) Hsu, Jesse Y; Zubizarreta, José R; Small, Dylan S; Rosenbaum, Paul R
    An effect modifier is a pretreatment covariate that affects the magnitude of the treatment effect or its stability. When there is effect modification, an overall test that ignores an effect modifier may be more sensitive to unmeasured bias than a test that combines results from subgroups defined by the effect modifier. If there is effect modification, one would like to identify specific subgroups for which there is evidence of effect that is insensitive to small or moderate biases. In this paper, we propose an exploratory method for discovering effect modification, and combine it with a confirmatory method of simultaneous inference that strongly controls the familywise error rate in a sensitivity analysis, despite the fact that the groups being compared are defined empirically. A new form of matching, strength-k matching, permits a search through more than k covariates for effect modifiers, in such a way that no pairs are lost, provided that at most k covariates are selected to group the pairs. In a strength-k match, each set of k covariates is exactly balanced, although a set of more than k covariates may exhibit imbalance. We apply the proposed method to study the effects of the earthquake that struck Chile in 2010.
  • Publication
    Stationary Gaussian Markov Processes as Limits of Stationary Autoregressive Time Series
    (2017-03-01) Ernst, Philip A; Brown, Lawrence D; Shepp, Larry; Wolpert, Robert L
    We consider the class, ℂp, of all zero mean stationary Gaussian processes, {Yt : t ∈ (—∞, ∞)} with p derivatives, for which the vector valued process {(Yt(0) ,...,Yt(p)) : t ≥ 0} is a p + 1-vector Markov process, where Yt(0) = Y(t). We provide a rigorous description and treatment of these stationary Gaussian processes as limits of stationary AR(p) time series.
  • Publication
    Measuring Multi-Channel Advertising Effectiveness Using Consumer-Level Advertising Response Data
    (2016-01-01) Zantedeschi, Daniel; Feit, Eleanor M; Bradlow, Eric T
    Advances in data collection have made it increasingly easy to collect information on advertising exposures. However, translating this seemingly rich data into measures of advertising response has proven difficult, largely because of concerns that advertisers target customers with a higher propensity to buy or increase advertising during periods of peak demand. We show how this problem can be addressed by studying a setting where a firm randomly held out customers from each campaign, creating a sequence of randomized field experiments that mitigates (many) potential endogeneity problems. Exploratory analysis of individual holdout experiments shows positive effects for both email and catalog; however, the estimated effect for any individual campaign is imprecise, because of the small size of the holdout. To pool data across campaigns, we develop a hierarchical Bayesian model for advertising response that allows us to account for individual differences in purchase propensity and marketing response. Building on the traditional ad-stock framework, we are able to estimate separate decay rates for each advertising medium, allowing us to predict channel-specific short- and long-term effects of advertising and use these predictions to inform marketing strategy. We find that catalogs have substantially longer-lasting impact on customer purchase than emails. We show how the model can be used to score and target individual customers based on their advertising responsiveness, and we find that targeting the most responsive customers increases the predicted returns on advertising by approximately 70% versus traditional recency, frequency, and monetary value-based targeting.
  • Publication
    The Effect of Entry Regulation in the Health Care Sector: The Case of Home Health
    (2014-02-01) Polsky, Daniel; David, Guy; Yang, Jianing; Kinosian, Bruce; Werner, Rachel M
    The consequences of government regulation in the post-acute care sector are not well understood. We examine the effect of entry regulation on quality of care in home health care by analyzing the universe of hospital discharges during 2006 for publicly insured beneficiaries (about 4.5 million) and subsequent home health admissions to determine whether there is a significant difference in home health utilization, hospital readmission rates, and health care expenditures in states with and without Certificate of Need laws (CON) regulating entry. We identify these effects by looking across regulated and nonregulated states within Hospital Referral Regions, which characterize well-defined health care markets and frequently cross state boundaries. We find that CON states use home health less frequently, but system-wide rehospitalization rates, overall Medicare expenditures, and home health practice patterns are similar. Removing CON for home health would have negligible system-wide effects on health care costs and quality.
  • Publication
    An Alternative Approach for Eliciting Willingness-to-Pay: A Randomized Internet Trial
    (2007-04-01) Damschroder, Laura J; Ubel, Peter A; Riis, Jason; Smith, Dylan M
    Open-ended methods that elicit willingness-to-pay (WTP) in terms of absolute dollars often result in high rates of questionable and highly skewed responses, insensitivity to changes in health state, and raise an ethical issue related to its association with personal income. We conducted a 2x2 randomized trial over the Internet to test 4 WTP formats: 1) WTP in dollars; 2) WTP as a percentage of financial resource; 3) WTP in terms of monthly payments; and 4) WTP as a single lump-sum amount. WTP as a percentage of financial resources generated fewer questionable values, had better distribution properties, greater sensitivity of health states, and was not associated with income. WTP elicited on a monthly basis also showed promise.
  • Publication
    Preference-Adaptive Randomization in Comparative Effectiveness Studies
    (2015-03-01) French, Benjamin; Small, Dylan S; Novak, Julie; Saulsgiver, Kathryn; Harhay, Michael O; Asch, David A; Volpp, Kevin G; Halpern, Scott D
    Background Determination of comparative effectiveness in a randomized controlled trial requires consideration of an intervention’s comparative uptake (or acceptance) among randomized participants and the intervention’s comparative efficacy among participants who use their assigned intervention. If acceptance differs across interventions, then simple randomization of participants can result in post-randomization losses that introduce bias and limit statistical power. Methods We develop a novel preference-adaptive randomization procedure in which the allocation probabilities are updated based on the inverse of the relative acceptance rates among randomized participants in each arm. In simulation studies, we determine the optimal frequency with which to update the allocation probabilities based on the number of participants randomized. We illustrate the development and application of preference-adaptive randomization using a randomized controlled trial comparing the effectiveness of different financial incentive structures on prolonged smoking cessation. Results Simulation studies indicated that preference-adaptive randomization performed best with frequent updating, accommodated differences in acceptance across arms, and performed well even if the initial values for the allocation probabilities were not equal to their true values. Updating the allocation probabilities after randomizing each participant minimized imbalances in the number of accepting participants across arms over time. In the smoking cessation trial, unexpectedly large differences in acceptance among arms required us to limit the allocation of participants to less acceptable interventions. Nonetheless, the procedure achieved equal numbers of accepting participants in the more acceptable arms, and balanced the characteristics of participants across assigned interventions. Conclusions Preference-adaptive randomization, coupled with analysis methods based on instrumental variables, can enhance the validity and generalizability of comparative effectiveness studies. In particular, preference-adaptive randomization augments statistical power by maintaining balanced sample sizes in efficacy analyses, while retaining the ability of randomization to balance covariates across arms in effectiveness analyses.