Causal Inference in Observational Studies with Outcome-Dependent Sampling

In this paper, we consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals are drawn from a population. On these individuals, information is obtained on treatment, outcome, and a few low-dimensional confounders. These individuals are then stratified according to these factors. In the second phase, a random sub-sample of individuals are drawn from each stratum, with known, stratum-specific selection probabilities. On these individuals, a rich set of confounding factors are collected. In this setting, we introduce four estimators: (1) simple inverse weighted, (2) locally efficient, (3) doubly robust and (4)enriched inverse weighted. We evaluate the finite-sample performance of these estimators in a simulation study. We also use our methodology to estimate the causal effect of trauma care on in-hospital mortality using data from the National Study of Cost and Outcomes of Trauma

A Nonlinear Super-Exponential Rational Model of Speculative Financial Bubbles

Keeping a basic tenet of economic theory, rational expectations, we model the nonlinear positive feedback between agents in the stock market as an interplay between nonlinearity and multiplicative noise. The derived hyperbolic stochastic finite-time singularity formula transforms a Gaussian white noise into a rich time series possessing all the stylized facts of empirical prices, as well as accelerated speculative bubbles preceding crashes. We use the formula to invert the two years of price history prior to the recent crash on the Nasdaq (april 2000) and prior to the crash in the Hong Kong market associated with the Asian crisis in early 1994. These complex price dynamics are captured using only one exponent controlling the explosion, the variance and mean of the underlying random walk. This offers a new and powerful detection tool of speculative bubbles and herding behavior.Comment: Latex document of 24 pages including 5 eps figure

Why is order flow so persistent?

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by brokerage. On timescales of less than a few hours the persistence of order flow is overwhelmingly due to splitting rather than herding. We also study the properties of brokerage order flow and show that it is remarkably consistent both cross-sectionally and longitudinally.Comment: 42 pages, 15 figure

Study protocol: The Improving Care of Acute Lung Injury Patients (ICAP) study

INTRODUCTION: The short-term mortality benefit of lower tidal volume ventilation (LTVV) for patients with acute lung injury/acute respiratory distress syndrome (ALI/ARDS) has been demonstrated in a large, multi-center randomized trial. However, the impact of LTVV and other critical care therapies on the longer-term outcomes of ALI/ARDS survivors remains uncertain. The Improving Care of ALI Patients (ICAP) study is a multi-site, prospective cohort study that aims to evaluate the longer-term outcomes of ALI/ARDS survivors with a particular focus on the effect of LTVV and other critical care therapies. METHODS: Consecutive mechanically ventilated ALI/ARDS patients from 11 intensive care units (ICUs) at four hospitals in the city of Baltimore, MD, USA, will be enrolled in a prospective cohort study. Exposures (patient-based, clinical management, and ICU organizational) will be comprehensively collected both at baseline and throughout patients' ICU stay. Outcomes, including mortality, organ impairment, functional status, and quality of life, will be assessed with the use of standardized surveys and testing at 3, 6, 12, and 24 months after ALI/ARDS diagnosis. A multi-faceted retention strategy will be used to minimize participant loss to follow-up. RESULTS: On the basis of the historical incidence of ALI/ARDS at the study sites, we expect to enroll 520 patients over two years. This projected sample size is more than double that of any published study of long-term outcomes in ALI/ARDS survivors, providing 86% power to detect a relative mortality hazard of 0.70 in patients receiving higher versus lower exposure to LTVV. The projected sample size also provides sufficient power to evaluate the association between a variety of other exposure and outcome variables, including quality of life. CONCLUSION: The ICAP study is a novel, prospective cohort study that will build on previous critical care research to improve our understanding of the longer-term impact of ALI/ARDS, LTVV and other aspects of critical care management. Given the paucity of information about the impact of interventions on long-term outcomes for survivors of critical illness, this study can provide important information to inform clinical practice

Perspectives on the Trypanosoma cruzi-host cell receptor interaction

Chagas disease is caused by the parasite Trypanosoma cruzi. The critical initial event is the interaction of the trypomastigote form of the parasite with host receptors. This review highlights recent observations concerning these interactions. Some of the key receptors considered are those for thromboxane, bradykinin, and for the nerve growth factor TrKA. Other important receptors such as galectin-3, thrombospondin, and laminin are also discussed. Investigation into the molecular biology and cell biology of host receptors for T. cruzi may provide novel therapeutic targets