On the analysis of tuberculosis studies with intermittent missing sputum data

In randomized studies evaluating treatments for tuberculosis (TB), individuals are scheduled to be routinely evaluated for the presence of TB using sputum cultures. One important endpoint in such studies is the time of culture conversion, the first visit at which a patient’s sputum culture is negative and remains negative. This article addresses how to draw inference about treatment effects when sputum cultures are intermittently missing on some patients. We discuss inference under a novel benchmark assumption and under a class of assumptions indexed by a treatment-specific sensitivity parameter that quantify departures from the benchmark assumption. We motivate and illustrate our approach using data from a randomized trial comparing the effectiveness of two treatments for adult TB patients in Brazil.Fil: Scharfstein, Daniel. University Johns Hopkins; Estados UnidosFil: Rotnitzky, Andrea Gloria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Torcuato Di Tella. Departamento de Economía; ArgentinaFil: Abraham, Maria. Statistics Collaborative; Estados UnidosFil: McDermott, Aidan. University Johns Hopkins; Estados UnidosFil: Chaisson, Richard. University Johns Hopkins; Estados UnidosFil: Geiter, Lawrence. Otsuka Novel Products; Estados Unido

ANALYSIS OF SUBGROUP EFFECTS IN RANDOMIZED TRIALS WHEN SUBGROUP MEMBERSHIP IS INFORMATIVELY MISSING: APPLICATION TO THE MADIT II STUDY

In this paper, we develop and implement a general sensitivity analysis methodology for drawing inference about subgroup effects in a two-arm randomized trial when subgroup status is only known for a non-random sample in one of the trial arms. The methodology is developed in the context of the MADIT II study, a randomized trial designed to evaluate the effectiveness of implantable defibrillators on survival

Estimating the Causal Effect of Lower Tidal Volume Ventilation on Survival in Patients with Acute Lung Injury

Acute lung injury (ALI) is a condition characterized by acute onset of severe hypoxemia and bliateral pulmonary infiltrates. ALI patients typically require mechanical ventilation in an intensive care unit. Low tidal volume ventilation (LTVV), a time-varying dynamic treatment regime, has been recommended as an effective ventilation strategy. This recommendation was based on the results of the ARMA study, a randomized clinical trial designed to compare low vs. high tidal volume strategies (ARDSNetwork, 2000) . After publication of the trial, some critics focused on the high non-adherence rates in the LTVV arm suggesting that non-adherence occurred because treating physicians felt that deviating from the prescribed regime would improve patient outcomes. In this paper, we seek to address this controversy by estimating the survival distribution in the counterfactual setting where all patients assigned to LTVV followed the regime. Our estimation strategy is based on Robins’s (1986) G-computation formula and fully Bayesian multiple imputation to handle intermittent missing data

ON THE POTENTIAL FOR ILL-LOGIC WITH LOGICALLY DEFINED OUTCOMES

Logically defined outcomes are commonly used in medical diagnoses and epidemiological research. When missing values in the original outcomes exist, the method of handling the missingness can have unintended consequences, even if the original outcomes are missing completely at random. Complicating the issue is that the default behavior of standard statistical packages yields different results. In this paper, we consider two binary original outcomes, which are missing completely at random. For estimating the prevalence of a logically defined or outcome, we discuss the properties of four estimators: complete case estimator, all-available case estimator, maximum likelihood estimator (MLE), and moment-based estimator. With the exception of the all-available case estimator, the estimators are consistent. A simulation study is conducted to evaluate the finite sample performance of the four estimators and an analysis of hypertension data from the Sleep Heart Health Study is presented

INFERENCE FOR SURVIVAL CURVES WITH INFORMATIVELY COARSENED DISCRETE EVENT-TIME DATA: APPLICATION TO ALIVE

In many prospective studies, including AIDS Link to the Intravenous Experience (ALIVE), researchers are interested in comparing event-time distributions (e.g.,for human immunodeficiency virus seroconversion) between a small number of groups (e.g., risk behavior categories). However, these comparisons are complicated by participants missing visits or attending visits off schedule and seroconverting during this absence. Such data are interval-censored, or more generally,coarsened. Most analysis procedures rely on the assumption of non-informative censoring, a special case of coarsening at random that may produce biased results if not valid. Our goal is to perform inference for estimated survival functions across a small number of goups in the presence of informative coarsening. To do so, we propose methods for frequentist and Bayesian inference of ALIVE data utilizing information elicited from ALIVE scientists and an AIDS epidemiology expert about the visit compliance process

A BAYESIAN SHRINKAGE MODEL FOR INCOMPLETE LONGITUDINAL BINARY DATA WITH APPLICATION TO THE BREAST CANCER PREVENTION TRIAL

We consider inference in randomized studies, in which repeatedly measured outcomes may be informatively missing due to drop out. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and posit an exponential tilt model that links non-identifiable and identifiable distributions. This model is indexed by non-identified parameters, which are assumed to have an informative prior distribution, elicited from subject-matter experts. Under this model, full data estimands are shown to be expressed as functionals of the distribution of the observed data. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. In a simulation study, we compare our approach to a fully parametric and a fully saturated model for the distribution of the observed data. Our methodology is motivated and applied to data from the Breast Cancer Prevention Trial

INVESTIGATING MEDIATION WHEN COUNTERFACTUALS ARE NOT METAPHYSICAL: DOES SUNLIGHT UVB EXPOSURE MEDIATE THE EFFECT OF EYEGLASSES ON CATARACTS?

We investigate the degree to which a reduction in ocular sunlight ultra-violet B (UVB) exposure mediates a relationship between wearing eyeglasses and a decreased risk of cataracts. An estimand is proposed in which causal effects are estimated locally within strata based on potential UVB exposure without glasses and the degree to which glasses use reduces UVB exposure. We take advantage of the structure of the data in which the counterfactual UVB exposures if the participants in the study who wore glasses had not worn glasses are considered observable