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

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

idem: An R Package for Inferences in Clinical Trials with Death and Missingness

In randomized controlled trials of seriously ill patients, death is common and often defined as the primary endpoint. Increasingly, non-mortality outcomes such as functional outcomes are co-primary or secondary endpoints. Functional outcomes are not defined for patients who die, referred to as "truncation due to death", and among survivors, functional outcomes are often unobserved due to missed clinic visits or loss to follow-up. It is well known that if the functional outcomes "truncated due to death" or missing are handled inappropriately, treatment effect estimation can be biased. In this paper, we describe the package idem that implements a procedure for comparing treatments that is based on a composite endpoint of mortality and the functional outcome among survivors. Among survivors, the procedure incorporates a missing data imputation procedure with a sensitivity analysis strategy. A web-based graphical user interface is provided in the idem package to facilitate users conducting the proposed analysis in an interactive and user-friendly manner. We demonstrate idem using data from a recent trial of sedation interruption among mechanically ventilated patients

SEMIPARAMETRIC BIVARIATE QUANTILE-QUANTILE REGRESSION FOR ANALYZING SEMI-COMPETING RISKS DATA

In this paper, we consider estimation of the effect of a randomized treatment on time to disease progression and death, possibly adjusting for high-dimensional baseline prognostic factors. We assume that patients may or may not have a specific type of disease progression prior to death and those who have this endpoint are followed for their survival information. Progression and survival may also be censored due to loss to follow-up or study termination. We posit a semi-parametric bivariate quantile-quantile regression failure time model and show how to construct estimators of the regression parameters. The causal interpretation of the parameters depends on non-identifiable assumptions. We discuss two assumptions: the first applies to situations where it is reasonable to view disease progression as well defined after death and the second applies to situations where such a view is unreasonable. We conduct a simulation study and analyze data from a randomized trial for the treatment of brain cancer