Medicine and Health Sciences Commons^™

Computational Model For Survey And Trend Analysis Of Patients With Endometriosis : A Decision Aid Tool For Ebm, Salvo Reina, Vito Reina, Franco Ameglio, Mauro Costa, Alessandro Fasciani

COBRA Preprint Series

Endometriosis is increasingly collecting worldwide attention due to its medical complexity and social impact. The European community has identified this as a “social disease”. A large amount of information comes from scientists, yet several aspects of this pathology and staging criteria need to be clearly defined on a suitable number of individuals. In fact, available studies on endometriosis are not easily comparable due to a lack of standardized criteria to collect patients’ informations and scarce definitions of symptoms. Currently, only retrospective surgical stadiation is used to measure pathology intensity, while the Evidence Based Medicine (EBM) requires shareable methods and correct …

On The Definition Of A Confounder, Tyler J. Vanderweele, Ilya Shpitser

COBRA Preprint Series

The causal inference literature has provided a clear formal definition of confounding expressed in terms of counterfactual independence. The causal inference literature has not, however, produced a clear formal definition of a confounder, as it has given priority to the concept of confounding over that of a confounder. We consider a number of candidate definitions arising from various more informal statements made in the literature. We consider the properties satisfied by each candidate definition, principally focusing on (i) whether under the candidate definition control for all "confounders" suffices to control for "confounding" and (ii) whether each confounder in some context …

Components Of The Indirect Effect In Vaccine Trials: Identification Of Contagion And Infectiousness Effects, Tyler J. Vanderweele, Eric J. Tchetgen, M. Elizabeth Halloran

COBRA Preprint Series

Vaccination of one person may prevent the infection of another either because (i) the vaccine prevents the first from being infected and from infecting the second or because (ii) even if the first person is infected, the vaccine may render the infection less infectious. We might refer to the first of these mechanisms as a contagion effect and the second as an infectiousness effect. In this paper, for the simple setting of a randomized vaccine trial with households of size two, we use counterfactual theory under interference to provide formal definitions of a contagion effect and an infectiousness effect. Using …

On The Nondifferential Misclassification Of A Binary Confounder, Elizabeth L. Ogburn, Tyler J. Vanderweele

COBRA Preprint Series

Abstract Consider a study with binary exposure, outcome, and confounder, where the confounder is nondifferentially misclassified. Epidemiologists have long accepted the unproven but oft-cited result that, if the confounder is binary, odds ratios, risk ratios, and risk differences which control for the mismeasured confounder will lie between the crude and the true measures. In this paper the authors provide an analytic proof of the result in the absence of a qualitative interaction between treatment and confounder, and demonstrate via counterexample that the result need not hold when there is a qualitative interaction between treatment and confounder. They also present an …

Minimum Description Length And Empirical Bayes Methods Of Identifying Snps Associated With Disease, Ye Yang, David R. Bickel

COBRA Preprint Series

The goal of determining which of hundreds of thousands of SNPs are associated with disease poses one of the most challenging multiple testing problems. Using the empirical Bayes approach, the local false discovery rate (LFDR) estimated using popular semiparametric models has enjoyed success in simultaneous inference. However, the estimated LFDR can be biased because the semiparametric approach tends to overestimate the proportion of the non-associated single nucleotide polymorphisms (SNPs). One of the negative consequences is that, like conventional p-values, such LFDR estimates cannot quantify the amount of information in the data that favors the null hypothesis of no disease-association.

We …

The Handling Of Missing Data In Molecular Epidemiologic Studies, Manisha Desai, Jessica Kubo, Denise Esserman, Mary Beth Terry

COBRA Preprint Series

Background: Molecular epidemiologic studies face a missing data problem as biospecimen data are often collected on only a proportion of subjects eligible for study.

Methods: We investigated all molecular epidemiologic studies published in CEBP in 2009 to characterize the prevalence of missing data and to elucidate how the issue was addressed. We considered multiple imputation (MI), a missing data technique that is readily available and easy to implement, as a possible solution.

Results: While the majority of studies had missing data, only 16% compared subjects with and without missing data. Furthermore, 95% of the studies with missing data performed a …

The Use Of Multiple Imputation In Molecular Epidemiologic Studies Assessing Interaction Effects, Manisha Desai, Denise Esserman, Marilie Gammon, Mary Beth Terry

COBRA Preprint Series

Background: In molecular epidemiologic studies biospecimen data are collected on only a proportion of subjects eligible for study. This leads to a missing data problem. Missing data methods, however, are not typically incorporated into analyses. Instead, complete-case (CC) analyses are performed, which result in biased and inefficient estimates.

Methods: Through simulations, we characterized the bias that results from CC methods when interaction effects are estimated, as this is a major aim of many molecular epidemiologic studies. We also investigated whether standard multiple imputation (MI) could improve estimation over CC methods when the data are not missing at random (NMAR) and …

Nonparametric Incidence Estimation From Prevalent Cohort Survival Data, Marco Carone, Masoud Asgharian, Mei-Cheng Wang

COBRA Preprint Series

Incidence is an important epidemiologic concept particularly useful in assessing an intervention, quantifying disease risk, and planning health resources. Incident cohort studies constitute the gold-standard in estimating disease incidence. However, due to material constraints, data are often collected from prevalent cohort studies whereby diseased individuals are recruited through a cross-sectional survey and followed forward in time. We discuss the identifiability of measures of incidence in the context of prevalent cohort survival studies and derive nonparametric maximum likelihood estimators and their asymptotic properties. The proposed methodology accounts for calendar-time and age-at-onset variation in disease incidence while also addressing common complications arising …

Properties Of Monotonic Effects On Directed Acyclic Graphs, Tyler J. Vanderweele, James M. Robins

COBRA Preprint Series

Various relationships are shown hold between monotonic effects and weak monotonic effects and the monotonicity of certain conditional expectations. Counterexamples are provided to show that the results do not hold under less restrictive conditions. Monotonic effects are furthermore used to relate signed edges on a causal directed acyclic graph to qualitative effect modification. The theory is applied to an example concerning the direct effect of smoking on cardiovascular disease controlling for hypercholesterolemia. Monotonicity assumptions are used to construct a test for whether there is a variable that confounds the relationship between the mediator, hypercholesterolemia, and the outcome, cardiovascular disease.

Estimating The Prevalence Of Disease Using Relatives Of Case And Control Probands, Kristin N. Javaras, Nan M. Laird, James I. Hudson, Brian D. Ripley

COBRA Preprint Series

We introduce a method for estimating the prevalence of disease using data from a case-control family study performed to investigate the aggregation of disease in families. The families are sampled via case and control probands, and the resulting data consist of information on disease status and covariates for the probands and their relatives. We introduce estimators for overall prevalence and for covariate stratum-specific prevalence (e.g., sex-specific prevalence) that yield approximately unbiased estimates of their population counterparts. We also introduce corresponding confidence intervals that have good coverage properties even for small prevalences. The estimators and intervals address the over-representation of diseased …

Biologic Interaction And Their Identification, Tyler J. Vanderweele, James Robins

COBRA Preprint Series

The definitions of a biologic interaction and causal interdependence are reconsidered in light of a sufficient-component cause framework. Various conditions and statistical tests are derived for the presence of biologic interactions. The conditions derived are sufficient but not necessary for the presence of a biologic interaction. Through a series of examples it is made evident that in the context of monotonic effects, but not in general, the conditions which are derived are closely related but not identical to effect modification on the risk difference scale.

A Theory Of Sufficient Cause Interactions, Tyler J. Vanderweele, James M. Robins

COBRA Preprint Series

Sufficient-component causes are discussed within the potential outcome framework so as to formalize notions of sufficient causes, synergism and sufficient cause interactions. Doing so allows for the derivation of counterfactual conditions and statistical tests for detecting the presence of sufficient cause interactions. Under the assumption of monotonic effects, more powerful statistical tests for sufficient cause interactions can be derived. The statistical tests derived for sufficient cause interactions are compared with and contrasted to interaction terms in standard statistical models.

Causal Comparisons In Randomized Trials Of Two Active Treatments: The Effect Of Supervised Exercise To Promote Smoking Cessation, Jason Roy, Joseph W. Hogan

COBRA Preprint Series

In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are often compared. Noncompliance by some subjects with their assigned treatment poses a challenge to the data analyst. Causal parameters of interest might include those defined by subpopulations based on their potential compliance status under each assignment, using the principal stratification framework (e.g., causal effect of new therapy compared to standard therapy among subjects that would comply with either intervention). Even if subjects in one arm do not have access to the other treatment(s), the causal effect of each treatment typically can only be identified from …