Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Causal inference (2)
- Adaptive designs; Average treatment effect; Cluster randomized trials; Pair-matching; Randomized trials; Targeted minimum loss-based estimation (TMLE) (1)
- Bayesian method; Case-control; Etiology; Latent class; Measurement error; Pneumonia (1)
- Bioinformatics (1)
- Clinical indicators (1)
-
- Compatibility; deductive procedure; Gateaux derivative; influence function; semiparametric estimation; Turing machine (1)
- Decomposition (1)
- Direct effects (1)
- Dose finding; Phase I trials; dose expansion; sequential monitoring; average sample number. (1)
- Endometriosis (1)
- Interaction (1)
- Inverse probability weighting (1)
- Mediation (1)
- Prognosis index (1)
- Survey (1)
- Targeted maximum likelihood estimation (1)
- Publication
-
- Johns Hopkins University, Dept. of Biostatistics Working Papers (3)
- U.C. Berkeley Division of Biostatistics Working Paper Series (2)
- COBRA Preprint Series (1)
- Harvard University Biostatistics Working Paper Series (1)
- Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series (1)
Articles 1 - 8 of 8
Full-Text Articles in Medicine and Health Sciences
Enhanced Precision In The Analysis Of Randomized Trials With Ordinal Outcomes, Iván Díaz, Elizabeth Colantuoni, Michael Rosenblum
Enhanced Precision In The Analysis Of Randomized Trials With Ordinal Outcomes, Iván Díaz, Elizabeth Colantuoni, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
We present a general method for estimating the effect of a treatment on an ordinal outcome in randomized trials. The method is robust in that it does not rely on the proportional odds assumption. Our estimator leverages information in prognostic baseline variables, and has all of the following properties: (i) it is consistent; (ii) it is locally efficient; (iii) it is guaranteed to match or improve the precision of the standard, unadjusted estimator. To the best of our knowledge, this is the first estimator of the causal relation between a treatment and an ordinal outcome to satisfy these properties. We …
Partially-Latent Class Models (Plcm) For Case-Control Studies Of Childhood Pneumonia Etiology, Zhenke Wu, Maria Deloria-Knoll, Laura L. Hammitt, Scott L. Zeger
Partially-Latent Class Models (Plcm) For Case-Control Studies Of Childhood Pneumonia Etiology, Zhenke Wu, Maria Deloria-Knoll, Laura L. Hammitt, Scott L. Zeger
Johns Hopkins University, Dept. of Biostatistics Working Papers
In population studies on the etiology of disease, one goal is the estimation of the fraction of cases attributable to each of several causes. For example, pneumonia is a clinical diagnosis of lung infection that may be caused by viral, bacterial, fungal, or other pathogens. The study of pneumonia etiology is challenging because directly sampling from the lung to identify the etiologic pathogen is not standard clinical practice in most settings. Instead, measurements from multiple peripheral specimens are made. This paper considers the problem of estimating the population etiology distribution and the individual etiology probabilities. We formulate the scientific …
Deductive Derivation And Computerization Of Compatible Semiparametric Efficient Estimation, Constantine E. Frangakis, Tianchen Qian, Zhenke Wu, Ivan Diaz
Deductive Derivation And Computerization Of Compatible Semiparametric Efficient Estimation, Constantine E. Frangakis, Tianchen Qian, Zhenke Wu, Ivan Diaz
U.C. Berkeley Division of Biostatistics Working Paper Series
Researchers often seek robust inference for a parameter through semiparametric estimation. Efficient semiparametric estimation currently requires theoretical derivation of the efficient influence function (EIF), which can be a challenging and time-consuming task. If this task can be computerized, it can save dramatic human effort, which can be transferred, for example, to the design of new studies. Although the EIF is, in principle, a derivative, simple numerical differentiation to calculate the EIF by a computer masks the EIF's functional dependence on the parameter of interest. For this reason, the standard approach to obtaining the EIF has been the theoretical construction of …
Dose Expansion Cohorts In Phase I Trials, Alexia Iasonos, John O'Quigley
Dose Expansion Cohorts In Phase I Trials, Alexia Iasonos, John O'Quigley
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
A rapidly increasing number of Phase I dose-finding studies, and in particular those based on the standard 3+3 design, frequently prolong the study and include dose expansion cohorts (DEC) with the goal to better characterize the toxicity profiles of experimental agents and to study disease specific cohorts. These trials consist of two phases: the usual dose escalation phase that aims to establish the maximum tolerated dose (MTD) and the dose expansion phase that accrues additional patients, often with different eligibility criteria, and where additional information is being collected. Current protocols typically do not specify whether the MTD will be updated …
A Unification Of Mediation And Interaction: A Four-Way Decomposition, Tyler J. Vanderweele
A Unification Of Mediation And Interaction: A Four-Way Decomposition, Tyler J. Vanderweele
Harvard University Biostatistics Working Paper Series
It is shown that the overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into four components: (i) the effect of the exposure in the absence of the mediator, (ii) the interactive effect when the mediator is left to what it would be in the absence of exposure, (iii) a mediated interaction, and (iv) a pure mediated effect. These four components, respectively, correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation …
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
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 …
Adaptive Pair-Matching In The Search Trial And Estimation Of The Intervention Effect, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan
Adaptive Pair-Matching In The Search Trial And Estimation Of The Intervention Effect, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially increase study power. In a common design, candidate units are identified, and their baseline characteristics used to create the best n/2 matched pairs. Within the resulting pairs, the intervention is randomized, and the outcomes measured at the end of follow-up. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units. As consequence, the observed data cannot be considered as n/2 independent, identically distributed (i.i.d.) pairs of units, as current practice assumes. …
Estimating Population Treatment Effects From A Survey Sub-Sample, Kara E. Rudolph, Ivan Diaz, Michael Rosenblum, Elizabeth A. Stuart
Estimating Population Treatment Effects From A Survey Sub-Sample, Kara E. Rudolph, Ivan Diaz, Michael Rosenblum, Elizabeth A. Stuart
Johns Hopkins University, Dept. of Biostatistics Working Papers
We consider the problem of estimating an average treatment effect for a target population from a survey sub-sample. Our motivating example is generalizing a treatment effect estimated in a sub-sample of the National Comorbidity Survey Replication Adolescent Supplement to the population of U.S. adolescents. To address this problem, we evaluate easy-to-implement methods that account for both non-random treatment assignment and a non-random two-stage selection mechanism. We compare the performance of a Horvitz-Thompson estimator using inverse probability weighting (IPW) and two double robust estimators in a variety of scenarios. We demonstrate that the two double robust estimators generally outperform IPW in …