Biomedical subjects

Sandra E Sinisi

Publications and source records attributed to Sandra E Sinisi.

5 recordsLinked to original sources

PubMed2006

Estimation of direct causal effects.

Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects is typically the goal of research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings. Although multivariable regression is commonly used to estimate direct effects, this approach requires assumptions beyond those required for the estimation of total causal effects. In addition, when the exposure and intermediate variables interact to cause disease, multivariable regression estimates a particular type of direct effect-the effect of an exposure on an outcome when the intermediate is fixed at a specified level. Using the counterfactual framework, we distinguish this definition of a direct effect (controlled direct effect) from an alternative definition, in which the effect of the exposure on the intermediate is blocked, but the intermediate is otherwise allowed to vary as it would in the absence of exposure (natural direct effect). We illustrate the difference between controlled and natural direct effects using several examples. We present an estimation approach for natural direct effects that can be implemented using standard statistical software, and we review the assumptions underlying our approach (which are less restrictive than those proposed by previous authors).

Clinical Trials as Topic↗

PubMed2006

Cross-validated bagged prediction of survival.

In this article, we show how to apply our previously proposed Deletion/Substitution/Addition algorithm in the context of right-censoring for the prediction of survival. Furthermore, we introduce how to incorporate bagging into the algorithm to obtain a cross-validated bagged estimator. The method is used for predicting the survival time of patients with diffuse large B-cell lymphoma based on gene expression variables.

Algorithms↗

PubMed2005

Multiple testing and data adaptive regression: an application to HIV-1 sequence data.

Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological insights regarding the replication ability of HIV-1. Determining specific target codons on the viral strand will facilitate the manufacturing of target-specific antiretrovirals. Various algorithmic and analysis techniques can be applied to this application. In this paper, we apply two techniques to a data set consisting of 317 patients, each with 282 sequenced protease and reverse transcriptase codons. The first application is recently developed multiple testing procedures to find codons which have significant univariate associations with the replication capacity of the virus. A single-step multiple testing procedure (Pollard and van der Laan 2003) method was used to control the family wise error rate (FWER) at the five percent alpha level as well as the application of augmentation multiple testing procedures to control the generalized family wise error (gFWER) or the tail probability of the proportion of false positives (TPPFP). We also applied a data adaptive multiple regression algorithm to obtain a prediction of viral replication capacity based on an entire mutant/non-mutant sequence profile. This is a loss-based, cross-validated Deletion/Substitution/Addition regression algorithm (Sinisi and van der Laan 2004), which builds candidate estimators in the prediction of a univariate outcome by minimizing an empirical risk. These methods are two separate techniques with distinct goals used to analyze this structure of viral data.

Journal Article↗

PubMed2005

Diagnoses based on the Research Diagnostic Criteria for Temporomandibular Disorders in a biracial population of young women.

AIMS: To compare the clinical characteristics of diagnostic subtypes of temporomandibular disorders (TMD) based on the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) in terms of physical findings (Axis I) and psychosocial findings (Axis II) among Caucasian and African American young women. An ancillary goal was to assess the value of using self-reported TMD pain as a screening tool compared to RDC/TMD examinations. METHODS: A biracial community sample (n = 830) of young women 19 to 23 years old was screened for facial pain with the Chronic Pain Grade questionnaire. Patients were considered to be putative cases of TMD if they reported facial pain present within the last 6 months; putative controls had no facial pain history or jaw symptoms. Women with facial pain more than 6 months ago and jaw symptoms (jaw symptom-past pain, JSPP group) were added. 129 women were clinically examined for TMD diagnosis for final confirmation of case-control status. RESULTS: 41 of 43 Caucasian and 11 of 18 African American putative cases were confirmed as cases; 9 of 27 Caucasians, but 0 of 17 African Americans from the JSPP group were confirmed as cases. All 24 putative controls were confirmed as controls. Based on RDC/TMD Axis I, 80% of 61 cases were muscle-related diagnoses, 33% as disc-related diagnoses, and 48% as arthralgia/arthritis/arthrosis. Based on Axis I, there were no significant differences in diagnoses between African American and Caucasian women. Based on Axis II, cases had significantly greater depression (P = .002) and somatization with pain (P < .001) than controls as expected. African Americans had significantly greater somatization with pain than Caucasians (P = .020). There were no other significant racial differences. CONCLUSION: Among young women reporting facial pain, clinical TMD subtypes, pain impact, treatment utilization, and additional characteristics other than somatization with pain were similar between races. A high percentage of these young non-clinical cases presented severe depression and somatization.

Adult↗

PubMed2004

Deletion/substitution/addition algorithm in learning with applications in genomics.

van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment where a parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using a loss function. After briefly reviewing this approach, this article proposes a general deletion/substitution/addition algorithm for minimizing, over subsets of variables (e.g., basis functions), the empirical risk of subset-specific estimators of the parameter of interest. This algorithm provides us with a new class of loss-based cross-validated algorithms in prediction of univariate outcomes, which can be extended to handle multivariate outcomes, conditional density and hazard estimation, and censored outcomes such as survival. In the context of regression, using polynomial basis functions, we study the properties of the deletion/substitution/addition algorithm in simulations and apply the method to detect transcription factor binding sites in yeast gene expression experiments.

Journal Article↗