Biomedical subjects

Merrill D Birkner

Publications and source records attributed to Merrill D Birkner.

4 recordsLinked to original sources

PubMed2006

Comorbidity was associated with neurologic and psychiatric diseases: a general practice-based controlled study.

BACKGROUND AND OBJECTIVE: To comprehensively examine comorbidity in unselected cohorts of patients with depression, stroke, multiple sclerosis (MS), Parkinson's disease/parkinsonism (PD/PKM), dementia, migraine, and epilepsy. METHODS: This cross-sectional study used morbidity data recorded by Dutch general practitioners. Index disease cohort sizes ranged from 241 patients with MS to 6,641 patients with lifetime depression. Thirty somatic and seven psychiatric disease categories were examined to determine whether they were comorbid with the index diseases by performing comparisons with age- and gender-matched control cohorts. Identified comorbidities were classified as either "possible" or "highly probable" comorbidity. RESULTS: An extensive range of 26 disease categories was found to be comorbid with lifetime depression. The comorbidity profile of stroke was also wide, including 21 disease categories. The comorbidity patterns of migraine and epilepsy comprised each 11 disease categories. Those concerning MS, PD/PKM, and dementia included a small number of disease categories. CONCLUSION: This study provides comprehensive knowledge of the occurrence of somatic and psychiatric comorbidity in general populations of patients with depression, stroke, MS, PD/PKM, dementia, migraine, and epilepsy. The implications of the findings for clinical practice and research are discussed.

Aged↗

PubMed2006

Issues of processing and multiple testing of SELDI-TOF MS proteomic data.

A new data filtering method for SELDI-TOF MS proteomic spectra data is described. We examined technical repeats (2 per subject) of intensity versus m/z (mass/charge) of bone marrow cell lysate for two groups of childhood leukemia patients: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). As others have noted, the type of data processing as well as experimental variability can have a disproportionate impact on the list of "interesting'' proteins (see Baggerly et al. (2004)). We propose a list of processing and multiple testing techniques to correct for 1) background drift; 2) filtering using smooth regression and cross-validated bandwidth selection; 3) peak finding; and 4) methods to correct for multiple testing (van der Laan et al. (2005)). The result is a list of proteins (indexed by m/z) where average expression is significantly different among disease (or treatment, etc.) groups. The procedures are intended to provide a sensible and statistically driven algorithm, which we argue provides a list of proteins that have a significant difference in expression. Given no sources of unmeasured bias (such as confounding of experimental conditions with disease status), proteins found to be statistically significant using this technique have a low probability of being false positives.

Acute Disease↗

PubMed2005

Empirical Bayes and resampling based multiple testing procedure controlling tail probability of the proportion of false positives.

Simultaneously testing a collection of null hypotheses about a data generating distribution based on a sample of independent and identically distributed observations is a fundamental and important statistical problem involving many applications. In this article we propose a new re-sampling based multiple testing procedure asymptotically controlling the probability that the proportion of false positives among the set of rejections exceeds q at level alpha, where q and alpha are user supplied numbers. The procedure involves 1) specifying a conditional distribution for a guessed set of true null hypotheses, given the data, which asymptotically is degenerate at the true set of null hypotheses, and 2) specifying a generally valid null distribution for the vector of test-statistics proposed in Pollard & van der Laan (2003), and generalized in our subsequent article Dudoit, van der Laan, & Pollard (2004), van der Laan, Dudoit, & Pollard (2004), and van der Laan, Dudoit, & Pollard (2004b). Ingredient 1) is established by fitting the empirical Bayes two component mixture model (Efron (2001b)) to the data to obtain an upper bound for marginal posterior probabilities of the null being true, given the data. We establish the finite sample rational behind our proposal, and prove that this new multiple testing procedure asymptotically controls the wished tail probability for the proportion of false positives under general data generating distributions. In addition, we provide simulation studies establishing that this method is generally more powerful in finite samples than our previously proposed augmentation multiple testing procedure (van der Laan, Dudoit, & Pollard (2004b)) and competing procedures from the literature. Finally, we illustrate our methodology with a data analysis.

Journal Article↗

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↗