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PubMed2006

Pooled data analysis of laparoscopic vs. open ventral hernia repair: 14 years of patient data accrual.

BACKGROUND: The purpose of this study was to analyze the published perioperative results and outcomes of laparoscopic (LVHR) and open (OVHR) ventral hernia repair focusing on complications and hernia recurrences. METHODS: Data were compiled from all English-language reports of LVHR published from 1996 through January 2006. Series with fewer than 20 cases of LVHR, insufficient details of complications, or those part of a larger series were excluded. Data were derived from 31 reports of LVHR alone (unpaired studies) and 14 that directly compared LVHR to OVHR (paired studies). Chi-squared analysis, Fisher's exact test, and two-tailed t-test analysis were used. RESULTS: Forty-five published series were included, representing 5340 patients (4582 LVHR, 758 OVHR). In the pooled analysis (combined paired and unpaired studies), LVHR was associated with significantly fewer wound complications (3.8% vs. 16.8%, p < 0.0001), total complications (22.7% vs. 41.7%, p < 0.0001), hernia recurrences (4.3% vs. 12.1%, p < 0.0001), and a shorter length of stay (2.4 vs. 4.3 days, p = 0.0004). These outcomes maintained statistical significance when only the paired studies were analyzed. In the pooled analysis, LVHR was associated with fewer gastrointestinal (2.6% vs. 5.9%, p < 0.0001), pulmonary (0.6% vs. 1.7%, p = 0.0013), and miscellaneous (0.7% vs. 1.9%, p = 0.0011) complications, but a higher incidence of prolonged procedure site pain (1.96% vs. 0.92%, p = 0.0469); none of these outcomes was significant in the paired study analysis. No differences in cardiac, neurologic, septic, genitourinary, or thromboembolic complications were found. The mortality rate was 0.13% with LVHR and 0.26% with OVHR (p = NS). Trends toward larger hernia defects and larger mesh sizes were observed for LVHR. CONCLUSIONS: The published literature indicates fewer wound-related and overall complications and a lower rate of hernia recurrence for LVHR compared to OVHR. Further controlled trials are necessary to substantiate these findings and to assess the health care economic impact of this approach.

Female↗
PubMed2006

Statistical issues in microarray data analysis.

Microarrays provide the ability to quantitatively measure the abundance of specific RNA transcripts through sample hybridization to a solid-state grid of oligonucleotides or amplicons. The prospect of measuring the entire transcriptome is extremely alluring, but as with any experiment, it should be met with caution and great consideration. The level of confidence we can assign to the results depends on the skill at which the experiment is conducted, the quality of the experimental design and subsequent analysis, and, most important, the power in the study. Any microarray experiment consists of several components: (1) carrying out an appropriately designed (replicated) plant experiment; (2) array processing, which includes several steps of data acquisition and normalization; and (3) analysis of expression data to identify differentially expressed genes and overall patterns of expression. Numerous software packages are available to assist in performing these steps and it is not our intent to provide a software users manual or a statistical review. It is our intent to provide a brief user's explanation of these various components and present the commonly used methods.

Algorithms↗
PubMed1993

Data analysis in craniofacial biology with special emphasis on longitudinal studies.

Recommendations are made for strengthening data description and analysis in craniofacial biology. Special emphasis is placed on longitudinal data, and PC programs for accomplishing appropriate analyses in this context are described and made available to interested readers. Some more general recommendations are treated in less detail. These include the effective description of data using stem-and-leaf displays and/or boxplots, the use of decision-analytic methods in the management of patients with dentofacial deformities, and the valid application of certain statistical methods in single-subject studies. Finally, it is conjectured that computer-intensive methods such as randomization tests and jackknifing will play an increasingly prominent role in craniofacial research.

Analysis of Variance↗
PubMed2000

Bayesian statistics in medical research: an intuitive alternative to conventional data analysis.

Statistical analysis of both experimental and observational data is central to medical research. Unfortunately, the process of conventional statistical analysis is poorly understood by many medical scientists. This is due, in part, to the counter-intuitive nature of the basic tools of traditional (frequency-based) statistical inference. For example, the proper definition of a conventional 95% confidence interval is quite confusing. It is based upon the imaginary results of a series of hypothetical repetitions of the data generation process and subsequent analysis. Not surprisingly, this formal definition is often ignored and a 95% confidence interval is widely taken to represent a range of values that is associated with a 95% probability of containing the true value of the parameter being estimated. Working within the traditional framework of frequency-based statistics, this interpretation is fundamentally incorrect. It is perfectly valid, however, if one works within the framework of Bayesian statistics and assumes a 'prior distribution' that is uniform on the scale of the main outcome variable. This reflects a limited equivalence between conventional and Bayesian statistics that can be used to facilitate a simple Bayesian interpretation based on the results of a standard analysis. Such inferences provide direct and understandable answers to many important types of question in medical research. For example, they can be used to assist decision making based upon studies with unavoidably low statistical power, where non-significant results are all too often, and wrongly, interpreted as implying 'no effect'. They can also be used to overcome the confusion that can result when statistically significant effects are too small to be clinically relevant. This paper describes the theoretical basis of the Bayesian-based approach and illustrates its application with a practical example that investigates the prevalence of major cardiac defects in a cohort of children born using the assisted reproduction technique known as ICSI (intracytoplasmic sperm injection).

Bayes Theorem↗
PubMed1991

Mathematical model of antiviral immune response. I. Data analysis, generalized picture construction and parameters evaluation for hepatitis B.

The present approach to the mathematical modelling of infectious diseases is based upon the idea that specific immune mechanisms play a leading role in development, course, and outcome of infectious disease. The model describing the reaction of the immune system to infectious agent invasion is constructed on the bases of Burnet's clonal selection theory and the co-recognition principle. The mathematical model of antiviral immune response is formulated by a system of ten non-linear delay-differential equations. The delayed argument terms in the right-hand part are used for the description of lymphocyte division, multiplication and differentiation processes into effector cells. The analysis of clinical and experimental data allows one to construct the generalized picture of the acute form of viral hepatitis B. The concept of the generalized picture includes a quantitative description of dynamics of the principal immunological, virological and clinical characteristics of the disease. Data of immunological experiments in vitro and experiments on animals are used to obtain estimates of permissible values of model parameters. This analysis forms the bases for the solution of the parameter identification problem for the mathematical model of antiviral immune response which will be the topic of the following paper (Marchuk et al., 1991, J. theor. Biol. 15).

Hepatitis B↗
PubMed2002

Data analysis of high-throughput screening results: application of multidomain clustering to the NCI anti-HIV data set.

The routine use of high-throughput screening (HTS) systems in the drug discovery process has resulted in an increasing need for fast, reliable analysis of massive amounts of data. A new automated multidomain clustering method that thoroughly analyzes screening data sets is used to examine both the active and the inactive compounds in a well-known, publicly available data set based on primary screening. Large and small compound sets that defined both chemical families and potential pharmacophore points were discovered. The detection of structure-activity relationships (SAR), aided by the unique classification method, is described in this article.

Algorithms↗
PubMed1981

Multivariate data analysis for outcome studies.

The use of multivariate statistical techniques for analyzing the complex data often gathered in outcome studies is discussed. The multivariate analysis of variance (MANOVA) is suggested for multiple group studies common to outcome studies. This technique can be utilized for a large number of specific research designs whenever multiple outcome measures are collected. MANOVA offers two specific advantages over more familiar univariate approaches: it presents better control over Type 1 error rates while preserving statistical power, and it allows more thorough analysis of complex data.

Humans↗
PubMed2005

Centralized data analysis of a large interlaboratory proteomics project: a feasibility study.

The human Plasma Proteome Project (PPP) is a large-scale collaboration between many laboratories. One of the most demanding tasks in the PPP involved the analysis of very large amounts of raw MS/MS data produced by the participants. The main approach for managing this task was letting the participants analyze their own data and submit the results to the central PPP repository as lists of identified proteins and peptides. To complement this distributed approach, we also performed centralized analysis of the raw MS/MS data provided by the participants. Due to the data redundancy inherent in such a project, centralized analysis has the potential to reduce the computational effort by reducing redundancy before the analysis. Centralized analysis can also unify the process and take advantage of data sharing among laboratories to improve protein identification and validation. The process we employed included removing low-quality spectra, clustering spectra by mutual similarity, and applying uniform peptide and protein identification procedures. To demonstrate the process, we analyzed 5.28 million MS/MS spectra derived by eight laboratories from tryptic peptides of serum and plasma proteins.

Blood Proteins↗
PubMed1989

Comparison of the data, analysis, and results of X-ray absorption studies of cytochrome c oxidase.

Differences in the methods of analysis of X-ray absorption data used by Powers et al. [Powers, L., Blumberg, W. E., Chance, B., Barlow, C., Leigh, J., Jr., Smith, J., Yonetani, T., Vik, S., & Peisach, J. (1979) Biochim. Biophys. Acta 547, 520-538; Powers, L., Chance, B., Ching, Y., & Angiolillo, P. (1981) Biophys. J. 34, 465-498] and Scott et al. [Scott, R., Schwartz, J., & Cramer S. (1986) Biochemistry 25, 5546-5555] are clarified. In addition, we compare the X-ray absorption data and results for resting cytochrome c oxidase reported by both groups using the same analysis method and conclude apart from any assumptions that the data are not identical.

Copper↗
PubMed1984

Data analysis.

Explore the source record for details and available documents.

Data Collection↗
PubMed1995

LabVIEW: a software system for data acquisition, data analysis, and instrument control.

Computer-based data acquisition systems play an important role in clinical monitoring and in the development of new monitoring tools. LabVIEW (National Instruments, Austin, TX) is a data acquisition and programming environment that allows flexible acquisition and processing of analog and digital data. The main feature that distinguishes LabVIEW from other data acquisition programs is its highly modular graphical programming language, "G," and a large library of mathematical and statistical functions. The advantage of graphical programming is that the code is flexible, reusable, and self-documenting. Subroutines can be saved in a library and reused without modification in other programs. This dramatically reduces development time and enables researchers to develop or modify their own programs. LabVIEW uses a large amount of processing power and computer memory, thus requiring a powerful computer. A large-screen monitor is desirable when developing larger applications. LabVIEW is excellently suited for testing new monitoring paradigms, analysis algorithms, or user interfaces. The typical LabVIEW user is the researcher who wants to develop a new monitoring technique, a set of new (derived) variables by integrating signals from several existing patient monitors, closed-loop control of a physiological variable, or a physiological simulator.

Analog-Digital Conversion↗