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PubMed2004

Comparison of predictive ability of water solubility QSPR models generated by MLR, PLS and ANN methods.

ADME/Tox computational screening is one of the most hot topics of modern drug research. About one half of the potential drug candidates fail because of poor ADME/Tox properties. Since the experimental determination of water solubility is time-consuming also, reliable computational predictions are needed for the pre-selection of acceptable "drug-like" compounds from diverse combinatorial libraries. Recently many successful attempts were made for predicting water solubility of compounds. A comprehensive review of previously developed water solubility calculation methods is presented here, followed by the description of the solubility prediction method designed and used in our laboratory. We have selected carefully 1381 compounds from scientific publications in a unified database and used this dataset in the calculations. The externally validated models were based on calculated descriptors only. The aim of model optimization was to improve repeated evaluations statistics of the predictions and effective descriptor scoring functions were used to facilitate quick generation of multiple linear regression analysis (MLR), partial least squares method (PLS) and artificial neural network (ANN) models with optimal predicting ability. Standard error of prediction of the best model generated with ANN (with 39-7-1 network structure) was 0.72 in logS units while the cross validated squared correlation coefficient (Q(2)) was better than 0.85. These values give a good chance for successful pre-selection of screening compounds from virtual libraries, based on the predicted water solubility.

Chemical Phenomena↗
PubMed2003

High-throughput functional affinity purification of mannose binding proteins from Oryza sativa.

We have used affinity chromatography in combination with mass spectrometry to isolate, identify, and assign a preliminary functional annotation to a large number of both known and novel proteins from rice. Rice (Oryza sativa) leaf, root, and seed tissue extracts were fractionated by column affinity chromatography using alpha-D-mannose as the ligand. Bound fractions were eluted and subjected to one-dimensional electrophoresis, followed by high-performance liquid chromatography-tandem mass spectrometric analysis of separated proteins. This multiplexed technology resulted in the isolation and identification of 136 distinct mannose binding proteins from rice. A comparative analysis demonstrates very little overlap of identified proteins between the respective tissues, and confirms the correctly compartmentalized presence of a significant number of proteins from largely tissue-specific biochemical pathways. Over 30% of the identified proteins with a previously annotated function are directly involved in sugar metabolism, including several highly expressed known rice lectins. Direct comparison of the peptide sequences identified in this study to those peptides identified in the most comprehensive survey of the rice proteome to date indicates that our current data represents a significant enrichment of proteins unique to this dataset. Nearly 15% of the identified proteins, identified on the basis of exact peptide matching to sequences in the rice genomic database, represent proteins without a previously known functional annotation, indicating the potential of this combined chromatographic approach to assign a preliminary function to novel proteins in a high-throughput fashion.

Binding, Competitive↗
PubMed2004

Ovarian cancer detection by logical analysis of proteomic data.

A new type of efficient and accurate proteomic ovarian cancer diagnosis systems is proposed. The system is developed using the combinatorics and optimization-based methodology of logical analysis of data (LAD) to the Ovarian Dataset 8-7-02 (http://clinicalproteomics.steem.com), which updates the one used by Petricoin et al. in The Lancet 2002, 359, 572-577. This mass spectroscopy-generated dataset contains expression profiles of 15 154 peptides defined by their mass/charge ratios (m/z) in serum of 162 ovarian cancer and 91 control cases. Several fully reproducible models using only 7-9 of the 15 154 peptides were constructed, and shown in multiple cross-validation tests (k-folding and leave-one-out) to provide sensitivities and specificities of up to 100%. A special diagnostic system for stage I ovarian cancer patients is shown to have similarly high accuracy. Other results: (i) expressions of peptides with relatively low m/z values in the dataset are shown to be better at distinguishing ovarian cancer cases from controls than those with higher m/z values; (ii) two large groups of patients with a high degree of similarities among their formal (mathematical) profiles are detected; (iii) several peptides with a blocking or promoting effect on ovarian cancer are identified.

Algorithms↗
PubMed2005

Using LogitBoost classifier to predict protein structural classes.

Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other bio-macromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other.

Animals↗
PubMed2005

Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14.

The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided.

Alcoholism↗
PubMed1997

Recursive partitioning analysis of 1592 patients on four Radiation Therapy Oncology Group studies in inoperable non-small cell lung cancer.

Survival outcome of 1592 analyzable patients on four Radiation Therapy Oncology Group (RTOG) studies in inoperable non-small cell lung cancer were studied utilizing a recursive partitioning analysis (RPA). This approach creates a regression tree according to prognostic variables which partitions into homogenous subsets according to survival. Four protocols, RTOG 83-11, 83-21, 84-03 and 84-07 were analyzed. 83-11 and 84-07 were studies utilizing irradiation with alterfractionation; 83-21 and 84-03 were studies evaluating thymocin with irradiation and prophylactic cranial irradiation with thoracic irradiation respectively. Nine pretreatment variables and one treatment variable were analyzed. Adjustment for radiotherapy effect was made in the accelerated treatment protocol (84-07). Overall, median survival for the entire group was 9.0 months with 17% alive at 2 years. Univariate analysis suggests that KPS, < or = 70 vs. 80-100, pleural effusion, weight loss, < or = 5% vs. 5%, age, 60+ vs. < 60, T stage (T1 and T2 vs. T3 and T4) and N stage (N- vs. N+) were important prognastic factors. Radiation dose, sex, race and histology were not univariate prognastic factors. RPA identified KPS as the most significant covariate (median survival 5.9 mos. < or = 70 vs. 9.9 mos. 80-100). Within KPS 80-100 other splits occurred for N stage, age, weight loss and radiation therapy dose. KPS < or = 70 split at pleural effusion only. The best overall RPA tree has four distinct classes with median survival times ranging from 3.3 to 12.6 months. The RPA classes were validated in an independent non-small cell lung cancer dataset. This analysis may allow more intelligent stratification and study-design for future RTOG trials in inoperable non-small cell lung cancer.

Aged↗
PubMed1998

Population attributable risk for breast cancer: diet, nutrition, and physical exercise.

BACKGROUND: The percentages of breast cancer cases in a given population attributable to specific risk factors (i.e., the population-attributable risks) can be calculated; determination of such risks associated with potentially modifiable risks factors, such as diet (e.g., levels of consumption of fruits, vegetables, vitamins, etc.), alcohol consumption, exercise, and body weight, are necessary to focus prevention strategies. METHODS: With the use of data from a case-control study conducted in Italy from June 1991 through April 1994 on 2569 breast cancer case subjects and 2588 control subjects, we calculated multivariate odds ratios and population-attributable risks for breast cancer in relation to dietary beta-carotene and vitamin E intake, alcohol consumption, physical activity, and, for postmenopausal women, body mass index. RESULTS: Among all subjects, the following attributable risks for the indicated risk factors were observed: 10.7% (95% confidence interval [CI] = 4.4%-17.0%) for high alcohol intake (i.e., >20 g/day), 15.0% (95% CI = 7.4%-22.9%) for low beta-carotene intake (i.e., <3366 microg/day), 8.6% (95% CI = -0.4%-17.5%) for low vitamin E intake (i.e., <8.5 mg/day), and 11.6% (95% CI = -0.1%-23.3%) for low levels of physical activity. The risks associated with alcohol and beta-carotene intake were larger among premenopausal women, and the risk associated with physical activity was larger among postmenopausal women. Being overweight accounted for 10.2 % (95% CI = 0.2%-20.2%) of breast cancer cases in postmenopausal women. Beta-carotene plus alcohol accounted for 28.1% (95% CI = 16.8-39.4) of the cases. Beta-carotene and physical activity accounted for 32% (95% CI = 14.3-49.8), and these three factors together accounted for 33% (95% CI = 19.9-46.1) of the breast cancer cases in the overall dataset. CONCLUSION: Exposure to a few selected and potentially modifiable risk indicators explained about one third of the cases of breast cancer in this Italian population, indicating the theoretical scope for prevention of the disease.

Adult↗
PubMed2003

Relation between left ventricular mass and systolic blood pressure at baseline in the APTH and THOP trials.

BACKGROUND: The relationship between LV mass and systolic blood pressure at baseline was studied in the pooled database of the APTH (n = 408) and THOP (n = 376) trials. METHODS: Partial correlation coefficients were calculated between electrocardiographic and echocardiographic LV mass at baseline and different types of blood pressure, that is, office blood pressure (OBP), 24-h ambulatory blood pressure (ABP), daytime ABP and night-time ABP. RESULTS: Electrocardiographic LV mass correlated equally well (P < or = 0.001) with all types of systolic blood pressure (partial r = 0.19 to 0.22 for the Sokolow-Lyon index, 0.20 to 0.22 for the sum of the Sokolow-Lyon index plus the R-wave in aVl, and 0.12 to 0.19 for the Cornell voltage). The findings were similar for echocardiographic LV mass index (partial r = 0.15 to 0.22). However, for echocardiographic mean wall thickness, there was a stronger association with ABP than with OBP (partial r = 0.30 for 24-h ABP, 0.29 for daytime ABP, 0.26 for night-time ABP versus 0.15 for OBP (P < or = 0.05)). CONCLUSIONS In the combined dataset of the APTH and THOP trials, electrocardiographic and echocardiographic LV mass at baseline correlated significantly with all types of systolic blood pressure. For mean wall thickness, the relationship was stronger for ABP than OBP.

Adult↗
PubMed2004

The use of bootstrap methods for analysing Health-Related Quality of Life outcomes (particularly the SF-36).

Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials as primary outcome measures. Investigators are now asking statisticians for advice on how to analyse studies that have used HRQoL outcomes.HRQoL outcomes, like the SF-36, are usually measured on an ordinal scale. However, most investigators assume that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes (the ordered categories), reflect contiguous intervals along this continuum. The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis such as the t-test and linear regression that assume Normality and constant variance may not be appropriate. For this reason, conventional statistical advice would suggest that non-parametric methods be used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for analysing data. We used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in four datasets (which illustrate the different aspects of study design). We then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes. The standard methods included t-tests, linear regression, summary measures and General Linear Models.Overall, in the datasets we studied, using the SF-36 outcome, bootstrap methods produce results similar to conventional statistical methods. This is likely because the t-test and linear regression are robust to the violations of assumptions that HRQoL data are likely to cause (i.e. non-Normality). While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion.

Arthritis, Rheumatoid↗
PubMed1992

Clinical trials in psychiatry: should protocol deviation censor patient data?

Clinical trials methodologists recommend counting all events regardless of adherence to protocol and comparing originally randomized groups. This strict "intent-to-treat" policy implies availability and use of outcome measures taken regardless of adherence to treatment protocol. However, outcome measurement in psychiatry requires the cooperation of the patient, and usually occurs in the context of treatment management. Consequently, the patient's or clinician's decision not to adhere to the treatment protocol may be design or default cause censorship of patient data by early truncation. This disables the analysis "by intent to treat" in the strict sense. Current methods applied to such nonrandomly truncated datasets are unsatisfactory ("last value" analysis, survival analysis) or worse (imputation by last value carried forward). I review the context of clinical experimentation in psychiatry, contrast the state of design and analysis with expert recommendations on general methods, review the current statistical strategies and propose that investigators should try to obtain complete follow-up data on all patients without regard to their adherence to treatment protocol.

Clinical Trials as Topic↗
PubMed1994

Analysis of repeated categorical data using generalized estimating equations.

Moment methods for analysing repeated binary responses have been proposed by Liang and Zeger, and extended by Prentice and Zhao and Prentice. In these estimating equations, models are proposed for the correlation between the repeated binary responses. We extend Liang and Zeger's method to models for the correlation between repeated nominal or ordinal categorical responses; in particular, when the repeated responses are binary, our methods reduce to Liang and Zeger's method. Our method is illustrated with two datasets. One dataset contains repeated observations of self-assessment of arthritis, an ordered variable with three categories, collected during a randomized comparative study of alternative treatments of patients with rheumatoid arthritis. The second dataset is a longitudinal study of the health effects of air pollution, in which the repeated ordered multinomial response is the wheezing status (no wheeze, wheeze with cold, wheeze apart from cold) of a child at ages 9, 10, 11 and 12 years.

Adult↗
PubMed2002

Lessons learnt about early prostate cancer from large scale databases: population-based pearls of wisdom.

Prostate cancer is one of most common solid tumors in men and poses some of the most difficult problems in clinical research. Although many clinical research hypotheses in this condition have been explored using single center cases series and multi-center clinical trials, the results of these studies have often been equivocal, leaving many questions unanswered. Recently, investigators have utilized large, administrative datasets for prostate cancer research. These databases tend to include large numbers of patients from different geographic regions increasing their generalizability and statistical power. The goal of this report is to review lessons learnt about early prostate cancer using these data sources. In particular, we focus on the application of large, population-based datasets to address issues concerning the natural history of prostate cancer, the impact of race on outcomes in prostate cancer and the effectiveness of various treatments for localized disease. Information gathered from large, administrative databases will be helpful when counseling patients regarding their treatments options for localized prostate cancer and in identifying future directions for prostate cancer research.

Centers for Medicare and Medicaid Services, U.S.↗
PubMed2004

Nursing process outcome linkage research: issues, current status, and health policy implications.

BACKGROUND: The use of large clinical datasets to assess the effectiveness of health care is of growing interest in continuing efforts to understand the impact of healthcare costs on quality. Correspondingly, there is a greater need to define and measure outcomes that are sensitive to nursing interventions. However, concerns exist about the ability to amass and use large clinical nursing datasets to assess the effectiveness of nursing interventions. Some nursing studies have used large clinical datasets to examine patterns of nursing diagnoses, interventions, and outcomes. Among patient populations, however, systematic effectiveness studies of nursing process and outcome linkages at the individual nurse and patient level of analysis are essentially nonexistent. This is largely the result of slow development of nursing classifications, reference terminologies, and reference information standards. Nursing information systems have an unprecedented potential for documentation of nursing practice, as well as the accumulation and analysis of large clinical datasets, to improve nursing performance, increase nursing knowledge, and provide data and information necessary for nursing to participate in the formulation of healthcare policy. OBJECTIVES: A literature search shows that a common framework is beginning to evolve that represents nursing's essential information, eg, the Nursing Minimum Data Set, Management Minimum Data Set, and several standardized nursing languages. Extensive research and other initiatives have produced 1) nursing languages and reference terminologies that span healthcare settings; 2) information models; and 3) standards for datasets supporting information systems. A number of issues remain, however, that concern the development of uniform nursing datasets, definitions of outcomes, quality of nursing data, information system design, and methods of data analysis. We review nursing process outcome research, clarify issues inherent in nursing effectiveness research, and discuss implications for nursing and health policy.

Health Policy↗
PubMed2006

Developing a data dictionary for the irish nursing minimum dataset.

One of the challenges in health care in Ireland is the relatively slow acceptance of standardised clinical information systems. Yet the national Irish health reform programme indicates that an Electronic Health Care Record (EHCR) will be implemented on a phased basis. [3-5]. While nursing has a key role in ensuring the quality and comparability of health information, the so- called 'invisibility' of some nursing activities makes this a challenging aim to achieve [3-5]. Any integrated health care system requires the adoption of uniform standards for electronic data exchange [1-2]. One of the pre-requisites for uniform standards is the composition of a data dictionary. Inadequate definition of data elements in a particular dataset hinders the development of an integrated data depository or electronic health care record (EHCR). This paper outlines how work on the data dictionary for the Irish Nursing Minimum Dataset (INMDS) has addressed this issue. Data set elements were devised on the basis of a large scale empirical research programme. ISO 18104, the reference terminology for nursing [6], was used to cross-map the data set elements with semantic domains, categories and links and data set items were dissected.

Databases as Topic↗
PubMed1999

The neonatal "sepsis work-up": personal reflections on the development of an evidence-based approach toward newborn infections in a managed care organization.

"Rule out sepsis" may be the most common discharge diagnosis among infants admitted to the neonatal intensive care unit. Although the frequency of sepsis, meningitis, and other confirmed bacterial infections has remained constant (between 1 and 5/1000 live births) for many years, the number of infants evaluated and treated is much higher. Each year in the United States, as many as 600 000 infants experience at least one evaluation for suspected bacterial infection during the birth hospitalization. The number treated is estimated at 130 000 to 400 000 per year. Despite massive overtreatment, delayed diagnosis still occurs. The Kaiser Permanente Medical Care Program (KPMCP) considers developing and implementing an evidence-based approach to "rule out sepsis," a research and operational priority. To achieve these goals, it is essential to consider two key aspects of the problem. First, it is important to adopt a phenomenologic approach that takes clinicians' personal experience into account. This must include reflection on those aspects of experience often considered "irrational" or "subjective." Second, incorporation of a phenomenologic approach needs to be tempered with sound epidemiologic methods. If one considers these two aspects-physician experience and sound epidemiology-it is clear that much of the existing literature on "rule out sepsis" is of limited utility. Consequently, the KPMCP has conducted its own studies. These are aimed at characterizing the "sepsis work-up," developing electronic datasets that would permit clinicians to simulate various strategies, and developing techniques for ongoing electronic monitoring. This article summarizes the approach taken by the KPMCP Division of Research. It describes the results of a pilot study as well as the development and use of a dedicated neonatology outcomes database, the Kaiser Permanente Neonatal Minimum Data Set (NMDS). The NMDS database includes the Score for Neonatal Acute Physiology and permits ongoing monitoring of sepsis "work-ups" as well as confirmed cases of neonatal infection. The article also describes how the experience from the pilot as well as the NMDS was incorporated in the design of a much larger study on "rule out sepsis." Finally, the article describes some important theoretic issues affecting decision rule development and the use of computer simulations in neonatology. These issues are 1) how one handles possible overanalysis of a dataset; 2) how one handles data points that are unstable (eg, the absolute neutrophil count, which can vary considerably depending on age and sampling conditions); and 3) the limitations of decision rules based on computer simulations.

Bacterial Infections↗
PubMed2005

Parametric modelling of cost data: some simulation evidence.

Recently, commentators have suggested that the distributional form of cost data should be explicitly modelled to gain efficiency in estimating the population mean. We perform a series of simulation experiments to evaluate the usual sample mean and the mean estimator of a lognormal distribution, in the context of both theoretical distributions and three large empirical datasets. The sample mean is always unbiased, but is somewhat less efficient when the population distribution is truly lognormal. However the lognormal estimator can perform appallingly when the true distribution is not lognormal. In practical situations, where the true distribution is unknown, the sample mean generally remains the estimator of choice, especially when limited sample size prohibits detailed modelling of the cost data distribution.

Clinical Trials as Topic↗
PubMed1988

The hidden effect of time.

It is customary to regard datasets as homogeneous with respect to the order of collection of the measurements. Examples are given in which this assumption is breached. Hidden time trends have implications for the design of studies, their analysis and interpretation. It is suggested that, if the order of observations is known, a plot by time should be performed, perhaps using a cusum.

Aged↗
PubMed2004

Permutation tests for factorially designed neuroimaging experiments.

Permutation methods for analysis of functional neuroimaging data acquired as factorially designed experiments are described and validated. The F ratio was estimated for main effects and interactions at each voxel in standard space. Critical values corresponding to probability thresholds were derived from a null distribution sampled by appropriate permutation of observations. Spatially informed, cluster-level test statistics were generated by applying a preliminary probability threshold to the voxel F maps and then computing the sum of voxel statistics in each of the resulting three-dimensional clusters, i.e., cluster "mass." Using simulations comprising two between- or within-subject factors each with two or three levels, contaminated by Gaussian and non-normal noise, the voxel-wise permutation test was compared to the standard parametric F test and to the performance of the spatially informed statistic using receiver operating characteristic (ROC) curves. Validity of the permutation-testing algorithm and software is endorsed by almost identical performance of parametric and permutation tests of the voxel-level F statistic. Permutation testing of suprathreshold voxel cluster mass, however, was found to provide consistently superior sensitivity to detect simulated signals than either of the voxel-level tests. The methods are also illustrated by application to an experimental dataset designed to investigate effects of antidepressant drug treatment on brain activation by implicit sad facial affect perception in patients with major depression. Antidepressant drug effects in left amygdala and ventral striatum were detected by this software for an interaction between time (within-subject factor) and group (between-subject factor) in a representative two-way factorial design.

Algorithms↗