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Results for “Bootstrap resampling”

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PubMed2001

Prognostic factor studies in oncology: osteosarcoma as a clinical example.

OBJECTIVE: Prognostic factor studies are abundant in oncology. Nevertheless, most of them have very limited impact on clinical practice, in part because many of them have a low statistical power. The importance of statistical power is illustrated using bootstrap resampling of data from a series of osteosarcoma patients. METHODS AND MATERIALS: Osteosarcoma is a rare disease, the incidence being just a few cases per million person-years in the Western World. Very few Phase III studies have been conducted in the disease, and much knowledge on therapeutic progress has come from Phase II studies. This has caused controversy concerning the validity of historical controls, which again has stimulated interest in the identification of prognostic factors in this disease. A literature search in the National Library of Medicine MEDLINE database was performed to identify prognostic factor studies in osteosarcoma published between 1975 and 1998. Monte Carlo methods, so-called bootstrap resampling, are used to investigate the importance of sample size based on survival data for a previously published series of 158 osteosarcomas treated with surgery alone. RESULTS: Most published studies are too small to provide useful information on prognostic factors in osteosarcoma. Three-quarters of the papers reviewed included less than 100 patients with osteosarcoma. More than 20 different potential prognostic factors were included in these papers. Inherent differences between studies and poor reporting hamper a synthesis of information from various studies. The results from the bootstrap resampling illustrate how the majority of published studies would miss even quite significant prognostic factors. CONCLUSIONS: An effort is needed to improve the design, conduct, and reporting of prognostic studies in oncology.

Bone Neoplasms↗

PubMed1998

Estimation of component and parameter distributions in spectral analysis.

A method is presented for estimating the distributions of the components and parameters determined with spectral analysis when it is applied to a single data set. The method uses bootstrap resampling to simulate the effect of noise on the computed spectrum and to correct for possible bias in the estimates. A number of bootstrap procedures are reviewed, and one is selected for application to the kinetic analysis of positron emission tomography dynamic studies. The technique is shown to require minimal assumptions about noise in the measurements, and its small sample properties are established through Monte-Carlo simulations. The advantages and limitations of spectral analysis with bootstrap resampling for deriving inferences for tracer kinetic modeling are illustrated through sample analyses of time-activity curves for [18F]fluorodeoxyglucose and [15O]-labeled water.

Algorithms↗

PubMed2004

Partial least squares analysis of neuroimaging data: applications and advances.

Partial least squares (PLS) analysis has been used to characterize distributed signals measured by neuroimaging methods like positron emission tomography (PET), functional magnetic resonance imaging (fMRI), event-related potentials (ERP) and magnetoencephalography (MEG). In the application to PET, it has been used to extract activity patterns differentiating cognitive tasks, patterns relating distributed activity to behavior, and to describe large-scale interregional interactions or functional connections. This paper reviews the more recent extension of PLS to the analysis of spatiotemporal patterns present in fMRI, ERP, and MEG data. We present a basic mathematical description of PLS and discuss the statistical assessment using permutation testing and bootstrap resampling. These two resampling methods provide complementary information of the statistical strength of the extracted activity patterns (permutation test) and the reliability of regional contributions to the patterns (bootstrap resampling). Simulated ERP data are used to guide the basic interpretation of spatiotemporal PLS results, and examples from empirical ERP and fMRI data sets are used for further illustration. We conclude with a discussion of some caveats in the use of PLS, including nonlinearities, nonorthogonality, and interpretation difficulties. We further discuss its role as an important tool in a pluralistic analytic approach to neuroimaging.

Adult↗

PubMed2004

Predictors of prolonged air leak after pulmonary lobectomy.

BACKGROUND: The objective of this study was to identify the predictors of prolonged air leak (air leak longer than 7 days) in patients submitted to pulmonary lobectomy for lung cancer. METHODS: A retrospective analysis on 588 patients operated on of pulmonary lobectomy from January 1995 through June 2003 was performed. Univariate and logistic regression analyses were performed to generate a model predicting the risk of prolonged air leak. Bootstrap resampling technique was used to validate the regression model. RESULTS: A prolonged leak was exhibited by 15.6% of patients. Logistic regression analysis demonstrated that significant independent predictors of prolonged air leak were a reduced predicted postoperative forced expiratory volume in 1 second (p < 0.0001), the presence of pleural adhesions (p = 0.003), and upper resections (p = 0.006). Bootstrap resampling analysis confirmed the reliability of these variables. A regression equation was generated for the prediction of the risk of prolonged air leak. CONCLUSIONS: We report that a low predicted postoperative forced expiratory volume in 1 second, the presence of pleural adhesions, and the upper lobectomy or bilobectomy increased the risk of air leak persisting for more than 7 days. A model was generated to calculate this risk and assist the surgeon in taking extra measures to prevent such complication (ie, optimizing bronchodilator treatment, pleural tent, sealants, buttressed staple lines, water seal, and chest tube drainage).

Aged↗

PubMed2005

Gene expression profiling allows distinction between primary and metastatic squamous cell carcinomas in the lung.

Lung neoplasms commonly develop in patients previously treated for head and neck carcinomas. The derivation of these tumors, either as new primary lung cancers or as metastatic head and neck cancers, is difficult to establish based on clinical or histopathologic criteria since both are squamous cell carcinomas and have identical features under light microscopy. However, this distinction has significant treatment and prognostic implications. Gene expression profiling was performed on a panel of 52 sequentially collected patients with either primary lung (n = 21) or primary head and neck (n = 31) carcinomas using the Affymetrix HG_U95Av2 high-density oligonucleotide microarray. Unsupervised hierarchical clustering with Ward linkage and the Pearson correlation metric was performed. To assess robustness, bootstrap resampling was performed with 1,000 iterations. A t test of the normalized values for each gene was used to determine the genes responsible for segregating head and neck from lung primary carcinomas, and those with the most differential expression were used for later analyses. In the absence of a large "test" set of tumors, we used a supervised leave-one-out cross-validation to test how well we could predict the tumor origin. Once a gene expression profile was established, 12 lung lesions taken from patients with previously treated head and neck cancers were similarly analyzed by gene expression profiling to determine their sites of origin. Unsupervised clustering analysis separated the study cohort into two distinct groups which reliably remained segregated with bootstrap resampling. Group 1 consisted of 30 tongue carcinomas. Group 2 consisted of 21 lung cancers and 1 tongue carcinoma. The clustering was not changed even when normal lung or tongue profiles were subtracted from the corresponding carcinomatous lesions, and a leave-one-out cross-validation showed a 98% correct prediction (see Supplementary Data 1). A minimum set of 500 genes required to distinguish these groups was established. Given the ability to segregate these lesions using molecular profiling, we analyzed the lung tumors of undetermined origin. All cases clearly clustered with either lung or tongue tumor subsets, strongly supporting our hypothesis that this technique could elucidate the tissue of origin of metastatic lesions. Although histologically similar, squamous cell carcinomas have distinct gene expression profiles based on their anatomic sites of origin. Accordingly, the application of gene expression profiling may be useful in identifying the derivation of lung nodules and consequently enhances treatment planning.

Algorithms↗

PubMed2005

Placing confidence limits on the molecular age of the human-chimpanzee divergence.

Molecular clocks have been used to date the divergence of humans and chimpanzees for nearly four decades. Nonetheless, this date and its confidence interval remain to be firmly established. In an effort to generate a genomic view of the human-chimpanzee divergence, we have analyzed 167 nuclear protein-coding genes and built a reliable confidence interval around the calculated time by applying a multifactor bootstrap-resampling approach. Bayesian and maximum likelihood analyses of neutral DNA substitutions show that the human-chimpanzee divergence is close to 20% of the ape-Old World monkey (OWM) divergence. Therefore, the generally accepted range of 23.8-35 millions of years ago for the ape-OWM divergence yields a range of 4.98-7.02 millions of years ago for human-chimpanzee divergence. Thus, the older time estimates for the human-chimpanzee divergence, from molecular and paleontological studies, are unlikely to be correct. For a given the ape-OWM divergence time, the 95% confidence interval of the human-chimpanzee divergence ranges from -12% to 19% of the estimated time. Computer simulations suggest that the 95% confidence intervals obtained by using a multifactor bootstrap-resampling approach contain the true value with >95% probability, whether deviations from the molecular clock are random or correlated among lineages. Analyses revealed that the use of amino acid sequence differences is not optimal for dating human-chimpanzee divergence and that the inclusion of additional genes is unlikely to narrow the confidence interval significantly. We conclude that tests of hypotheses about the timing of human-chimpanzee divergence demand more precise fossil-based calibrations.

Animals↗

PubMed2000

Aortic valve replacement: is valve size important?

OBJECTIVE: We sought to determine whether aortic prosthesis size adversely influences survival after aortic valve replacement. METHODS: A total of 892 adults receiving a mechanical (n = 346), pericardial (n = 463), or allograft (n = 83) valve for aortic stenosis were observed for up to 20 years (mean, 5.0 +/- 3.9 years) after primary isolated aortic valve replacement. We used multivariable propensity scores to adjust for valve selection factors, multivariable hazard function analyses to identify risk factors for all-cause mortality, and bootstrap resampling to quantify the reliability of the results. RESULTS: Twenty-five percent of patients had indexed internal orifice areas of less than 1.5 cm(2)/m(2) and more than 2 SDs (Z-value) below predicted normal aortic valve size. Mechanical valve orifices were smaller (1.3 +/- 0. 29 cm(2)/m(2), Z = -2.2 +/- 1.16) than pericardial (1.9 +/- 0.36 cm(2)/m(2), Z = -0.40 +/- 1.01) or allograft valves (2.1 +/- 0.50, Z = 0.24 +/- 1.17). The overall survival was 98%, 96%, 86%, 69%, and 49% at 30 days and 1, 5, 10, and 15 years postoperatively. Univariably, survival was weakly and inversely related to manufacturer valve size (P =.16) and internal orifice diameter (P =. 2) but completely unrelated to indexed valve area (P =.6) or Z-value (P =.8). These, and univariable differences among valve types (P =. 004), were accounted for by different prevalences in patient risk factors and not by valve size or type per se. Bootstrap resampling indicated that these findings had a less than 15% chance of being incorrect. CONCLUSIONS: Survival after aortic valve replacement is strongly related to patient risk factors but appears not to be adversely affected by moderate patient-prosthesis mismatch (down to about 4 SDs below normal). Aortic root enlargement to accommodate a large prosthesis may be required in few situations.

Adolescent↗

PubMed2000

Predictors of adverse events with intramuscular ketamine sedation in children.

STUDY OBJECTIVE: Ketamine is a safe and effective sedative for emergency department procedures in children. However, the use of ketamine sometimes is associated with airway complications, emesis, and recovery agitation. We wished to identify predictors of these adverse events that clinicians might use to risk-stratify children who are candidates for ketamine sedation. METHODS: We analyzed data from 1,021 ED intramuscular ketamine sedations in children 15 years of age or younger at a university medical center and an affiliated county hospital over a 9-year period. Five potential predictor variables (age, gender, American Society of Anesthesiologists' [ASA] risk classification, quantity of first ketamine dose, and number of ketamine doses administered) were compared between children with and without complications. We used multiple logistic regression analyses to determine the association of these 5 variables with emesis and recovery agitation, and validated these analyses with bootstrap resampling techniques. We compared children with and without airway complications using univariate statistics alone, as there were too few patients with airway complications to support a multivariate analysis. RESULTS: No study variables had significant univariate associations with airway complications (all P values >.40). We found emesis to be associated with increasing age in multivariate analysis (odds ratio [OR] 1.25 per year, bias-corrected 95% confidence interval [CI] 1.17 to 1.34, P<.001). The incidence of emesis was 12. 1% in children aged 5 years or older, and 3.5% in those younger than 5 years (Delta8.6%, 95% CI 4.9% to 12.1%). Recovery agitation was associated with the presence of an underlying medical condition (ie, ASA class > or =2, OR 3.05, bias-corrected 95% CI 1.65 to 7.30, P=.004) and inversely associated with increasing age (OR 0.79 per year, bias-corrected 95% CI 0.69 to 0.89, P<.001). The incidence of recovery agitation was 17.9% in ASA class 1 children and 33.3% in children in ASA class 2 or greater (Delta-15.4%, 95% CI 0.0% to -30. 7%). The incidence of recovery agitation was 12.1% in children aged 5 years or older, and 22.5% in those younger than 5 years (Delta-10. 4%, 95% CI -3.0% to -17.7%). Bootstrap resampling techniques validated the importance of the significant variables identified in the regression analyses. CONCLUSION: No study variable was predictive of ketamine-associated airway complications. Emesis that occurred after ketamine administration was modestly associated with increasing age. Recovery agitation was modestly associated with decreasing age and the presence of an underlying medical condition. The discriminatory power of these variables was low enough as to be unlikely to alter clinical decisions regarding patient selection for ketamine administration. No evidence of a significant ketamine dose relationship was noted for airway complications, emesis, or recovery agitation.

Adolescent↗

PubMed2003

Population pharmacokinetics of platinum after nedaplatin administration and model validation in adult patients.

AIMS: The pharmacokinetics of unbound platinum after administration of an anticancer drug nedaplatin, cis-diammineglycolateplatinum were examined using population analysis. The relevant covariates and the extent of inter- and intra-individual variability were evaluated. METHODS: In order to clarify the pharmacokinetic profile of nedaplatin, unbound platinum concentrations (789 points) in plasma after intravenous infusion of nedaplatin were obtained from 183 courses for 141 patients. Plasma concentration data were analysed by nonlinear mixed effect modelling using NONMEM to evaluate the population mean parameters and variances for inter- and intra-individual random effects. The final population model was validated by parameter sensitivity analysis using objective function mapping, the bootstrap resampling and a data-splitting technique, i.e. the Jackknife method, and the predictive performance of the final model was evaluated. RESULTS: A two-compartment pharmacokinetic model with zero-order input and first order elimination described the current data well. The significant covariates were creatinine clearance (CLcr) for clearance of platinum (CL) [population mean [95% confidence interval (CI)] CL (l h(-1)) = 4.47 (3.27, 5.67) + 0.0738 (0.0581, 0.0896) x CLcr (CLcr: ml min(-1))] and body weight (BW: kg) for volume of distribution of platinum (Vc) [Vc (l) = 12.0 (7.5, 16.5) + 0.163 (0.081, 0.246) x BW]. Inter-individual variations (CV%, 95% CI) for CL and Vc were 25.5% (20.7, 29.6) and 21.4% (17.0, 24.1), respectively, and intra-individual variation (CV%, 95% CI) was 12.6% (10.5, 14.4). The effects of pretreatment with nedaplatin or other platinum agents on clearance and volume of distribution were also tested, but no significant effect was found. The relationship between the observed and predicted unbound platinum concentration by empirical Bayesian prediction showed good correlation with no bias, suggesting that the final model explains well the observed data in the patients. The mean prediction error and root mean square prediction error (95% CI) were - 0.0164 micro g ml(-1) (- 0.4379, 0.4051) and 0.2155 micro g ml(-1) (not calculable, 0.6523), respectively. The values of mean, standard error and 95% CI for objective function mapping, the bootstrap resampling, the Jackknife estimates and the final model coincided well. CONCLUSIONS: A population pharmacokinetic model was developed for unbound platinum after intravenous infusion of nedaplatin. Only creatinine clearance was found to be a significant covariate of clearance, and BW was found to be a significant covariate of volume of distribution. These population pharmacokinetic estimates are useful for setting initial dosing of nedaplatin using its population mean and can also be used for setting appropriate dosage regimens using empirical Bayesian forecasting.

Adult↗

PubMed2005

Statistical evaluation of pairwise protein sequence comparison with the Bayesian bootstrap.

MOTIVATION: Protein sequence comparison methods are routinely used to infer the intricate network of evolutionary relationships found within the rapidly growing library of protein sequences, and thereby to predict the structure and function of uncharacterized proteins. In the present study, we detail an improved statistical benchmark of pairwise protein sequence comparison algorithms. We use bootstrap resampling techniques to determine standard statistical errors and to estimate the confidence of our conclusions. We show that the underlying structure within benchmark databases causes Efron's standard, non-parametric bootstrap to be biased. Consequently, the standard bootstrap underpredicts average performance when used in the context of evaluating sequence comparison methods. We have developed, as an alternative, an unbiased statistical evaluation based on the Bayesian bootstrap, a resampling method operationally similar to the standard bootstrap. RESULTS: We apply our analysis to the comparative study of amino acid substitution matrix families and find that using modern matrices results in a small, but statistically significant improvement in remote homology detection compared with the classic PAM and BLOSUM matrices. AVAILABILITY: The sequence sets and code for performing these analyses are available from http://compbio.berkeley.edu/. CONTACT: brenner@compbio.berkeley.edu.

Algorithms↗

PubMed1999

Stability of questionnaire items in sport and exercise psychology: bootstrap limits of agreement.

We describe an alternative method for assessing the stability of individual questionnaire items in sport and exercise psychology. To date, the Pearson product-moment correlation coefficient has been widely used in psychometrics. We propose an alternative non-parametric method based on proportion of agreement. Ninety-two male university students completed the revised 9-item Social Physique Anxiety Scale on two occasions, separated by a 2-week interval. Point estimates of the proportion of direct within-individual agreement between the two occasions were calculated separately for each item of the Social Physique Anxiety Scale. Estimates of uncertainty of the agreement were calculated using a bootstrapping resampling technique. For each item, 2000 bootstrap samples (each n = 92 pairs) were redrawn from the original sample. The sample statistic was calculated for each bootstrap sample to provide a bootstrap sampling distribution. The 95% confidence intervals (CI) were then calculated using the percentile method. The three most problematic items were items 7, 8 and 10 (as labelled in the original 12-item scale). These items demonstrated an agreement of 0.46 (95% CI= 0.36 0.56), 0.42 (95% CI = 0.33-0.52) and 0.41 (95% CI = 0.32-0.51) respectively. Our proposed method measures absolute agreement between test-retest responses, is free of normal assumptions, does not depend on high between-individuals variance, and can be applied successfully to individual items in the development of psychological tests.

Adult↗

PubMedDate not supplied

Analysis of pseudo-profiles in organ pharmacokinetics and toxicokinetics.

In general, the pharmacokinetic model parameters, like rate constants, area under the curve (AUC) etc. are estimated via a two-stage procedure, where the values obtained from concentration-time relationships within one subject (experimental unit) are considered to be functionally related to the drug concentrations measured. In many cases 'mean' estimators and their respective standard errors are calculated afterwards. The determination of drug concentrations in organs as well as in the serum of small animals (mice, rats) in dependence of the time after administration often does not permit the establishment of reasonable profiles within one subject suited for conventional pharmacokinetic analyses and tolerability studies. Frequently, only one experimental value per organ or animal is recorded. The consequence is that most pharmacokinetic parameters are to be estimated on the basis of the mean concentrations rather than via the mean of individual parameter estimates. In all cases of a non-linear relationship between a target item and the concentration, the mean-concentration based estimators and the two-stage profile based estimators need not coincide. In addition, in the former case variance estimators may be either difficult to obtain or not deducible. In order to get variance estimators as well as to enable comparisons between different treatment regimens, in addition to bioequivalence testing as a step towards human dose finding studies, various resampling techniques (parametric and non-parametric bootstrap) were applied to generate pseudo-profiles from independent measurements and compared to their more conventional counterparts where applicable. Simulation studies based on different predefined pharmacokinetic models (first-order elimination after i.v. bolus, first-order elimination after first-order absorption, simple capacity-limited kinetics) revealed that even the non-parametric pseudo-profile stratified 'bootstrap' (resampling with replacement per time point) performs quite satisfactorily.

Administration, Oral↗

PubMed2003

Comparison of Bayesian and maximum likelihood bootstrap measures of phylogenetic reliability.

Owing to the exponential growth of genome databases, phylogenetic trees are now widely used to test a variety of evolutionary hypotheses. Nevertheless, computation time burden limits the application of methods such as maximum likelihood nonparametric bootstrap to assess reliability of evolutionary trees. As an alternative, the much faster Bayesian inference of phylogeny, which expresses branch support as posterior probabilities, has been introduced. However, marked discrepancies exist between nonparametric bootstrap proportions and Bayesian posterior probabilities, leading to difficulties in the interpretation of sometimes strongly conflicting results. As an attempt to reconcile these two indices of node reliability, we apply the nonparametric bootstrap resampling procedure to the Bayesian approach. The correlation between posterior probabilities, bootstrap maximum likelihood percentages, and bootstrapped posterior probabilities was studied for eight highly diverse empirical data sets and were also investigated using experimental simulation. Our results show that the relation between posterior probabilities and bootstrapped maximum likelihood percentages is highly variable but that very strong correlations always exist when Bayesian node support is estimated on bootstrapped character matrices. Moreover, simulations corroborate empirical observations in suggesting that, being more conservative, the bootstrap approach might be less prone to strongly supporting a false phylogenetic hypothesis. Thus, apparent conflicts in topology recovered by the Bayesian approach were reduced after bootstrapping. Both posterior probabilities and bootstrap supports are of great interest to phylogeny as potential upper and lower bounds of node reliability, but they are surely not interchangeable and cannot be directly compared.

Animals↗

PubMed2004

Health-related quality of life parameters as prognostic factors in a nonmetastatic breast cancer population: an international multicenter study.

PURPOSE: The purpose of this research was to evaluate whether baseline health-related quality of life (HRQOL) parameters are prognostic factors for survival in locally advanced breast cancer patients. Although the literature highlights the important role of HRQOL parameters in predicting survival in advanced metastatic disease, little evidence exists for earlier stages. PATIENTS AND METHODS: The overall sample consisted of 448 patients randomly assigned to receive cyclophosphamide, epirubicin, and fluorouracil versus epirubicin, cyclophosphamide, and granulocyte colony-stimulating factor. Patients were enrolled in 12 countries. HRQOL baseline scores were assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30. The Cox proportional hazards regression model was used for both univariate and multivariate analyses of survival. In addition, a bootstrap resampling technique was used to assess the stability of the outcomes. Bootstrap results were then applied for model averaging purposes as a means to account for the observed model selection uncertainty. RESULTS: The final multivariate model retained inflammatory breast cancer (T4d) as the only factor predicting overall survival (OS) with a hazard ratio of 1.375 (95% CI, 1.027 to 1.840; P =.03). The presence of inflammatory breast cancer lowers the median survival time from 6.6 to 4.2 years (36% reduction). None of the preselected HRQOL variables were prognostic for OS or disease-free survival, in either the univariate or multivariate analysis. CONCLUSION: Our findings suggest that baseline HRQOL parameters have no prognostic value in a nonmetastatic breast cancer population.

Adult↗

PubMed1991

Exploring the order of odds ratios using the bootstrap.

We show the use of the bootstrap resampling method to examine further the order of a series of odds ratios. Specifically, the bootstrap provides a method for estimating the probabilities that one would find in subsequent independent samples from the same population the observed odds ratio rankings. To illustrate this use of the bootstrap, we modelled the responses of 77 white male physicians to an ethical dilemma involving hypothetical patients. Would the physician report positive HIV status to the health department or would he maintain patient confidentiality? To see if a patient's sex, race, or sexual preference would influence the physicians' decisions, each physician received one of eight randomly selected descriptions of a hypothetical patient. To evaluate the initial order of the patient categories, we constructed 1000 bootstrap samples. Black heterosexual males ranked first or second in 92.2 per cent of the bootstrap samples; black homosexual males ranked first, second or third in 88.6 per cent; and white homosexual females ranked sixth or seventh in 82.9 per cent. Thus we would expect to observe these rankings of the categories in a high percentage of subsequent independent samples.

Black or African American↗

PubMedDate not supplied

On combining dose-response data from epidemiological studies by meta-analysis.

Using data from a meta-analysis of the effects of oestrogen replacement therapy on the development of breast cancer, we compared alternative methods for combining dose-response slopes from epidemiological studies. We evaluated issues related both to summarizing data from single studies and to combining results from multiple studies. Findings related to the analysis of individual dose-response studies include: (1) a method of weighing studies that gives greater influence to dose-response slopes that conform to the linear relation of relative risk to duration can lead to large differences in calculated weights as a function of non-linearity; (2) a regression model using a variable-intercept resulted in a mean dose-response slope that increased as much as threefold when compared with the values obtained with a zero-intercept model. When combining results from multiple studies, we found: (1) calculating standard errors of mean dose-response slopes by methods that allow for both among-study and within-study variability (a random-effects type model) gave values different from a method that assumes homogeneity and equal within-study precision (a fixed-effects model); (2) the random-effects model gives mean and standard error results most similar to a bootstrap resampling method as increasing heterogeneity is observed (however, this model could give biased mean estimates compared with the bootstrap method); (3) a components-of-variance model compares favourably with the bootstrap and is easier to apply than the random-effects model. Based on these findings, we recommend the use of methods which incorporate heterogeneity to guard against underestimating the standard error. However, caution is urged because bias in point estimates can occur if extreme heterogeneity is present. Two other observations affect the interpretation of data combined from multiple studies. First, inclusion into a model of quality scores assigned by blinded reviewers had little effect on the mean dose-response slope and its standard error. Second, the number of studies required to achieve desired statistical power, varies with effect size.

Analysis of Variance↗

PubMed2002

RIO: analyzing proteomes by automated phylogenomics using resampled inference of orthologs.

BACKGROUND: When analyzing protein sequences using sequence similarity searches, orthologous sequences (that diverged by speciation) are more reliable predictors of a new protein's function than paralogous sequences (that diverged by gene duplication). The utility of phylogenetic information in high-throughput genome annotation ("phylogenomics") is widely recognized, but existing approaches are either manual or not explicitly based on phylogenetic trees. RESULTS: Here we present RIO (Resampled Inference of Orthologs), a procedure for automated phylogenomics using explicit phylogenetic inference. RIO analyses are performed over bootstrap resampled phylogenetic trees to estimate the reliability of orthology assignments. We also introduce supplementary concepts that are helpful for functional inference. RIO has been implemented as Perl pipeline connecting several C and Java programs. It is available at http://www.genetics.wustl.edu/eddy/forester/. A web server is at http://www.rio.wustl.edu/. RIO was tested on the Arabidopsis thaliana and Caenorhabditis elegans proteomes. CONCLUSION: The RIO procedure is particularly useful for the automated detection of first representatives of novel protein subfamilies. We also describe how some orthologies can be misleading for functional inference.

Animals↗

PubMed1997

Stability and performance of a population pharmacokinetic model.

This study aimed to determine the stability (in terms of covariate selection) of a population pharmacokinetic model and evaluate its performance in the absence of a test data set. Data from 88 full-term infants, 11 of whom were human immunodeficiency virus (HIV)-seropositive, taking an antiinfective agent were analyzed using exploratory data analysis methods and the nonlinear mixed-effects modeling (NONMEM) program to obtain the final population pharmacokinetic model. The stability of the population pharmacokinetic model was tested using the nonparametric bootstrap approach in four steps: 1) with the base pharmacokinetic model, 100 bootstrap replicates of the original data were generated by sampling with replacement; 2) ascertainment that each bootstrap data replicate was described by the basic structural model using the NONMEM objective function; 3) generalized additive modeling (GAM) applied to empiric Bayesian estimates for covariate selection at alpha = 0.05 and a frequency (f) cutoff value of 0.50; and 4) NONMEM population model building using covariates selected in the third step with alpha = 0.005. Performance of the population pharmacokinetic model was evaluated using 200 additional bootstrap replicates of the data by fitting the model obtained in step 4 to them. Parameters obtained were compared with those obtained in the model stability step, and improved prediction error, a measure of predictive accuracy as an index of internal validation, was computed. The reciprocal of serum creatinine (RSC; f = 0.73) and HIV (f = 0.70) were selected by GAM as predictors of clearance (Cl). The population pharmacokinetic model obtained without the determination of model stability included RSC as a predictor of Cl, but the final model from the model stability step included both HIV and RSC as predictors of Cl. Final population pharmacokinetic parameters were obtained with this model fitted to the original data; however, the 95% confidence interval on the HIV status regression coefficient included zero, indicating no significance. The mean parameter estimates obtained with the additional 200 bootstrap replicates of data were within 15% of those obtained with the final model at the regression stability step. Bootstrap resampling procedure is useful for evaluating the stability and performance of a population model by repeatedly fitting it to the bootstrap samples when there is no test data set.

Adolescent↗