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Results for “Bootstrap resampling”

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PubMed2006

Development and internal validation of preoperative transition zone prostate cancer nomogram.

OBJECTIVES: Up to 20% of men may harbor a transition zone (TZ) prostate cancer (PCa) at radical prostatectomy (RP). TZ PCa may be associated with more favorable RP pathologic findings than peripheral zone (PZ) PCa. To identify these men, we developed a model capable of predicting the probability of TZ PCa at RP. METHODS: The study cohort consisted of 945 consecutive men treated with RP, with clinical stage, prostate-specific antigen (PSA) level, and detailed biopsy and RP pathology data available. The preoperative variables were used as predictors in the multivariate logistic regression models to predict the rate of TZ PCa at RP. PCa was defined as a TZ tumor when more than 50% of the planimetrically measured tumor volume was situated within the TZ. Regression coefficients were used to develop nomograms, which were subjected to 200 bootstrap resamples to reduce overfit bias. RESULTS: TZ PCa at the final pathologic examination was recorded in 110 patients (11.6%). After 200 bootstraps, the most parsimonious and most accurate nomogram was 77.3% accurate in predicting the probability of TZ PCa. CONCLUSIONS: This nomogram is ideally suited to identify patients with markedly elevated, nearly metastatic serum PSA levels who harbor a TZ tumor that is highly treatable by RP.

Adult↗

PubMed2006

Locus-specific heritability estimation via the bootstrap in linkage scans for quantitative trait loci.

BACKGROUND/AIMS: In genome-wide linkage analysis of quantitative trait loci (QTL), locus-specific heritability estimates are biased when the original data are used to both localize linkage and estimate effects, due to maximization of the LOD score over the genome. Positive bias is increased by adoption of stringent significance levels to control genome-wide type I error. We propose multi-locus bootstrap resampling estimators for bias reduction in the situation in which linkage peaks at more than one QTL are of interest. METHODS: Bootstrap estimates were based on repeated sample splitting in the original dataset. We conducted simulation studies in nuclear families with 0 to 5 QTLs and applied the methods in a genome-wide analysis of a blood pressure phenotype in extended pedigrees from the Framingham Heart Study (FHS). RESULTS: Compared to naïve estimates in the original simulation samples, bootstrap estimates had reduced bias and smaller mean squared error. In the FHS pedigrees, the bootstrap yielded heritability estimates as much as 70% smaller than in the original sample. CONCLUSIONS: Because effect estimates obtained in an initial study are typically inflated relative to those expected in an independent replication study, successful replication will be more likely when sample size requirements are based on bias-reduced estimates.

Cardiovascular Diseases↗

PubMed2002

Better bootstrap estimation of hazardous concentration thresholds for aquatic assemblages.

The introduction of species sensitivity distribution (SSD) approaches to ecological risk assessment offers the potential for a more transparent scientific basis for the derivation of predicted no-effect concentrations. However, conventional SSD methodologies have relied on standard distributions (e.g., log logistic, log normal) that are not necessarily based on sound ecological or statistical grounds. More recently, bootstrap resampling techniques that do not rely on distributional assumptions have been applied to the problem. Here we describe how a more advanced bootstrap methodology may be applied to derive better point estimates and confidence intervals for SSD estimates of safe environmental concentrations. Motivated by the fact that the true SSD may not fit any standard model category, we go on to consider a hybrid bootstrap regression approach. This can yield a substantially different estimate for the SSD when compared with both the basic bootstrap and the more frequently used parametric curve approaches. With increasing use of SSDs in ecological risk assessment, it is now imperative that the scientific community develops agreement over appropriate methods for their derivation.

Data Interpretation, Statistical↗

PubMed2006

Estimated incidence of hepatitis A virus infection in Catalonia.

PURPOSE: Hepatitis A normally is underreported by statutory disease reporting systems. The objective of this study is to estimate the incidence of hepatitis A virus (HAV) infection from prevalence surveys of infection carried out in representative samples of the population in 1989, 1996, and 2002 and the reported disease incidence during 1991 to 2003 in Catalonia. METHODS: The real incidence of the infection was estimated from the reported incidence adjusted by the prevalence of susceptible individuals and the probability of presenting clinical manifestations. The bootstrap resampling technique was used to calculate 95% confidence intervals (CIs) of reported, clinical, and all infection cases. RESULTS: The infection rate estimated by the bootstrap method was 31.1/100,000 person-years (bootstrap studentized 95% CI, 19.4-56.0), and the rate of clinical hepatitis was 20.0/100,000 person-years (95% CI, 11.8-39.9), rates that were 6.3 and 4.1 times greater than the reported rate during the same period, respectively. CONCLUSIONS: In children younger than 5 years, the estimated infection rate was 13.8 times greater than the reported rate. Combined use of reported cases and results of seroprevalence surveys suggest that underreporting of HAV infection is substantial in Catalonia, especially in children younger than 5 years.

Adolescent↗

PubMedDate not supplied

The bootstrap and identification of prognostic factors via Cox's proportional hazards regression model.

This paper describes the use of the bootstrap, a new computer-based statistical methodology, to help validate a regression model resulting from the fitting of Cox's proportional hazards model to a set of censored survival data. As an example, we define a prognostic model for outcome in childhood acute lymphocytic leukemia with the Cox model and use of a training set of 224 patients. To validate the accuracy of the model, we use a bootstrap resampling technique to mimic the population under study in two stages. First, we select the important prognostic factors via a stepwise regression procedure with 100 bootstrap samples. Secondly we estimate the corresponding regression parameters for these important factors with 400 bootstrap samples. The bootstrap result suggests that the model constructed from the training set is reasonable.

Child↗

PubMed2005

Inferring species phylogenies from multiple genes: concatenated sequence tree versus consensus gene tree.

Phylogenetic trees from multiple genes can be obtained in two fundamentally different ways. In one, gene sequences are concatenated into a super-gene alignment, which is then analyzed to generate the species tree. In the other, phylogenies are inferred separately from each gene, and a consensus of these gene phylogenies is used to represent the species tree. Here, we have compared these two approaches by means of computer simulation, using 448 parameter sets, including evolutionary rate, sequence length, base composition, and transition/transversion rate bias. In these simulations, we emphasized a worst-case scenario analysis in which 100 replicate datasets for each evolutionary parameter set (gene) were generated, and the replicate dataset that produced a tree topology showing the largest number of phylogenetic errors was selected to represent that parameter set. Both randomly selected and worst-case replicates were utilized to compare the consensus and concatenation approaches primarily using the neighbor-joining (NJ) method. We find that the concatenation approach yields more accurate trees, even when the sequences concatenated have evolved with very different substitution patterns and no attempts are made to accommodate these differences while inferring phylogenies. These results appear to hold true for parsimony and likelihood methods as well. The concatenation approach shows >95% accuracy with only 10 genes. However, this gain in accuracy is sometimes accompanied by reinforcement of certain systematic biases, resulting in spuriously high bootstrap support for incorrect partitions, whether we employ site, gene, or a combined bootstrap resampling approach. Therefore, it will be prudent to report the number of individual genes supporting an inferred clade in the concatenated sequence tree, in addition to the bootstrap support.

Animals↗

PubMed2002

A mathematical method for determining genome divergence and species delineation using AFLP.

The delineation of bacterial species is presently achieved using direct DNA-DNA relatedness studies of whole genomes. It would be helpful to obtain the same genomically based delineation by indirect methods, provided that descriptions of individual genome composition of bacterial genomes are obtained and included in species descriptions. The amplified fragment length polymorphism (AFLP) technique could provide the necessary data if the nucleotides involved in restriction and amplification are fundamental to the description of genomic divergences. Firstly, in order to verify that AFLP analysis permits a realistic exploration of bacterial genome composition, the strong correspondence between predicted and experimental AFLP data was demonstrated using Agrobacterium strain C58 as a model system. Secondly, a method is proposed for determining current genome mispairing and evolutionary genome divergences between pairs of bacteria, based on arbitrary sampling of genomes by using AFLP. The measure of current genome mispairing was validated by comparison with DNA-DNA relatedness data, which itself correlates with base mispairing. The evolutionary genome divergence is the estimated rate of nucleotide substitution that has occurred since the strains diverged from a common ancestor. Current genome mispairing and evolutionary genome divergence were used to compare members of Agrobacterium, used as a model of closely related genomic species. A strong and highly significant correlation was found between calculated genome mispairing and DNA-DNA relatedness values within genomic species. The canonical 70% DNA-DNA hybridization value used to delineate genomic species was found to correspond to a range of current genome mispairing of 13-13.6%. These limits correspond to 0.097 and 0.104 nucleotide substitutions per site, respectively. In addition, experimental data showed that the large Ti and cryptic plasmids of Agrobacterium had little effect on the estimation of genome divergence. Evolutionary genome divergence was used for phylogenetic inferences. Data showed that members of the same genomic species clustered consistently, as supported by bootstrap resampling. On the basis of these results, it is proposed that the genomic delineation of bacterial species could be based, in future, on phylogenetic groups supported by bootstraps and genome descriptions of individual strains, obtained by AFLP analysis, recorded in accessible databases; this approach might eventually replace DNA-DNA hybridization studies.

Biological Evolution↗

PubMed2003

Clinical identifiers of complicated Staphylococcus aureus bacteremia.

BACKGROUND: Complications of Staphylococcus aureus bacteremia (SAB) are often difficult to identify. The ability to accurately predict the likelihood of these complications would impact patient management. This investigation sought to define readily available clinical characteristics that could help identify patients at risk for complicated SAB. METHODS: A prospective, observational cohort study was conducted from September 1994 through December 1999. Patients were followed up for 12 weeks after the initial positive blood culture result. The primary end point was complicated SAB (attributable mortality, complicated infection, embolic stroke, or recurrent S aureus infection during the 12-week follow-up period). The predictive model was validated using bootstrap resampling. RESULTS: Complicated SAB was present in 43% of 724 consecutive adult hospitalized patients identified during the study period. The full predictive model had a high discriminative ability (bootstrap-corrected c index, 0.78). The strongest predictor of complicated SAB was a positive follow-up blood culture result at 48 to 96 hours. A scoring system based on the presence or absence of 4 risk factors (community acquisition, skin examination findings suggesting acute systemic infection, persistent fever at 72 hours, and positive follow-up blood culture results at 48-96 hours) accurately identified complicated SAB (bootstrap-corrected c index, 0.76). CONCLUSION: Readily available clinical variables can help identify patients at risk for complicated SAB.

Adult↗

PubMed2005

Development and internal validation of a nomogram predicting the probability of prostate cancer Gleason sum upgrading between biopsy and radical prostatectomy pathology.

OBJECTIVE: Previous reports indicate that as many as 43% of men with low grade PCa at biopsy will be diagnosed with high-grade PCa at RP. We explored the rate of upgrading from biopsy to RP specimen in our contemporary cohort, and developed a model capable of predicting the probability of biopsy Gleason sum upgrading. MATERIALS AND METHODS: The study cohort consisted of 2982 men treated with RP, with available clinical stage, serum prostate specific antigen and biopsy Gleason scores. These clinical data were used as predictors in multivariate logistic regression models (LRM) addressing the rate of Gleason sum upgrading between biopsy and RP pathology. LRM regression coefficients were used to develop a nomogram predicting the probability of Gleason sum upgrading and was subjected to 200 bootstrap resamples for internal validation and to reduce overfit bias. RESULTS: Overall, 875 patients were upgraded (29.3%). In multivariate LRMs, all predictors were highly significant (all p values <0.0001). Bootstrap-corrected predictive accuracy of the nomogram predicting the probability of Gleason sum upgrading between biopsy and RP was 0.804. CONCLUSION: We developed a highly accurate clinical aid for treatment decision-making. It may prove useful when the possibility of a more aggressive Gleason variant may change the treatment options.

Biopsy↗

PubMed1997

Analysis of genetic relationships between 10 cattle breeds with 17 microsatellites.

To guide genetic conservation programmes with objective criteria, general genetic variability has to be taken into account. This study was conducted to determine the genetic variation between 10 cattle breeds by using 17 microsatellite loci and 13 biochemical markers (11 blood groups, the transferrin and beta-casein loci). Microsatellite loci were amplified in 31-50 unrelated individuals from 10 cattle breeds: Charolais, Limousin, Breton Black Pied, Parthenais, Montbéliard, Vosgien, Maine-Anjou, Normande, Jersey and Holstein. Neighbor-joining trees were calculated from genetic distance estimates. The robustness of tree topology was obtained by bootstrap resampling of loci. A total of 210 alleles of the 17 microsatellites were detected in this study and average heterozygosities ranged from 0.53 in the Jersey breed to 0.66 in the Parthenais breed. In general, low bootstrap values were obtained: with the 17 microsatellites, the highest bootstrap values concerned the Holstein/Maine-Anjou grouping with an occurrence of 74%; with the biochemical markers, this node had an occurrence of 79% and the Charolais/Limousin grouping appeared with an occurrence of 74%; when microsatellites and biochemical polymorphism were analysed together, the occurrence of the Holstein/Maine-Anjou grouping was 90% and that of the Charolais/Limousin grouping was 42%. These results suggest that 30 microsatellites, a number currently considered as sufficient to distinguish closely related breeds is, in fact, probably insufficient.

Alleles↗

PubMed2001

Population pharmacokinetic modeling and model validation of a spicamycin derivative, KRN5500, in phase 1 study.

PURPOSE: KRN5500, a novel spicamycin derivative, shows the greatest activity against a human tumor xenograft model and the highest therapeutic index among spicamycin derivatives. KRN5500 is currently under clinical development in Japan and the United States. The objective of this study was to develop a population pharmacokinetic model that describes the KRN5500 plasma concentration versus time data. METHODS: Data were collected from 18 patients entered in a phase 1 study. These patients received KRN5500 3-21 mg/m2 as a 2-h infusion. A total of 219 concentration measurements were available. The data were analyzed using the nonlinear mixed effect model (NONMEM) program. In addition, the basic and final population pharmacokinetic models were evaluated using bootstrapping resampling. RESULTS: The basic model selected was a two-compartment model with a combination of additive and constant coefficient of variation error models. The basic model fitted well not only the original data, but also 100 bootstrap replicates generated from the original data set. With regard to the effect of covariates selected by generalized additive modeling analysis, gender (SEX) and performance status were found to be possible determinants of the volume of central compartment by NONMEM analysis. The final regression model for V1 was V1 = theta V1 (1--SEX x theta SEX), where V1 is the typical population value of the volume of central compartment, and SEX = 0 if the patient is male, otherwise SEX = 1. The final model was fitted to the 200 bootstrapped samples. The mean parameter estimates were within 15% of those obtained with the original data set. CONCLUSIONS: The KRN5500 plasma concentration versus time data obtained from the phase 1 study were well described by the population pharmacokinetic model. Further evaluation by bootstrapping showed that the population pharmacokinetic model was stable.

Adult↗

PubMed2001

Uncertainties in model-based outcome predictions for treatment planning.

PURPOSE: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. METHODS AND MATERIALS: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ("noise") is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. RESULTS: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment plans can be ranked based on the probability that one plan is superior to another. Thus, reliability of plan ranking could also be assessed. CONCLUSIONS: A comprehensive framework for incorporating uncertainties into treatment-plan-specific outcome predictions is described. Uncertainty histograms for continuous variable endpoint models provide a straightforward method for visual review of the reliability of outcome predictions for each treatment plan.

Humans↗

PubMed2003

External validation is necessary in prediction research: a clinical example.

BACKGROUND AND OBJECTIVES: Prediction models tend to perform better on data on which the model was constructed than on new data. This difference in performance is an indication of the optimism in the apparent performance in the derivation set. For internal model validation, bootstrapping methods are recommended to provide bias-corrected estimates of model performance. Results are often accepted without sufficient regard to the importance of external validation. This report illustrates the limitations of internal validation to determine generalizability of a diagnostic prediction model to future settings. METHODS: A prediction model for the presence of serious bacterial infections in children with fever without source was derived and validated internally using bootstrap resampling techniques. Subsequently, the model was validated externally. RESULTS: In the derivation set (n=376), nine predictors were identified. The apparent area under the receiver operating characteristic curve (95% confidence interval) of the model was 0.83 (0.78-0.87) and 0.76 (0.67-0.85) after bootstrap correction. In the validation set (n=179) the performance was 0.57 (0.47-0.67). CONCLUSION: For relatively small data sets, internal validation of prediction models by bootstrap techniques may not be sufficient and indicative for the model's performance in future patients. External validation is essential before implementing prediction models in clinical practice.

Bacterial Infections↗

PubMed2000

Ribosomal DNA and resolution of branching order among the ascomycota: how many nucleotides are enough?

Molecular phylogenies for the fungi in the Ascomycota rely heavily on 18S rRNA gene sequences but this gene alone does not answer all questions about relationships. Particularly problematical are the relationships among the first ascomycetes to diverge, the Archiascomycetes, and the branching order among the basal filamentous ascomycetes, the Euascomycetes. Would more data resolve branching order? We used the jackknife and bootstrapping resampling approach that constitutes the "pattern of resolved nodes" method to address the relationship between number of variable sites in a DNA sequence alignment and support for taxonomic clusters. We graphed the effect of increasing sizes of subsamples of the 18S rRNA gene sequences on bootstrap support for nodes in the Ascomycota tree. Nodes responded differently to increasing data. Some nodes, those uniting the filamentous ascomycetes for example, would still have been well supported with only two thirds of the 18S rRNA gene. Other nodes, like the one uniting the Archiascomycetes as a monophyletic group, would require about double the number of variable sites available in the 18S gene for 95% neighbor-joining bootstrap support. Of the several groups emerging at the base of the filamentous ascomycetes, the Pezizales receive the most support as the first to diverge. Our analysis suggests that we would also need almost three times as much sequence data as that provided by the 18S gene to confirm the basal position for the Pezizales and more than seven times as much data to resolve the next group to diverge. If more data from other genes show the same pattern, the lack of resolution for the filamentous ascomycetes may indicate rapid radiation within this clade.

Ascomycota↗

PubMed1997

Resampling and cross-validation techniques: a tool to reduce bias caused by model building?

The process of model building involved in the analysis of many medical studies may lead to a considerable amount of over-optimism with respect to the predictive ability of the 'final' regression model. In this paper we illustrate this phenomenon in a simple cutpoint model and explore to what extent bias can be reduced by using cross-validation and bootstrap resampling. These computer intensive methods are compared to an ad hoc approach and to a heuristic method. Besides illustrating all proposals with the data from a breast cancer study we perform a simulation study in order to assess the quality of the methods.

Bias↗

PubMed2006

Validation of a nomogram predicting the probability of lymph node invasion among patients undergoing radical prostatectomy and an extended pelvic lymphadenectomy.

INTRODUCTION: Our goal was to develop and internally validate a nomogram for prediction of lymph node invasion (LNI) in patients with clinically localized prostate cancer undergoing extended pelvic lymphadenectomy (ePLND). METHODS: 602 consecutive patients (mean age 65.8 years) underwent an ePLND, where 10 or more nodes were removed. PSA was 1.1-49.9 (median 7.2). Clinical stages were: T1c in 55.6%, T2 in 41.4% and T3 in 3%. Biopsy Gleason sums were: 6 or less in 66%, 7 in 25.4%, 8-10 in 8.6%. Multivariate logistic regression models tested the association between all of the above predictors and LNI. Regression-based coefficients were used to develop a nomogram predicting LNI and 200 bootstrap resamples were used for internal validation. RESULTS: Mean number of lymph nodes removed was 17.1 (range 10-40). LNI was detected in 66 patients (11.0%). Univariate predictive accuracy for total PSA, clinical stage and biopsy Gleason sum was 63%, 58% and 73%, respectively. A nomogram based on clinical stage, PSA and Biopsy Gleason sum demonstrated bootstrap-corrected predictive accuracy of 76%. CONCLUSIONS: A nomogram based on pre-treatment PSA, clinical stage and biopsy Gleason sum can highly accurately predict LNI at ePLND.

Aged↗

PubMed1997

Statistical analysis of highly skewed immune response data.

This paper considers methods of statistical analysis for highly skewed immune response data. Observations from population studies of immunological variables are rarely normally distributed between individuals; typically the distribution shows extreme levels of skewness. In some situations, skewness remains considerable even after transforming the data. Using resampling techniques, applied to several actual datasets of ELISA assay data, we consider the robustness of normal parametric methods, e.g. t tests and linear regression. Despite the skewness of the transformed data, we demonstrate that such methods are quite robust depending on the number of observations, type of analysis and severity of skewness. We also illustrate how bootstrap resampling can be used to provide a valid alternative method of analysis that can be used either for checking normal parametric analysis or as a direct method of analysis. We illustrate this combined approach by analysing real data to test for association between human serum antibodies to malaria merozoite surface proteins, MSP1 and MSP2, and resistance to clinical malaria, and confirm the protective effect of antibodies to MSP1 and demonstrated a similar protective effect for some antibodies to MSP2.

Animals↗

PubMed1997

Multiple-time-point dissolution specifications for a sampling acceptance plan.

In dissolution testing, multiple dissolution measurements at specific time points are used to obtain the dissolution characteristics for most extended-release and some immediate-release drug products. This paper presents a general procedure for defining specifications by a multivariate confidence region or by simultaneous confidence limits on the dissolution values of individual time points. The confidence regions and simultaneous confidence limits were estimated using two approaches: the first approach assumed a multivariate normal distribution on the multiple dissolution values and the second approach used the bootstrap resampling method. The multivariate confidence region was constructed using the Hotelling's T2 statistic (equivalently, Mahalanobis distance D2), and the simultaneous confidence limits were based on the Mahalanobis statistic as well as on the Bonterroni adjustment. The Mahalanobis simultaneous confidence limits specification criteria have the overall false out-of-specification rate too low in both parametric and bootstrap approaches.

Chemistry, Pharmaceutical↗