Although the area under the receiver operating characteristic (ROC) curve (AUC) is the most popular measure of the performance of prediction models, it has limitations, especially when it is used to evaluate the added discrimination of a new risk marker in an existing risk model. Pencina et al. (2008) proposed two indices, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI), to supplement the improvement in the AUC (IAUC). Their NRI and IDI are based on binary outcomes in case-control settings, which do not involve time-to-event outcome. However, many disease outcomes are time-dependent and the onset time can be censored. Measuring discrimination potential of a prognostic marker without considering time to event can lead to biased estimates. In this paper, we extended the NRI and IDI to time-to-event settings and derived the corresponding sample estimators and asymptotic tests. Simulation studies showed that the time-dependent NRI and IDI have better performance than Pencina’s NRI and IDI for measuring the improved discriminatory power of a new risk marker in prognostic survival models.
Improved discrimination; Prognostic survival models; Time-dependent NRI; Time-dependent IDI
Concerns have been raised about the use of traditional measures of model fit in evaluating risk prediction models for clinical use, and reclassification tables have been suggested as an alternative means of assessing the clinical utility of a model. Several measures based on the table have been proposed, including the reclassification calibration (RC) statistic, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI), but the performance of these in practical settings has not been fully examined. We used simulations to estimate the type I error and power for these statistics in a number of scenarios, as well as the impact of the number and type of categories, when adding a new marker to an established or reference model. The type I error was found to be reasonable in most settings, and power was highest for the IDI, which was similar to the test of association. The relative power of the RC statistic, a test of calibration, and the NRI, a test of discrimination, varied depending on the model assumptions. These tools provide unique but complementary information.
Calibration; Discrimination; Model accuracy; Prediction; Reclassification
The performance of prediction models can be assessed using a variety of different methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic (ROC) curve), and goodness-of-fit statistics for calibration.
Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision–analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.
We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n=544 for model development, n=273 for external validation).
We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for making clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
Models for risk prediction are widely used in clinical practice to risk stratify and assign treatment strategies. The contribution of new biomarkers has largely been based on the area under the receiver operating characteristic curve, but this measure can be insensitive to important changes in absolute risk. Methods based on risk stratification have recently been proposed to compare predictive models. These include the reclassification calibration statistic, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI). This work demonstrates the use of reclassification measures, and illustrates their performance for well-known cardiovascular risk predictors in a cohort of women. These measures are targeted at evaluating the potential of new models and markers to change risk strata and alter treatment decisions.
Mortality among patients with heart failure (HF) is high. Though individual biomarkers have been investigated to determine their value in mortality risk prediction, the role of a multimarker strategy requires further evaluation.
Methods and Results
Olmsted County residents presenting with HF from July 2004 to September 2007 were recruited to undergo biomarker measurement. We investigated whether addition of C-reactive protein (CRP), B-type natriuretic peptide (BNP), and troponin T (TnT) to a model including established risk indicators improved 1-year mortality risk prediction using the c statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Among 593 participants, the mean age was 76.4 years and 48% were men. After 1 year follow-up, 122 (20.6%) participants had died. Patients with CRP (<11.8mg/L), BNP (<350pg/mL), and TnT (≤0.01ng/mL) below the median had low 1-year mortality (3.3%), while those with two or three biomarkers above the median had markedly increased mortality (30.8% and 35.5%, respectively). The addition of two or more biomarkers to the model offered greater improvement in 1-year mortality risk prediction than use of a single biomarker. The combination of CRP and BNP resulted in an increase in the c statistic from 0.757 to 0.810 (p<0.001), an IDI gain of 7.1% (p<0.001), and a NRI of 22.1% (p<0.001). Use of all three biomarkers offered no incremental gain (IDI gain 0.7% vs. CRP+BNP, p=0.065).
Biomarkers improved 1-year mortality risk prediction beyond established indicators. The use of a two-biomarker combination was superior to a single biomarker in risk prediction, though addition of a third biomarker conferred no added benefit.
epidemiology; heart failure; prognosis; inflammation; community
To date, the only established model for assessing risk for nasopharyngeal carcinoma (NPC) relies on the sero-status of the Epstein-Barr virus (EBV). By contrast, the risk assessment models proposed here include environmental risk factors, family history of NPC, and information on genetic variants. The models were developed using epidemiological and genetic data from a large case-control study, which included 1,387 subjects with NPC and 1,459 controls of Cantonese origin. The predictive accuracy of the models were then assessed by calculating the area under the receiver-operating characteristic curves (AUC). To compare the discriminatory improvement of models with and without genetic information, we estimated the net reclassification improvement (NRI) and integrated discrimination index (IDI). Well-established environmental risk factors for NPC include consumption of salted fish and preserved vegetables and cigarette smoking (in pack years). The environmental model alone shows modest discriminatory ability (AUC = 0.68; 95% CI: 0.66, 0.70), which is only slightly increased by the addition of data on family history of NPC (AUC = 0.70; 95% CI: 0.68, 0.72). With the addition of data on genetic variants, however, our model’s discriminatory ability rises to 0.74 (95% CI: 0.72, 0.76). The improvements in NRI and IDI also suggest the potential usefulness of considering genetic variants when screening for NPC in endemic areas. If these findings are confirmed in larger cohort and population-based case-control studies, use of the new models to analyse data from NPC-endemic areas could well lead to earlier detection of NPC.
To determine whether ascites can improve risk discrimination beyond MELD and serum sodium (MELDNa).
Consecutive cirrhotic patients were evaluated for ascites based on an outpatient CT along with concurrent MELD and Na values. Cox models were used to determine the added value of ascites for predicting one-year mortality. Increases in the C-index, integrated discrimination improvement (IDI), and the net reclassification index (NRI) were used to assess improvements in discrimination after the addition of ascites.
1,003 patients had Na and MELD scores available within 30 days of the CT scan. Sixty deaths occurred within one year, with mortality higher in patients with ascites (21.4% versus 4.0%, HR 6.08, 95% CI 3.62–10.19, p<0.0005). In the presence of ascites, the MELD and MELDNa score underestimated mortality risk when the scores were less than 21. The addition of ascites to the MELDNa model substantially improved discrimination by the C-index (0.804 versus 0.770, increase of 3.4%, 95% CI 0.2%–9.9%), IDI (1.8%, p=0.016), and NRI (15.8%, p=0.0006).
The incorporation of radiographic ascites significantly improves upon MELDNa for predicting one-year mortality. The presence of ascites may help identify patients at increased risk for mortality not otherwise captured by MELD or MELDNa.
Ascites; MELD; MELDNa; prognostic model; cirrhosis
Rapid detection of methicillin-resistant Staphylococcus aureus (MRSA) by PCR can be performed directly from nasal specimens with the IDI-MRSA assay. To improve the efficiency of screening, we evaluated the performance of the IDI-MRSA assay for the detection of MRSA from pooled and unpooled specimens cultured in a selective broth. Of the 287 specimens evaluated, 71 were culture and PCR positive, 203 were culture and PCR negative, 3 were culture positive and PCR negative, 8 were culture negative and PCR positive, and 2 remained inhibited. A methicillin-susceptible Staphylococcus aureus isolate was recovered from five of the eight specimens with false-positive PCR results. Compared to the results of culture, the sensitivity, specificity, and negative and positive predictive values of the IDI-MRSA assay for detection of MRSA from broth were 96%, 96%, 90%, and 98%, respectively. Following implementation of the IDI-MRSA assay, PCR-positive broths were subcultured for evaluation of assay performance. Of the 298 IDI-MRSA assay-positive broths, the results for 103 could not be confirmed by culture. A methicillin-susceptible S. aureus (MSSA) isolate was recovered from 77 of these 103 broths. Repeat testing by the IDI-MRSA assay directly with the MSSA isolates confirmed the original positive PCR result. The positive predictive value of the IDI-MRSA assay fell from 90% during the evaluation phase to 65% postimplementation. The IDI-MRSA assay performed well for the detection of MRSA from a selective broth compared to the performance of the detection of MRSA from culture. However, because of the burden associated with implementation of infection control precautions, cultures remain essential in confirming positive IDI-MRSA results.
The discovery and development of new biomarkers continues to be an exciting and promising field. Improvement of prediction of risk of developing disease is one of the key motivations in these pursuits. Appropriate statistical measures are necessary for drawing meaningful conclusions about the clinical usefulness of these new markers. In this review, we present several novel metrics proposed to serve this purpose. We use reclassification tables constructed based on clinically meaningful disease risk categories to discuss the concepts of calibration, risk separation, risk discrimination, and risk classification accuracy. We discuss the notion that the net reclassification improvement is a simple yet informative way to summarize information contained in risk reclassification tables. In the absence of meaningful risk categories, we suggest a ‘category-less’ version of the net reclassification improvement and integrated discrimination improvement as metrics to summarize the incremental value of new biomarkers. We also suggest that predictiveness curves be preferred to receiver-operating-characteristic curves as visual descriptors of a statistical model’s ability to separate predicted probabilities of disease events. Reporting of standard metrics, including measures of relative risk and the c statistic is still recommended. These concepts are illustrated with a risk prediction example using data from the Framingham Heart Study.
reclassification; risk prediction; NRI; IDI; calibration; discrimination
This study compares inflammation-related biomarkers with established cardiometabolic risk factors in the prediction of incident type 2 diabetes and incident coronary events in a prospective case-cohort study within the population-based MONICA/KORA Augsburg cohort.
Methods and Findings
Analyses for type 2 diabetes are based on 436 individuals with and 1410 individuals without incident diabetes. Analyses for coronary events are based on 314 individuals with and 1659 individuals without incident coronary events. Mean follow-up times were almost 11 years. Areas under the receiver-operating characteristic curve (AUC), changes in Akaike's information criterion (ΔAIC), integrated discrimination improvement (IDI) and net reclassification index (NRI) were calculated for different models. A basic model consisting of age, sex and survey predicted type 2 diabetes with an AUC of 0.690. Addition of 13 inflammation-related biomarkers (CRP, IL-6, IL-18, MIF, MCP-1/CCL2, IL-8/CXCL8, IP-10/CXCL10, adiponectin, leptin, RANTES/CCL5, TGF-β1, sE-selectin, sICAM-1; all measured in nonfasting serum) increased the AUC to 0.801, whereas addition of cardiometabolic risk factors (BMI, systolic blood pressure, ratio total/HDL-cholesterol, smoking, alcohol, physical activity, parental diabetes) increased the AUC to 0.803 (ΔAUC [95% CI] 0.111 [0.092–0.149] and 0.113 [0.093–0.149], respectively, compared to the basic model). The combination of all inflammation-related biomarkers and cardiometabolic risk factors yielded a further increase in AUC to 0.847 (ΔAUC [95% CI] 0.044 [0.028–0.066] compared to the cardiometabolic risk model). Corresponding AUCs for incident coronary events were 0.807, 0.825 (ΔAUC [95% CI] 0.018 [0.013–0.038] compared to the basic model), 0.845 (ΔAUC [95% CI] 0.038 [0.028–0.059] compared to the basic model) and 0.851 (ΔAUC [95% CI] 0.006 [0.003–0.021] compared to the cardiometabolic risk model), respectively.
Inclusion of multiple inflammation-related biomarkers into a basic model and into a model including cardiometabolic risk factors significantly improved the prediction of type 2 diabetes and coronary events, although the improvement was less pronounced for the latter endpoint.
To determine among community patients with heart failure (HF), whether pulmonary artery systolic pressure (PASP) assessed by Doppler echocardiography was associated with death and improved risk prediction over established factors, using the integrated discrimination improvement (IDI) and net reclassification improvement (NRI).
While several studies have focused on idiopathic pulmonary arterial hypertension, less is known about pulmonary hypertension among patients with HF, particularly on its prognostic value in the community.
Olmsted County residents with HF between 2003 and 2010 prospectively underwent assessment of ejection fraction (EF), diastolic function, and PASP by Doppler echocardiography.
PASP was recorded in 1049 of 1153 patients (mean age 76±13, 51% women). Median PASP was 48 mmHg (25th-75th percentile, 37.0-58.0). There were 489 deaths after a follow-up of 2.7±1.9 years. There was a strong positive graded association between PASP and mortality. Increasing PASP was associated with an increased risk of death (HR 1.45, 95%CI 1.13-1.85 for tertile 2; HR 2.07, 95%CI 1.62-2.64 for tertile 3, versus tertile 1), independently of age, sex, comorbidities, EF and diastolic function. Adding PASP to models including these clinical characteristics resulted in an increase in the c-statistic from 0.704 to 0.742 (p=0.007), an IDI gain of 4.2% (p<0.001), and an NRI of 14.1% (p=0.002), indicating that PASP improved prediction of death over traditional prognostic factors. All results were similar for CV death.
Among community patients with HF, PASP strongly predicts death and provides incremental and clinically relevant prognostic information independently of known predictors of outcomes.
heart failure; pulmonary hypertension; mortality; community
The use of an Infectious Diseases Impact Statement (IDIS) is proposed for predictive assessments of local changes in infectious diseases arising from human-engineered activities. IDIS is intended to be analogous to an Environmental Impact Statement. The drafting of an IDIS for specific activities, particularly in developing nations, would provide a formal mechanism for examining potential changes in local health conditions, including infected and susceptible populations, diseases likely to fluctuate in response to development, existing control measures, and vectors likely to be affected by human activities. The resulting survey data could provide a rational basis and direction for development, surveillance, and prevention measures. An IDIS process that balances environmental alterations, local human health, and economic growth could substantially alter the nature of international development efforts and infectious disease outbreaks.
B-type natriuretic peptide (BNP) has been associated with short- and long-term post-discharge prognosis among hospitalized heart failure (HF) patients. It is unknown if admission, discharge, or change from admission to discharge BNP measure is the most important predictor of long-term outcomes.
Methods and Results
We linked patients ≥65 years from hospitals in OPTIMIZE-HF to Medicare claims. Among patients with recorded admission and discharge BNP, we compared Cox models predicting 1-year mortality and/or rehospitalization, including clinical variables and clinical variables plus BNP. We calculated the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) for the best-fit model for each outcome versus the model with clinical variables alone. Among 7039 patients in 220 hospitals, median (25th, 75th) admission and discharge BNP were 832 pg/mL (451, 1660) and 534 pg/mL (281, 1111). Observed 1-year mortality and 1-year mortality or rehospitalization rates were 35.2% and 79.4%. The discharge BNP model had the best performance and was the most important characteristic for predicting 1-year mortality (hazard ratio [HR] for log transformation 1.34; 95% CI 1.28–1.40) and 1-year death or rehospitalization (HR 1.15; 95% CI 1.12–1.18). Compared with a clinical variables only model, the discharge BNP model improved risk reclassification and discrimination in predicting each outcome (1-year mortality: NRI 5.5%, P<0.0001; IDI 0.023, P<0.0001; 1-year mortality or rehospitalization: NRI 4.2%, P<0.0001; IDI 0.010, P<0.0001).
Discharge BNP best predicts 1-year mortality and/or rehospitalization among older patients hospitalized with HF. Discharge BNP plus clinical variables modestly improves risk classification and model discrimination for long-term outcomes.
B-type natriuretic peptide; outcomes; OPTIMIZE-HF; risk stratification
An updated IDI-MRSA assay version was released to address the assay's low positive predictive value (PPV). A prospective analysis of two assay versions indicated no significant improvement in the PPV. Colonization by methicillin-resistant Staphylococcus aureus in 24% of patients would not have been detected if only nasal samples had been tested, as approved, by this molecular method.
Isopentenyl diphosphate isomerase (IDI) catalyzes the interconversion of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), the basic five-carbon building blocks of isoprenoid molecules. Two structurally unrelated classes of IDI are known. Type I IPP isomerase (IDI-1) utilizes a divalent metal in a protonation-deprotonation reaction. In contrast, the type II enzyme (IDI-2) requires reduced flavin, raising the possibility that the reaction catalyzed by IDI-2 involves the net addition/abstraction of a hydrogen atom. As part of our studies of the mechanism of isomerization for IDI-2, we synthesized allene and alkyne substrate analogues for the enzyme. These molecules are predicted to be substantially less reactive toward proton addition than IPP and DMAPP, but have similar reactivities toward hydrogen atom addition. This prediction was verified by calculations of gas phase heats of reaction for addition of a proton and of a hydrogen atom to 1-butyne (3) and 1,2-butadiene (4) to form the 1-buten-2-yl carbocation and radical, respectively, and related affinities for 2-methyl-1-butene (5) and 2-methyl-2-butene (6) using G3MP2B3 and CBS-QB3 protocols. Alkyne 1-OPP and allene 2-OPP were not substrates for Thermus thermophilus IDI-2 or Escherichia coli IDI-1, but instead were competitive inhibitors. The experimental and computational results are consistent with a protonation-deprotonation mechanism for the enzyme-catalyzed isomerization of IPP and DMAPP.
Starting in June 2010 the Infectious Diseases Institute (IDI) clinic (a large urban HIV out-patient facility) switched to provider-based Electronic Medical Records (EMR) from paper EMR entered in the database by data-entry clerks. Standardized clinics forms were eliminated but providers still fill free text clinical notes in physical patients’ files. The objective of this study was to compare the rate of errors in the database before and after the introduction of the provider-based EMR.
Methods and Findings
Data in the database pre and post provider-based EMR was compared with the information in the patients’ files and classified as correct, incorrect, and missing. We calculated the proportion of incorrect, missing and total error for key variables (toxicities, opportunistic infections, reasons for treatment change and interruption). Proportions of total errors were compared using chi-square test. A survey of the users of the EMR was also conducted. We compared data from 2,382 visits (from 100 individuals) of a retrospective validation conducted in 2007 with 34,957 visits (from 10,920 individuals) of a prospective validation conducted in April–August 2011. The total proportion of errors decreased from 66.5% in 2007 to 2.1% in 2011 for opportunistic infections, from 51.9% to 3.5% for ART toxicity, from 82.8% to 12.5% for reasons for ART interruption and from 94.1% to 0.9% for reasons for ART switch (all P<0.0001). The survey showed that 83% of the providers agreed that provider-based EMR led to improvement of clinical care, 80% reported improved access to patients’ records, and 80% appreciated the automation of providers’ tasks.
The introduction of provider-based EMR improved the quality of data collected with a significant reduction in missing and incorrect information. The majority of providers and clients expressed satisfaction with the new system. We recommend the use of provider-based EMR in large HIV programs in Sub-Saharan Africa.
Isopentenyl diphosphate isomerase from M. jannaschii has been overexpressed in E. coli, purified and crystallized. Diffraction data were collected to 2.08 Å resolution.
Type 2 isopentenyl diphosphate isomerase (IDI-2) is a flavoprotein. Recently, flavin has been proposed to play a role as a general acid–base catalyst with no redox role during the enzyme reaction. To clarify the detailed enzyme reaction mechanism of IDI-2 and the unusual role of flavin, structural analysis of IDI-2 from Methanocaldococcus jannaschii (MjIDI) was performed. Recombinant MjIDI was crystallized at 293 K using calcium acetate as a precipitant. The diffraction of the crystal extended to 2.08 Å resolution at 100 K. The crystal belonged to the tetragonal space group I422, with unit-cell parameters a = 126.46, c = 120.03 Å. The presence of one monomer per asymmetric unit gives a crystal volume per protein weight (V
M) of 3.0 Å3 Da−1 and a solvent constant of 59.0% by volume.
isopentenyl diphosphate isomerase; flavoproteins; no net redox reaction
The recently identified type II isopentenyl diphosphate (IPP):dimethylallyl diphosphate (DMAPP) isomerase (IDI-2) is a flavoenzyme that requires FMN and NAD(P)H for activity. IDI-2 is an essential enzyme for the biosynthesis of isoprenoids in several pathogenic bacteria including Staphylococcus aureus, Streptococcus pneumoniae, and Enterococcus faecalis, and thus is considered as a potential new drug target to battle bacterial infections. One notable feature of the IDI-2 reaction is that there is no net change in redox state between the substrate (IPP) and product (DMAPP), indicating that the FMN cofactor must start and finish each catalytic cycle in the same redox state. Here, we report the characterization and initial mechanistic studies of the S. aureus IDI-2. The steady-state kinetic analyses under aerobic and anaerobic conditions show that FMN must be reduced to be catalytically active and the overall IDI-2 reaction is O2 sensitive. Interestingly, our results demonstrate that NADPH is needed only in catalytic amounts to activate the enzyme for multiple turnovers of IPP to DMAPP. The hydride transfer from NAD(P)H to reduce FMN is determined to be pro-S stereospecific. Photoreduction and oxidation-reduction potential studies reveal that the S. aureus IDI-2 can stabilize significant amounts of the neutral FMN semiquinone. In addition, reconstitution of apo-IDI-2 with 5-deazaFMN resulted in a dead enzyme, whereas reconstitution with 1-deazaFMN led to the full recovery of enzyme activity. Taken together, these studies of S. aureus IDI-2 support a catalytic mechanism in which the reduced flavin coenzyme mediates a single electron transfer to and from the IPP substrate during catalysis.
Terpenoids constitute a large family of natural products, attracting commercial interest for a variety of uses as flavours, fragrances, drugs and alternative fuels. Saccharomyces cerevisiae offers a versatile cell factory, as the precursors of terpenoid biosynthesis are naturally synthesized by the sterol biosynthetic pathway.
S. cerevisiae wild type yeast cells, selected for their capacity to produce high sterol levels were targeted for improvement aiming to increase production. Recyclable integration cassettes were developed which enable the unlimited sequential integration of desirable genetic elements (promoters, genes, termination sequence) at any desired locus in the yeast genome. The approach was applied on the yeast sterol biosynthetic pathway genes HMG2, ERG20 and IDI1 resulting in several-fold increase in plant monoterpene and sesquiterpene production. The improved strains were robust and could sustain high terpenoid production levels for an extended period. Simultaneous plasmid-driven co-expression of IDI1 and the HMG2 (K6R) variant, in the improved strain background, maximized monoterpene production levels. Expression of two terpene synthase enzymes from the sage species Salvia fruticosa and S. pomifera (SfCinS1, SpP330) in the modified yeast cells identified a range of terpenoids which are also present in the plant essential oils. Co-expression of the putative interacting protein HSP90 with cineole synthase 1 (SfCinS1) also improved production levels, pointing to an additional means to improve production.
Using the developed molecular tools, new yeast strains were generated with increased capacity to produce plant terpenoids. The approach taken and the durability of the strains allow successive rounds of improvement to maximize yields.
Visceral adiposity index (VAI) has recently been developed based on waist circumference, body mass index (BMI), triglycerides (TGs), and high-density lipoprotein cholesterol (HDL-C). We examined predictive performances for incident diabetes of the VAI per se and as compared to the metabolic syndrome (MetS) and waist-to-height-ratio (WHtR).
Participants free of diabetes at baseline with at least one follow-up examination (5,964) were included for the current study. Weibull regression models were developed for interval-censored survival data. Absolute and relative integrated discriminatory improvement index (IDI) and cut-point-based and cut-point-free net reclassification improvement index (NRI) were used as measures of predictive ability for incident diabetes added by VAI, as compared to the MetS and WHtR.
The annual incidence rate of diabetes was 0.85 per 1000 person. Mean VAI was 3.06 (95%CIs 2.99-3.13). Diabetes risk factors levels increased in stepwise fashion across VAI quintiles. Risk gradient between the highest and lowest quintile of VAI was 4.5 (95%CIs 3.0-6.9). VAI significantly improved predictive ability of the MetS. The relative IDI and cut-point free NRI for predictive ability added to MetS by VAI were 30.3% (95%CIs 18.8-41.8%) and 30.7% (95%CIs 20.8-40.7%), respectively. WHtR, outperformed VAI with cut-point-free NRI of 24.6% (95%CIs 14.1-35.2%).
In conclusion, although VAI could be a prognostic tool for incident diabetes events, gathering information on its components (WC, BMI, TGs, and HDL-C) is unlikely to improve the prediction ability beyond what could be achieved by the simply assessable and commonly available information on WHtR.
There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993–2003.
biological markers; classification analysis; diagnostic tests, routine; epidemiologic methods; predictive value of tests; prostate-specific antigen; risk assessment; risk model
Suppose that we are interested in using new bio- or clinical-markers to improve prediction or diagnosis of the patient’s clinical outcome in addition to the conventional markers. The incremental value from the new markers is typically assessed by averaging across patients in the entire study population. However, when measuring the new markers is costly or invasive, an overall improvement does not justify measuring the new markers in all patients. A more practical strategy is to utilize the patient’s conventional markers to decide whether the new markers are needed for improving prediction of his/her health outcomes. In this article, we propose inference procedures for the incremental values of new markers across various subgroups of patients classified by the conventional markers. The resulting point and interval estimates can be quite useful for medical decision makers seeking to balance the predictive or diagnostic value of new markers against their associated cost and risk. Our proposals are theoretically justified and illustrated empirically with two real examples.
Biomarker; Cardiovascular events; Diagnosis; K-fold crossvalidation; Prediction accuracy; Subgroup analysis
Recently a new definition of surrogate endpoint, the ‘principal surrogate’, was proposed based on causal associations between treatment effects on the biomarker and on the clinical endpoint. Despite its appealing interpretation, limited research has been conducted to evaluate principal surrogates, and existing methods focus on risk models that consider a single biomarker. How to compare principal surrogate value of biomarkers or general risk models that consider multiple biomarkers remains an open research question. We propose to characterize a marker or risk model’s principal surrogate value based on the distribution of risk difference between interventions. In addition, we propose a novel summary measure (the standardized total gain) that can be used to compare markers and to assess the incremental value of a new marker. We develop a semiparametric estimated-likelihood method to estimate the joint surrogate value of multiple biomarkers. This method accommodates two-phase sampling of biomarkers and ismore widely applicable than existing nonparametric methods by incorporating continuous baseline covariates to predict the biomarker(s), and is more robust than existing parametric methods by leaving the error distribution of markers unspecified. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials.
Estimated likelihood; Predictiveness curve; Principal stratification; Semiparametric; Surrogate marker; Total gain
Cyanobacteria are prominent constituents of the marine biosphere that account for a significant percentage of oceanic primary productivity. In an effort to resolve how open-ocean cyanobacteria persist in regions where the Fe concentration is thought to be limiting their productivity, we performed a number of Fe stress experiments on axenic cultures of marine Synechococcus spp., Crocosphaera sp., and Trichodesmium sp. Through this work, we determined that all of these marine cyanobacteria mount adaptive responses to Fe stress, which resulted in the induction and/or repression of several proteins. We have identified one of the Fe stress-induced proteins as an IdiA homologue. Genomic observations and laboratory data presented herein from open-ocean Synechococcus spp. are consistent with IdiA having a role in cellular Fe scavenging. Our data indicate that IdiA may make an excellent marker for Fe stress in open-ocean cyanobacterial field populations. By determining how these microorganisms respond to Fe stress, we will gain insight into how and when this important trace element can limit their growth in situ. This knowledge will greatly increase our understanding of how marine Fe cycling impacts oceanic processes, such as carbon and nitrogen fixation.
Type 1 isopentenyl diphosphate isomerase (IDI-1) in a new crystal form.
Type 1 isopentenyl diphosphate isomerase (IDI-1) has been crystallized in a new crystal form. After data collection from small thin needle-shaped crystals, a new monoclinic form of the studied protein was identified. In this article, the three crystal forms of IDI-1 (orthorhombic, monoclinic and trigonal) are compared.
IPP isomerase; polymorphism