Estimating the Effect of PrEP in Black Men Who Have Sex with Men: A Framework to Utilize Data from Multiple Non-Randomized Studies to Estimate Causal Effects.

Black men who have sex with men (MSM) are disproportionately burdened by the HIV epidemic in the US. The effectiveness of pre-exposure prophylaxis (PrEP) in preventing HIV infection has been demonstrated through randomized placebo-controlled clinical trials in several populations. Importantly, no such trial has been conducted exclusively among Black MSM in the US, and it would be unethical and infeasible to do so now. To estimate the causal effects of PrEP access, initiation, and adherence on HIV risk, we utilized causal inference methods to combine data from two non-randomized studies that exclusively enrolled Black MSM. The estimated relative risks of HIV were: (i) 0.52 (95% confidence interval: 0.21, 1.22) for individuals with versus without PrEP access, (ii) 0.48 (0.12, 0.89) for individuals who initiated PrEP but were not adherent versus those who did not initiate, and (iii) 0.23 (0.02, 0.80) for individuals who were adherent to PrEP versus those who did not initiate. Beyond addressing the knowledge gap around the effect of PrEP in Black MSM in the US, which may have ramifications for public health, we have provided a framework to combine data from multiple non-randomized studies to estimate causal effects, which has broad utility.

Duration of viral infectiousness and correlation with symptoms and diagnostic testing in non-hospitalized adults during acute SARS-CoV-2 infection: A longitudinal cohort study.

BACKGROUND: Guidelines for SARS-CoV-2 have relied on limited data on duration of viral infectiousness and correlation with COVID-19 symptoms and diagnostic testing. METHODS: We enrolled ambulatory adults with acute SARS-CoV-2 infection and performed serial measurements of COVID-19 symptoms, nasal swab viral RNA, nucleocapsid (N) and spike (S) antigens, and replication-competent SARS-CoV-2 by viral growth in culture. We determined average time from symptom onset to a first negative test result and estimated risk of infectiousness, as defined by positive viral growth in culture. RESULTS: Among 95 adults, median [interquartile range] time from symptom onset to first negative test result was 9 [5] days, 13 [6] days, 11 [4] days, and >19 days for S antigen, N antigen, culture growth, and viral RNA by RT-PCR, respectively. Beyond two weeks, virus growth and N antigen titers were rarely positive, while viral RNA remained detectable among half (26/51) of participants tested 21-30 days after symptom onset. Between 6-10 days from symptom onset, N antigen was strongly associated with culture positivity (relative risk=7.61, 95% CI: 3.01-19.22), whereas neither viral RNA nor symptoms were associated with culture positivity. During the 14 days following symptom onset, the presence of N antigen remained strongly associated (adjusted relative risk=7.66, 95% CI: 3.96-14.82) with culture positivity, regardless of COVID-19 symptoms. CONCLUSIONS: Most adults have replication-competent SARS-CoV-2 for 10-14 after symptom onset. N antigen testing is a strong predictor of viral infectiousness and may be a more suitable biomarker, rather than absence of symptoms or viral RNA, to discontinue isolation within two weeks from symptom onset.

PrEP uptake and HIV viral suppression when PrEP is integrated into Ugandan ART clinics for HIV-negative members of HIV-serodifferent couples: A stepped wedge cluster randomized trial.

Background: Global scale-up of HIV pre-exposure prophylaxis (PrEP) includes services to HIV-negative people in partnerships with people living with HIV (serodifferent couples). Data are needed on HIV outcomes, including uptake and adherence to PrEP and antiretroviral treatment (ART), to describe the impact of integrating PrEP into an existing HIV program. Methods: Using a stepped-wedge cluster randomized trial design, we launched PrEP delivery for HIV-negative members of serodifferent couples in Uganda by integrating PrEP into existing ART programs for people living with HIV. The program provided PrEP training for ART providers, ongoing technical assistance, and a provisional supply chain mechanism for PrEP medication. Primary data on PrEP initiation, PrEP refills, ART initiation, and HIV viremia at 6 months (measured at 42-270 days) were collected through data abstraction of medical records from HIV-serodifferent couples sequentially enrolling at the ART clinics. Modified Poisson regression models, controlling for time and cluster, compared viral suppression (<1000 copies/ml) before and after launch of the PrEP program. This trial was registered at ClinicalTrials.gov, NCT03586128. Findings: From June 1, 2018-December 15, 2020, 1,381 HIV-serodifferent couples were enrolled across 12 ART clinics in Kampala and Wakiso, Uganda, including 730 enrolled before and 651 after the launch of PrEP delivery. During the baseline period, 99.4% of partners living with HIV initiated ART and 85.0% were virally suppressed at 6 months. Among HIV-negative partners enrolled after PrEP launched, 81.0% (527/651) initiated PrEP within 90 days of enrolling; among these 527, 11.2% sought a refill 6 months later. In our powered intent-to-treat analysis, 82.1% and 76.7% of partners living with HIV were virally suppressed, respectively, which was not a statistically significant difference (RR=0.94, 95% CI: 0.82-1.07) and was stable across sensitivity analyses. Interpretation: Integration of PrEP into ART clinics reached a high proportion of people in HIV-serodifferent relationships and did not improve the already high frequency of HIV viral suppression among partners living with HIV. Funding: National Institute of Mental Health (R01MH110296).

Performance of anterior nares and tongue swabs for nucleic acid, Nucleocapsid, and Spike antigen testing for detecting SARS-CoV-2 against nasopharyngeal PCR and viral culture.

OBJECTIVES: This study assesses and compares the performance of different swab types and specimen collection sites for SARS-CoV-2 testing, to reference standard real-time reverse transcriptase-polymerase chain reaction (RT-PCR) and viral culture. METHODS: Symptomatic adults with COVID-19 who visited routine COVID-19 testing sites used spun polyester and FLOQSwabs to self-collect specimens from the anterior nares and tongue. We evaluated the self-collected specimen from anterior nares and tongue swabs for the nucleocapsid (N) or spike (S) antigen of SARS-CoV-2 by RT-PCR and then compared these results with results from RT-PCR and viral cultures from nurse-collected nasopharyngeal swabs. RESULTS: Diagnostic sensitivity was highest for RT-PCR testing conducted using specimens from the anterior nares collected on FLOQSwabs (84%; 95% CI 68-94%) and spun polyester swabs (82%; 95% CI 66-92%), compared to RT-PCR tests conducted using specimens from nasopharyngeal swabs. Relative to viral culture from nasopharyngeal swabs, diagnostic sensitivities were higher for RT-PCR and antigen testing of anterior nares swabs (91-100%) than that of tongue swabs (18-81%). Antigen testing of anterior nares swabs had higher sensitivities against viral culture (91%) than against nasopharyngeal RT-PCR (38-70%). All investigational tests had high specificity compared with nasopharyngeal RT-PCR. Spun polyester swabs are equally effective as FLOQSwabs for anterior nasal RT-PCR testing. CONCLUSIONS: We found that anterior nares specimens were more sensitive than tongue swab specimens or antigen testing for detecting SARS-CoV-2 by RT-PCR. Thus, self-collected anterior nares specimens may represent an alternative method for diagnostic SARS-CoV-2 testing in some settings.

Integrating PrEP delivery in public health family planning clinics: a protocol for a pragmatic stepped wedge cluster randomized trial in Kenya.

BACKGROUND: Adolescent girls and young women account for a disproportionate fraction of new HIV infections in Africa and are a priority population for HIV prevention, including provision of pre-exposure prophylaxis (PrEP). Anchoring PrEP delivery to care settings like family planning (FP) services that women already access routinely may offer an efficient platform to reach HIV at-risk women. However, context-specific implementation science evaluation is needed. METHODS: The Family Planning Plus Project is a prospective, pragmatic implementation evaluation, designed as a stepped wedge, cluster randomized trial, at 12 clinics in Kenya. In collaboration with the Kenya Ministry of Health and Kisumu County Department of Health, we will introduce integration of HIV risk screening and PrEP delivery in public health FP clinics. The core multifaceted implementation strategies to integrate PrEP in FP clinics will include: (1) PrEP delivery by existing FP clinic staff, (2) health provider training, (3) PrEP technical assistance to coach and mentor providers, (4) joint supervision with Kisumu County health officials, and (5) stakeholder engagement. All core components of PrEP delivery-including screening for HIV risk, HIV testing, dispensing, adherence and risk reduction counseling, assessment of side effects, and provision of refills, or safety assessment-will be conducted by existing FP clinic staff as part of a standard care service package. The goal is to catalyze sustainable scale-up within existing infrastructures beyond the project. We will rigorously evaluate implementation outcomes and impact, using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework, and we will use Organizational Readiness for Implementing Change (ORIC) and the Consolidated Framework for Implementation Science Research (CFIR) to assess readiness to implement and contextual enablers and barriers of implementation, including how clinics innovate efficient delivery systems. DISCUSSION: Anchoring PrEP delivery to existing FP systems and staffing has tremendous potential to address barriers that women face in accessing HIV prevention and PrEP care, including lack of time, cost, and stigma of visiting a facility solely for HIV prevention. The FP Plus Project will initiate preparation for full-scale and sustainable model of integration of comprehensive HIV prevention services, including PrEP implementation, in public health FP clinics in low-income settings. Trial registration Registered with ClinicalTrials.gov on December 14, 2020: NCT04666792.

Combining biomarkers by maximizing the true positive rate for a fixed false positive rate.

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate (TPR) for a biomarker combination at a prespecified, clinically acceptable false positive rate (FPR) is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the TPR while constraining the FPR. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations. Thus, use of our method could lead to the development of better biomarker combinations, increasing the likelihood of clinical adoption.

Developing biomarker combinations in multicenter studies via direct maximization and penalization.

Motivated by a study of acute kidney injury, we consider the setting of biomarker studies involving patients at multiple centers where the goal is to develop a biomarker combination for diagnosis, prognosis, or screening. As biomarker studies become larger, this type of data structure will be encountered more frequently. In the presence of multiple centers, one way to assess the predictive capacity of a given combination is to consider the center-adjusted area under the receiver operating characteristic curve (aAUC), a summary of the ability of the combination to discriminate between cases and controls in each center. Rather than using a general method, such as logistic regression, to construct the biomarker combination, we propose directly maximizing the aAUC. Furthermore, it may be desirable to have a biomarker combination with similar performance across centers. To that end, we allow for penalization of the variability in the center-specific AUCs. We demonstrate desirable asymptotic properties of the resulting combinations. Simulations provide small-sample evidence that maximizing the aAUC can lead to combinations with improved performance. We also use simulated data to illustrate the utility of constructing combinations by maximizing the aAUC while penalizing variability. Finally, we apply these methods to data from the study of acute kidney injury.

Combined Utility of 25 Disease and Risk Factor Polygenic Risk Scores for Stratifying Risk of All-Cause Mortality.

While genome-wide association studies have identified susceptibility variants for numerous traits, their combined utility for predicting broad measures of health, such as mortality, remains poorly understood. We used data from the UK Biobank to combine polygenic risk scores (PRS) for 13 diseases and 12 mortality risk factors into sex-specific composite PRS (cPRS). These cPRS were moderately associated with all-cause mortality in independent data within the UK Biobank: the estimated hazard ratios per standard deviation were 1.10 (95% confidence interval: 1.05, 1.16) and 1.15 (1.10, 1.19) for women and men, respectively. Differences in life expectancy between the top and bottom 5% of the cPRS were estimated to be 4.79 (1.76, 7.81) years and 6.75 (4.16, 9.35) years for women and men, respectively. These associations were substantially attenuated after adjusting for non-genetic mortality risk factors measured at study entry (i.e., middle age for most participants). The cPRS may be useful in counseling younger individuals at higher genetic risk of mortality on modification of non-genetic factors.

Case-Only Analysis of Gene-Environment Interactions Using Polygenic Risk Scores.

Investigations of gene (G)-environment (E) interactions have led to limited findings to date, possibly due to weak effects of individual genetic variants. Polygenic risk scores (PRS), which capture the genetic susceptibility associated with a set of variants, can be a powerful tool for detecting global patterns of interaction. Motivated by the case-only method for evaluating interactions with a single variant, we propose a case-only method for the analysis of interactions with a PRS in case-control studies. Assuming the PRS and E are independent, we show how a linear regression of the PRS on E in a sample of cases can be used to efficiently estimate the interaction parameter. Furthermore, if an estimate of the mean of the PRS in the underlying population is available, the proposed method can estimate the PRS main effect. Extensions allow for PRS-E dependence due to associations between variants in the PRS and E. Simulation studies indicate the proposed method offers appreciable gains in efficiency over logistic regression and can recover much of the efficiency of a cohort study. We applied the proposed method to investigate interactions between a PRS and epidemiologic factors on breast cancer risk in the UK Biobank (United Kingdom, recruited 2006-2010).

Biomarker combinations for diagnosis and prognosis in multicenter studies: Principles and methods.

Many investigators are interested in combining biomarkers to predict a binary outcome or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using random intercept logistic regression models, an approach that we demonstrate is generally inadequate and may be misleading. We instead propose using fixed intercept logistic regression, which appropriately accounts for center without relying on untenable assumptions. After constructing the biomarker combination, we recommend using performance measures that account for the multicenter nature of the data, namely the center-adjusted area under the receiver operating characteristic curve. We apply these methods to data from a multicenter study of acute kidney injury after cardiac surgery. Appropriately accounting for center, both in construction and evaluation, may increase the likelihood of identifying clinically useful biomarker combinations.

Development of biomarker combinations for postoperative acute kidney injury via Bayesian model selection in a multicenter cohort study.

BACKGROUND: Acute kidney injury (AKI) is a frequent complication of cardiac surgery. We sought prognostic combinations of postoperative biomarkers measured within 6 h of surgery, potentially in combination with cardiopulmonary bypass time (to account for the degree of insult to the kidney). We used data from a large cohort of patients and adapted methods for developing biomarker combinations to account for the multicenter design of the study. METHODS: The primary endpoint was sustained mild AKI, defined as an increase of 50% or more in serum creatinine over preoperative levels lasting at least 2 days during the hospital stay. Severe AKI (secondary endpoint) was defined as a serum creatinine increase of 100% or more or dialysis during hospitalization. Data were from a cohort of 1219 adults undergoing cardiac surgery at 6 medical centers; among these, 117 developed sustained mild AKI and 60 developed severe AKI. We considered cardiopulmonary bypass time and 22 biomarkers as candidate predictors. We adapted Bayesian model averaging methods to develop center-adjusted combinations for sustained mild AKI by (1) maximizing the posterior model probability and (2) retaining predictors with posterior variable probabilities above 0.5. We used resampling-based methods to avoid optimistic bias in evaluating the biomarker combinations. RESULTS: The maximum posterior model probability combination included plasma N-terminal-pro-B-type natriuretic peptide, plasma heart-type fatty acid binding protein, and change in serum creatinine from before to 0-6 h after surgery; the median probability combination additionally included plasma interleukin-6. The center-adjusted, optimism-corrected AUCs for these combinations were 0.80 (95% CI: 0.78, 0.87) and 0.81 (0.78, 0.87), respectively, for predicting sustained mild AKI, and 0.81 (0.76, 0.90) and 0.83 (0.76, 0.90), respectively, for predicting severe AKI. For these data, the Bayesian model averaging methods yielded combinations with prognostic capacity comparable to that achieved by standard frequentist methods but with more parsimonious models. CONCLUSIONS: Pending external validation, the identified combinations could be used to identify individuals at high risk of AKI immediately after cardiac surgery and could facilitate clinical trials of renoprotective agents.

Using ordinal outcomes to construct and select biomarker combinations for single-level prediction.

BACKGROUND: Biomarker studies may involve an ordinal outcome, such as no, mild, or severe disease. There is often interest in predicting one particular level of the outcome due to its clinical significance. METHODS: A simple approach to constructing biomarker combinations in this context involves dichotomizing the outcome and using a binary logistic regression model. We assessed whether more sophisticated methods offer advantages over this simple approach. It is often necessary to select among several candidate biomarker combinations. One strategy involves selecting a combination based on its ability to predict the outcome level of interest. We propose an algorithm that leverages the ordinal outcome to inform combination selection. We apply this algorithm to data from a study of acute kidney injury after cardiac surgery, where kidney injury may be absent, mild, or severe. RESULTS: Using more sophisticated modeling approaches to construct combinations provided gains over the simple binary logistic regression approach in specific settings. In the examples considered, the proposed algorithm for combination selection tended to reduce the impact of bias due to selection and to provide combinations with improved performance. CONCLUSIONS: Methods that utilize the ordinal nature of the outcome in the construction and/or selection of biomarker combinations have the potential to yield better combinations.

Evaluating biomarkers for prognostic enrichment of clinical trials.

BACKGROUND/AIMS: A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. METHODS: We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. RESULTS: We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. CONCLUSION: In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Urinalysis findings and urinary kidney injury biomarker concentrations.

INTRODUCTION: Urinary biomarkers of kidney injury are presumed to reflect renal tubular damage. However, their concentrations may be influenced by other factors, such as hematuria or pyuria. We sought to examine what non-injury related urinalysis factors are associated with urinary biomarker levels. METHODS: We examined 714 adults who underwent cardiac surgery in the TRIBE-AKI cohort that did not experience post-operative clinical AKI (patients with serum creatinine change of ≥ 20% were excluded). We examined the association between urinalysis findings and the pre- and first post-operative urinary concentrations of 4 urinary biomarkers: neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), and liver fatty acid binding protein (L-FABP). RESULTS: The presence of leukocyte esterase and nitrites on urinalysis was associated with increased urinary NGAL (R2 0.16, p < 0.001 and R2 0.07, p < 0.001, respectively) in pre-operative samples. Hematuria was associated with increased levels of all 4 biomarkers, with a much stronger association seen in post-operative samples (R2 between 0.02 and 0.21). Dipstick proteinuria concentrations correlated with levels of all 4 urinary biomarkers in pre-operative and post-operative samples (R2 between 0.113 and 0.194 in pre-operative and between 0.122 and 0.322 in post-operative samples). Adjusting the AUC of post-operative AKI for dipstick proteinuria lowered the AUC for all 4 biomarkers at the pre-operative time point and for 2 of the 4 biomarkers at the post-operative time point. CONCLUSIONS: Several factors available through urine dipstick testing are associated with increased urinary biomarker concentrations that are independent of clinical kidney injury. Future studies should explore the impact of these factors on the prognostic and diagnostic performance of these AKI biomarkers.

Pre-diagnostic circulating sex hormone levels and risk of prostate cancer by ERG tumour protein expression.

BACKGROUND: Experimental studies have shown androgen receptor stimulation to facilitate formation of the TMPRSS2:ERG gene fusion in prostate cell lines. No study has tested whether higher pre-diagnostic circulating sex hormone levels in men increase risk of developing TMPRSS2:ERG-positive prostate cancer specifically. METHODS: We conducted a nested case-control study of 200 prostate cancer cases and 1057 controls from the Physicians' Health Study and Health Professionals Follow-up Study. We examined associations between pre-diagnostic circulating levels of total testosterone, free testosterone, DHT, androstanediol glucuronide, estradiol, and SHBG and risk of prostate cancer by TMPRSS2:ERG status. TMPRSS2:ERG was estimated by ERG immunohistochemistry. We used multivariable unconditional polytomous logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for risk of ERG-positive (n=94) and, separately, ERG-negative (n=106) disease. RESULTS: Free testosterone was significantly associated with the risk of ERG-positive prostate cancer (OR: 1.37, 95% CI: 1.05-1.77), but not ERG-negative prostate cancer (OR: 1.09, 95% CI: 0.86-1.38) (Pdiff=0.17). None of the remaining hormones evaluated showed clear differential associations with ERG-positive vs ERG-negative disease. CONCLUSIONS: These findings provide some suggestive evidence that higher pre-diagnostic free testosterone levels are associated with an increased risk of developing TMPRSS2:ERG-positive prostate cancer.

Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury.

Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict AKI were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias, and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles), or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknowledge the bias. Inadequate reporting often hindered our assessment of the articles. We then evaluated, when possible (7 articles), the performance of published biomarker combinations in the TRIBE-AKI cardiac surgery cohort. Predictive performance was markedly attenuated in six out of seven cases. Thus, deficiencies in analysis and reporting are avoidable, and care should be taken to provide accurate estimates of risk prediction model performance. Hence, rigorous design, analysis, and reporting of biomarker combination studies are essential to realizing the promise of biomarkers in clinical practice.

Validity of a Neurological Scoring System for Canine X-Linked Myotubular Myopathy.

A simple clinical neurological test was developed to evaluate response to gene therapy in a preclinical canine model of X-linked myotubular myopathy (XLMTM). This devastating congenital myopathy is caused by mutation in the myotubularin (MTM1) gene. Clinical signs include muscle weakness, early respiratory failure, and ventilator dependence. A spontaneously occurring canine model has a similar clinical picture and histological abnormalities on muscle biopsy compared with patients. We developed a neuromuscular assessment score, graded on a scale from 10 (normal) to 1 (unable to maintain sternal recumbency). We hypothesize that this neurological assessment score correlates with genotype and established measures of disease severity and is reliable when performed by an independent observer. At 17 weeks of age, there was strong correlation between neurological assessment scores and established methods of severity testing. The neurological severity score correctly differentiated between XLMTM and wild-type dogs with good interobserver reliability, on the basis of strong agreement between neurological scores assigned by independent observers. Together, these data indicate that the neurological scoring system developed for this canine congenital neuromuscular disorder is reliable and valid. This scoring system may be helpful in evaluating response to therapy in preclinical testing in this disease model, such as response to gene therapy.

RiGoR: reporting guidelines to address common sources of bias in risk model development.

Reviewing the literature in many fields on proposed risk models reveals problems with the way many risk models are developed. Furthermore, papers reporting new risk models do not always provide sufficient information to allow readers to assess the merits of the model. In this review, we discuss sources of bias that can arise in risk model development. We focus on two biases that can be introduced during data analysis. These two sources of bias are sometimes conflated in the literature and we recommend the terms resubstitution bias and model-selection bias to delineate them. We also propose the RiGoR reporting standard to improve transparency and clarity of published papers proposing new risk models.

Androgen receptor CAG repeat polymorphism and risk of TMPRSS2:ERG-positive prostate cancer.

BACKGROUND: The androgen receptor (AR) is an essential gene in prostate cancer pathogenesis and progression. Genetic variation in AR exists, including a polymorphic CAG repeat sequence that is inversely associated with transcriptional activity. Experimental data suggest that heightened AR activity facilitates formation of TMPRSS2:ERG, a gene fusion present in approximately 50% of tumors of patients with prostate cancer. METHODS: We undertook a nested case-control study to investigate the hypothesis that shorter CAG repeat length would be associated with prostate cancer risk defined by TMPRSS2:ERG status. The study included 291 men with prostate cancer (147 ERG-positive) and 1,221 cancer-free controls. ORs and 95% confidence intervals (CI) were calculated using logistic regression. RESULTS: Median CAG repeat length (interquartile range) among controls was 22 (20-24). Men with shorter CAG repeats had an increased risk of ERG-positive (OR, 1.07 per 1 repeat decrease; 95% CI, 1.00-1.14), but not ERG-negative prostate cancer (OR, 0.99 per 1 repeat decrease; 95% CI, 0.93-1.05). CONCLUSIONS: These data suggest that shorter CAG repeats are specifically associated with development of TMPRSS2:ERG-positive prostate cancer. IMPACT: Our results provide supportive evidence that androgen signaling underlies the development of prostate tumors that harbor TMPRSS2:ERG. Moreover, these results suggest that TMPRSS2:ERG may represent a unique molecular subtype of prostate cancer with an etiology distinct from TMPRSS2:ERG-negative disease.

Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker.