Characterizing Imbalance in the Tails of the Propensity Score Distribution.

Understanding characteristics of patients with propensity scores in the tails of the propensity score (PS) distribution has relevance for inverse-probability-of-treatment-weighted and PS-based estimation in observational studies. Here we outline a method for identifying variables most responsible for extreme propensity scores. The approach is illustrated in 3 scenarios: 1) a plasmode simulation of adult patients in the National Ambulatory Medical Care Survey (2011-2015) and 2) timing of dexamethasone initiation and 3) timing of remdesivir initiation in patients hospitalized for coronavirus disease 2019 from February 2020 through January 2021. PS models were fitted using relevant baseline covariates, and tails of the PS distribution were defined using asymmetric first and 99th percentiles. After fitting of the PS model in each original data set, values of each key covariate were permuted and model-agnostic variable importance measures were examined. Visualization and variable importance techniques were helpful in identifying variables most responsible for extreme propensity scores and may help identify individual characteristics that might make patients inappropriate for inclusion in a study (e.g., off-label use). Subsetting or restricting the study sample based on variables identified using this approach may help investigators avoid the need for trimming or overlap weights in studies.

Risk of acute pancreatitis among new users of empagliflozin compared to sulfonylureas in patients with type 2 diabetes: A post-authorization safety study.

PURPOSE: This study was undertaken to evaluate the potential risk of acute pancreatitis with empagliflozin in patients with type 2 diabetes (T2D) newly initiating empagliflozin. METHODS: Data from two large US claims databases were analyzed in an observational study of patients with T2D receiving metformin who were newly prescribed empagliflozin versus sulfonylurea (SU). Because dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists have been associated with the risk of acute pancreatitis in some studies, patients on these agents were excluded. Using pooled analyses of data from the two databases (2014-2021), patients initiating empagliflozin were matched 1:1 within database to patients initiating SU using propensity scores (PS) that incorporated relevant demographic and clinical characteristics. Prespecified sensitivity analyses were performed for design parameters. RESULTS: The analyses identified 72 661 new users of empagliflozin and 422 018 new users of SUs, with both patient groups on concurrent metformin therapy. Baseline characteristics within treatment groups appeared to be similar across the 72 621 matched pairs. After mean follow-up of ~6 months, incidence rates of acute pancreatitis in the pooled matched cohort were 10.30 (95% confidence interval [CI] 9.29-11.39) events per 1000 patient-years (PY) for empagliflozin and 11.65 (95% CI 10.59-12.77) events per 1000 PY for SUs. On a background of metformin, patients newly initiating empagliflozin did not have an increased risk of acute pancreatitis compared with those initiating an SU (pooled PS matched hazard ratio 0.88 [0.76-1.02]) across 75621.42 PY of follow-up. CONCLUSIONS: The results of this voluntary post-approval safety study provide additional evidence that the use of empagliflozin for the treatment of T2D is not associated with an increased risk of acute pancreatitis.

Clarifying the causal contrast: An empirical example applying the prevalent new user study design.

PURPOSE: The prevalent new user design extends the active comparator new user design to include patients switching to a treatment of interest from a comparator. We examined the impact of adding "switchers" to incident new users on the estimated hazard ratio (HR) of hospitalized heart failure. METHODS: Using MarketScan claims data (2000-2014), we estimated HRs of hospitalized heart failure between patients initiating GLP-1 receptor agonists (GLP-1 RA) and sulfonylureas (SU). We considered three estimands: (1) the effect of incident new use; (2) the effect of switching; and (3) the effect of incident new use or switching, combining the two population. We used time-conditional propensity scores (TCPS) and time-stratified standardized morbidity ratio (SMR) weighting to adjust for confounding. RESULTS: We identified 76 179 GLP-1 RA new users, of which 12% were direct switchers (within 30 days) from SU. Among incident new users, GLP-1 RA was protective against heart failure (adjHRSMR = 0.74 [0.69, 0.80]). Among switchers, GLP-1 RA was not protective (adjHRSMR = 0.99 [0.83, 1.18]). Results in the combined population were largely driven by the incident new users, with GLP-1 RA having a protective effect (adjHRSMR = 0.77 [0.72, 0.83]). Results using TCPS were consistent with those estimated using SMR weighting. CONCLUSIONS: When analyses were conducted only among incident new users, GLP-1 RA had a protective effect. However, among switchers from SU to GLP-1 RA, the effect estimates substantially shifted toward the null. Combining patients with varying treatment histories can result in poor confounding control and camouflage important heterogeneity.

Sensitivity analysis of methods for active surveillance of acute myocardial infarction using electronic databases.

BACKGROUND: The validity of conclusions from observational studies depends on decisions regarding design, analysis, data quality, and implementation. Through sensitivity analyses, we explored the impact of such decisions on balance control and risk estimates. METHODS: Using as a template the Mini-Sentinel protocol for the active surveillance of acute myocardial infarction (MI) in association with use of antidiabetic agents, we defined cohorts of new users of metformin and second-generation sulfonylureas, baseline covariates and acute MI events using three combinations of washout and baseline periods. Using propensity-score matching, we assessed balance control and risk estimates using cumulative data for matching all patients compared with not rematching prior matches in quarterly analyses over the follow-up period. RESULTS: A longer washout period increased the confidence in new-user status, but at the expense of sample size; a longer baseline period improved capture of covariates related to pre-existing chronic conditions. When all patients were matched each quarter, balance was improved and risk estimates were more robust, especially in the later quarters. CONCLUSIONS: Durations of washout and baseline periods influence the likelihood of new-user status and sample size. Matching all patients tends to result in better covariate balance than matching only new patients. Decisions regarding the durations of washout and baseline periods depend on the specific research question and availability of longitudinal patient data within the database. This paper demonstrates the importance and utility of sensitivity analysis of methods for evaluating the robustness of results in observational studies.

Matching on the disease risk score in comparative effectiveness research of new treatments.

PURPOSE: We use simulations and an empirical example to evaluate the performance of disease risk score (DRS) matching compared with propensity score (PS) matching when controlling large numbers of covariates in settings involving newly introduced treatments. METHODS: We simulated a dichotomous treatment, a dichotomous outcome, and 100 baseline covariates that included both continuous and dichotomous random variables. For the empirical example, we evaluated the comparative effectiveness of dabigatran versus warfarin in preventing combined ischemic stroke and all-cause mortality. We matched treatment groups on a historically estimated DRS and again on the PS. We controlled for a high-dimensional set of covariates using 20% and 1% samples of Medicare claims data from October 2010 through December 2012. RESULTS: In simulations, matching on the DRS versus the PS generally yielded matches for more treated individuals and improved precision of the effect estimate. For the empirical example, PS and DRS matching in the 20% sample resulted in similar hazard ratios (0.88 and 0.87) and standard errors (0.04 for both methods). In the 1% sample, PS matching resulted in matches for only 92.0% of the treated population and a hazard ratio and standard error of 0.89 and 0.19, respectively, while DRS matching resulted in matches for 98.5% and a hazard ratio and standard error of 0.85 and 0.16, respectively. CONCLUSIONS: When PS distributions are separated, DRS matching can improve the precision of effect estimates and allow researchers to evaluate the treatment effect in a larger proportion of the treated population. However, accurately modeling the DRS can be challenging compared with the PS.

The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score.

The covariate-balancing propensity score (CBPS) extends logistic regression to simultaneously optimize covariate balance and treatment prediction. Although the CBPS has been shown to perform well in certain settings, its performance has not been evaluated in settings specific to pharmacoepidemiology and large database research. In this study, we use both simulations and empirical data to compare the performance of the CBPS with logistic regression and boosted classification and regression trees. We simulated various degrees of model misspecification to evaluate the robustness of each propensity score (PS) estimation method. We then applied these methods to compare the effect of initiating glucagonlike peptide-1 agonists versus sulfonylureas on cardiovascular events and all-cause mortality in the US Medicare population in 2007-2009. In simulations, the CBPS was generally more robust in terms of balancing covariates and reducing bias compared with misspecified logistic PS models and boosted classification and regression trees. All PS estimation methods performed similarly in the empirical example. For settings common to pharmacoepidemiology, logistic regression with balance checks to assess model specification is a valid method for PS estimation, but it can require refitting multiple models until covariate balance is achieved. The CBPS is a promising method to improve the robustness of PS models.

Assessing the impact of propensity score estimation and implementation on covariate balance and confounding control within and across important subgroups in comparative effectiveness research.

PURPOSE: Researchers are often interested in estimating treatment effects in subgroups controlling for confounding based on a propensity score (PS) estimated in the overall study population. OBJECTIVE: To evaluate covariate balance and confounding control in sulfonylurea versus metformin initiators within subgroups defined by cardiovascular disease (CVD) history comparing an overall PS with subgroup-specific PSs implemented by 1:1 matching and stratification. METHODS: We analyzed younger patients from a US insurance claims database and older patients from 2 Medicare (Humana Medicare Advantage, fee-for-service Medicare Parts A, B, and D) datasets. Confounders and risk factors for acute myocardial infarction were included in an overall PS and subgroup PSs with and without CVD. Covariate balance was assessed using the average standardized absolute mean difference (ASAMD). RESULTS: Compared with crude estimates, ASAMD across covariates was improved 70%-94% for stratification for Medicare cohorts and 44%-99% for the younger cohort, with minimal differences between overall and subgroup-specific PSs. With matching, 75%-99% balance improvement was achieved regardless of cohort and PS, but with smaller sample size. Hazard ratios within each CVD subgroup differed minimally among PS and cohorts. CONCLUSIONS: Both overall PSs and CVD subgroup-specific PSs achieved good balance on measured covariates when assessing the relative association of diabetes monotherapy with nonfatal myocardial infarction. PS matching generally led to better balance than stratification, but with smaller sample size. Our study is limited insofar as crude differences were minimal, suggesting that the new user, active comparator design identified patients with some equipoise between treatments.

Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study.

PURPOSE: It is often preferable to simplify the estimation of treatment effects on multiple outcomes by using a single propensity score (PS) model. Variable selection in PS models impacts the efficiency and validity of treatment effects. However, the impact of different variable selection strategies on the estimated treatment effects in settings involving multiple outcomes is not well understood. The authors use simulations to evaluate the impact of different variable selection strategies on the bias and precision of effect estimates to provide insight into the performance of various PS models in settings with multiple outcomes. METHODS: Simulated studies consisted of dichotomous treatment, two Poisson outcomes, and eight standard-normal covariates. Covariates were selected for the PS models based on their effects on treatment, a specific outcome, or both outcomes. The PSs were implemented using stratification, matching, and weighting (inverse probability treatment weighting). RESULTS: PS models including only covariates affecting a specific outcome (outcome-specific models) resulted in the most efficient effect estimates. The PS model that only included covariates affecting either outcome (generic-outcome model) performed best among the models that simultaneously controlled measured confounding for both outcomes. Similar patterns were observed over the range of parameter values assessed and all PS implementation methods. CONCLUSIONS: A single, generic-outcome model performed well compared with separate outcome-specific models in most scenarios considered. The results emphasize the benefit of using prior knowledge to identify covariates that affect the outcome when constructing PS models and support the potential to use a single, generic-outcome PS model when multiple outcomes are being examined.

Can Weight of Evidence, Quantitative Bias, and Bounding Methods Evaluate Robustness of Real-world Evidence for Regulator and Health Technology Assessment Decisions on Medical Interventions?

PURPOSE: High-quality evidence is crucial for health care intervention decision-making. These decisions frequently use nonrandomized data, which can be more vulnerable to biases than randomized trials. Accordingly, methods to quantify biases and weigh available evidence could elucidate the robustness of findings, giving regulators more confidence in making approval and reimbursement decisions. METHODS: We conducted an integrative literature review to identify methods for determining probability of causation, evaluating weight of evidence, and conducting quantitative bias analysis as related to health care interventions. Eligible studies were published from 2012 to 2021, applicable to pharmacoepidemiology, and presented a method that met our objective. FINDINGS: Twenty-two eligible studies were classified into 4 categories: (1) quantitative bias analysis; (2) weight of evidence methods; (3) Bayesian networks; and (4) miscellaneous. All of the methods have strengths, limitations, and situations in which they are more well suited than others. Some methods seem to lend themselves more to applications of health care evidence on medical interventions than others. IMPLICATIONS: To provide robust evidence for and improve confidence in regulatory or reimbursement decisions, we recommend applying multiple methods to triangulate associations of medical interventions, accounting for biases in different ways. This approach could lead to well-defined robustness assessments of study findings and appropriate science-driven decisions by regulators and payers for public health.

Strategy for Generating Blinded Evidence for Single-Arm Trials with External Controls Using Expert Review of Home Video.

INTRODUCTION: Neurodegenerative diseases cause developmental delays and loss of milestones in infants and children. However, scalable outcome measures that quantify features meaningful to parents/caregivers (P/CGs) and have regulatory precedence are lacking for assessing the effectiveness of treatments in clinical trials of neurodegenerative disorders. To address this gap, we developed an innovative, blinded strategy for single-arm trials with external controls using expert panel review of home video. METHOD: We identified meaningful, observable, and objective developmental milestones from iterative interviews with P/CGs and clinical experts. Subsequently, we standardized video recording procedures and instructions to ensure consistency in how P/CGs solicited each activity. In practice, videos would be graded by an expert panel blinded to treatment. To ensure blinding and quality control, video recordings from interim time points would be randomly interspersed. We conducted a pilot study and a pretest of grading to test feasibility and improve the final strategy. RESULTS: The five P/CGs participating in the pilot study found the instructions clear, selected activities important and reflective of their children's abilities, and recordings at-home preferrable to in-clinic assessments. The three grading experts found the videos easy to grade and the milestones clinically meaningful. CONCLUSION: Our standardized strategy enables expert panel grading of developmental milestone achievements using at-home recordings, blinded to treatment and post-baseline time points. This rigorous and objective scoring system has broad applicability in various disease contexts, with or without external controls. Moreover, our strategy facilitates flexible, continued data collection and the videos can be archived for future analyses.

CYP2D6 Substrate Dispensing Among Patients Dispensed Mirabegron: An Administrative Claims Analysis.

BACKGROUND: Overactive bladder (OAB) is characterized by the presence of bothersome urinary symptoms. Pharmacologic treatment options for OAB include anticholinergics and ß3-adrenergic agonists. Use of ß3-adrenergic agonists may result in similar treatment efficacy with a decreased side effect profile compared with anticholinergics because high anticholinergic burden is associated with cardiovascular and neurologic side effects. However, the ß3-adrenergic agonist mirabegron, one of two approved drugs within this class, is a moderate cytochrome P450 (CYP) 2D6 inhibitor, and coadministration of drugs that are CYP2D6 substrates with mirabegron may lead to adverse drug effects. OBJECTIVE: The aim of this study was to quantify how often CYP2D6 substrates were dispensed in patients receiving mirabegron among adults of any age and among those ≥ 65 years of age. METHODS: In this retrospective descriptive analysis, a deidentified administrative claims database in the United States, IQVIA PharMetrics® Plus, was used to identify dispensing claims for CYP2D6 substrates and mirabegron from November 2012 to September 2019. Prevalence of CYP2D6 substrate dispensing was assessed in patients dispensed mirabegron among all adults ≥ 18 years old and additionally among a cohort of those ≥ 65 years old. Patient baseline profiles at the time of mirabegron and CYP2D6 substrate codispensing and at the time of mirabegron dispensing were compared. CYP2D6 substrates were categorized as those with the potential for increased risk of QT prolongation, with anticholinergic properties, with narrow therapeutic index (NTI), contraindicated or having a black box warning when used with CYP2D6 inhibitors, or used for depression or other psychiatric disease. Dispensing data and patient profiles were summarized descriptively. RESULTS: Overall, 68.5% of adults ≥ 18 years old dispensed mirabegron had overlapping dispensings for one or more CYP2D6 substrate; 60.6% and 53.6% had overlapping dispensings for CYP2D6 substrates with anticholinergic properties or risk of QT prolongation, respectively. CYP2D6 substrates with NTI, contraindicated with CYP2D6 inhibitors, or for psychiatric use were codispensed in 17.7%, 16.6%, and 38.0% of adult mirabegron users, respectively. Mirabegron users receiving one or more concurrent CYP2D6 substrate were more likely to be older, have more comorbidities and baseline polypharmacy, and have increased healthcare resource utilization compared with those without concurrent CYP2D6 substrates. Commonly codispensed CYP2D6 substrates included hydrocodone, oxycodone, tramadol, metoprolol, and tamsulosin. Findings were similar for patients in the older cohort (≥ 65 years old), with 72.1% receiving overlapping CYP2D6 substrates. CONCLUSIONS: Codispensing of CYP2D6 substrates, especially those with anticholinergic properties or risk of QT prolongation, was common among adults and older adults receiving mirabegron. Results highlight the need for improved awareness of CYP2D6 substrate prescribing among patients receiving pharmacologic treatment for OAB that inhibits the CYP2D6 pathway.

Assessment of Codispensing Patterns of Mirabegron and Prespecified CYP2D6 Substrates in Patients with Overactive Bladder.

BACKGROUND: Patients with overactive bladder (OAB) experience sudden, intense urges to urinate, which may include urge urinary incontinence and nocturia. Pharmacotherapy includes ß3-adrenergic receptor agonists such as mirabegron; however, mirabegron contains a label warning for cytochrome P450 (CYP) 2D6 inhibition, making coadministration with CYP2D6 substrates require monitoring and dose adjustment to avoid unintended increases in substrate concentration. OBJECTIVE: To understand the codispensing patterns of mirabegron among patients using ten predefined CYP2D6 substrates with and before mirabegron dispensing. METHODS: This retrospective claims database analysis used the IQVIA PharMetrics® Plus Database to assess codispensing of mirabegron with ten predefined CYP2D6 substrate groups identified on the basis of medications most frequently prescribed in the United States, those with high susceptibility to CYP2D6 inhibition, and those with evidence for exposure-related toxicity. Patients had to be ≥ 18 years old before initiation of the CYP2D6 substrate episode that overlapped with mirabegron. The cohort entry period was November 2012 to September 2019, and the overall study period was 1 January 2011 to 30 September 2019. Comparisons of patient profiles at dispensing were made between time periods with and before mirabegron use in the same patient. Descriptive statistics were used to assess the number of exposure episodes, total duration of exposure, and median duration of exposure of CYP2D6 substrate dispensing with and before mirabegron. RESULTS: CYP2D6 substrate exposure periods totaling ≥ 9000 person-months were available before overlapping exposure to mirabegron for all ten CYP2D6 substrate cohorts. Median codispensing duration for chronically administered CYP2D6 substrates was 62 (interquartile range [IQR] 91) days for citalopram/escitalopram, 71 (105) days for duloxetine/venlafaxine, and 75 (115) days for metoprolol/carvedilol; median codispensing duration for acutely administered CYP2D6 substrates was 15 (33) days for tramadol and 9 (18) days for hydrocodone. CONCLUSIONS: In this claims database analysis, the dispensing patterns of CYP2D6 substrates with mirabegron displayed frequent overlapping of exposure. Thus, a need exists to better understand the outcomes experienced by patients with OAB who are at increased risk for drug‒drug interactions when taking multiple CYP2D6 substrates concurrently with a CYP2D6 inhibitor.

Integrating genetic association, genetics of gene expression, and single nucleotide polymorphism set analysis to identify susceptibility Loci for type 2 diabetes mellitus.

Large-scale genome-wide association studies (GWAS) have identified over 40 genomic regions significantly associated with type 2 diabetes mellitus. However, GWAS results are not always straightforward to interpret, and linking these loci to meaningful disease etiology is often difficult without extensive follow-up studies. The authors expanded on previously reported type 2 diabetes mellitus GWAS from the nested case-control studies of 2 prospective US cohorts by incorporating expression single nucleotide polymorphism (SNP) information and applying SNP set enrichment analysis to identify sets of SNPs associated with genes that could provide further biologic insight to traditional genome-wide analysis. Using data collected between 1989 and 1994 in these previous studies to form a nested case-control study, the authors found that 3 of the most significantly associated SNPs to type 2 diabetes mellitus in their study are expression SNPs to the lymphocyte antigen 75 gene (LY75), the ubiquitin-specific peptidase 36 gene (USP36), and the phosphatidylinositol transfer protein, cytoplasmic 1 gene (PITPNC1). SNP set enrichment analysis of the GWAS results identified enrichment for expression SNPs to the macrophage-enriched module and the Gene Ontology (GO) biologic process fat cell differentiation human, which includes the transcription factor 7-like 2 gene (TCF7L2), as well as other type 2 diabetes mellitus-associated genes. Integrating genome-wide association, gene expression, and gene set analysis may provide valuable biologic support for potential type 2 diabetes mellitus susceptibility loci and may be useful in identifying new targets or pathways of interest for the treatment and prevention of type 2 diabetes mellitus.

Genetic variants in ABO blood group region, plasma soluble E-selectin levels and risk of type 2 diabetes.

Blood soluble E-selectin (sE-selectin) levels have been related to various conditions such as type 2 diabetes. We performed a genome-wide association study among women of European ancestry from the Nurses' Health Study, and identified genome-wide significant associations between a cluster of markers at the ABO locus (9q34) and plasma sE-selectin concentration. The strongest association was with rs651007, which explained approximately 9.71% of the variation in sE-selectin concentrations. SNP rs651007 was also nominally associated with soluble intracellular cell adhesion molecule-1 (sICAM-1) (P = 0.026) and TNF-R2 levels (P = 0.018), independent of sE-selectin. In addition, the genetic-inferred ABO blood group genotypes were associated with sE-selectin concentrations (P = 3.55 x 10(-47)). Moreover, we found that the genetic-inferred blood group B was associated with a decreased risk (OR = 0.44, 0.27-0.70) of type 2 diabetes compared with blood group O, adjusting for sE-selectin, sICAM-1, TNF-R2 and other covariates. Our findings indicate that the genetic variants at ABO locus affect plasma sE-selectin levels and diabetes risk. The genetic associations with diabetes risk were independent of sE-selectin levels.

Genome-wide association study identifies polymorphisms in LEPR as determinants of plasma soluble leptin receptor levels.

Plasma soluble leptin receptor (sOB-R) levels were inversely associated with diabetes risk factors, including adiposity and insulin resistance, and highly correlated with the expression levels of leptin receptor, which is ubiquitously expressed in most tissues. We conducted a genome-wide association study of sOB-R in 1504 women of European ancestry from the Nurses' Health Study. The initial scan yielded 26 single nucleotide polymorphisms (SNPs) significantly associated with sOB-R levels (P < 5 x 10(-8)); all mapping to the leptin receptor gene (LEPR). Analysis of imputed genotypes on autosomal chromosomes revealed an additional 106 SNPs in and adjacent to this gene that reached genome-wide significance level. Of these 132 SNPs (including two non-synonymous SNPs, rs1137100 and rs1137101), rs2767485, rs1751492 and rs4655555 remained associated with sOB-R levels at the 0.05 level (P = 9.1 x 10(-9), 0.0105 and 0.0267, respectively) after adjustment for other univariately associated SNPs in a forward selection procedure. Significant associations with these SNPs were replicated in an independent sample of young males (n = 875) residing in Cyprus (P < 1 x 10(-4)). These data provide novel evidence revealing the role of polymorphisms in LEPR in modulating plasma levels of sOB-R and may further our understanding of the complex relationships among leptin, leptin receptor and diabetes-related traits.

Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes.

To identify type 2 diabetes (T2D) susceptibility loci, we conducted genome-wide association (GWA) scans in nested case-control samples from two prospective cohort studies, including 2591 patients and 3052 controls of European ancestry. Validation was performed in 11 independent GWA studies of 10,870 cases and 73,735 controls. We identified significantly associated variants near RBMS1 and ITGB6 genes at 2q24, best-represented by SNP rs7593730 (combined OR=0.90, 95% CI=0.86-0.93; P=3.7x10(-8)). The frequency of the risk-lowering allele T is 0.23. Variants in this region were nominally related to lower fasting glucose and HOMA-IR in the MAGIC consortium (P<0.05). These data suggest that the 2q24 locus may influence the T2D risk by affecting glucose metabolism and insulin resistance.

Distribution and associations of [-2]proenzyme-prostate specific antigen in community dwelling black and white men.

PURPOSE: We provide cross-sectional normative data on [-2]proenzyme-prostate specific antigen from the Olmsted County Study of Urinary Symptoms and Health Status among Men, and the Flint Men's Health Study. We also describe associations with clinical urological measures and the risk of prostate cancer diagnosis. MATERIALS AND METHODS: Measurements of [-2]proenzyme-prostate specific antigen were obtained from 420 white men from Olmsted County, Minnesota, and 328 black men from Genesee County, Michigan. Cross-sectional associations between [-2]proenzyme-prostate specific antigen and prostate enlargement/elevated prostate specific antigen were assessed. Cox proportional hazard models were used to assess associations between [-2]proenzyme-prostate specific antigen and the incident diagnosis of prostate cancer. RESULTS: Baseline [-2]proenzyme-prostate specific antigen was slightly higher in black men at a median of 6.3 pg/ml (25th, 75th percentiles 4.1, 8.9) than in white men at a median of 5.6 pg/ml (25th, 75th percentiles 3.9, 7.7, respectively, p = 0.01). Baseline [-2]proenzyme-prostate specific antigen was highly predictive of biopsy confirmed prostate cancer in the Olmsted County Study cohort. Relative to men in the [-2]proenzyme-prostate specific antigen lower quartile those in the upper quartile were at almost eightfold increased risk for prostate cancer (HR 7.8, 95% CI 2.2-27.8) after adjusting for age and baseline prostate specific antigen. CONCLUSIONS: In these cohorts of community dwelling black and white men [-2]proenzyme-prostate specific antigen was much lower than in previous studies. These data suggest that [-2]proenzyme-prostate specific antigen may help identify prostate cancer in men with serum prostate specific antigen in an indeterminate range, although the reference ranges for white and black men may differ slightly.