Donna P Ankerst

Technische Universität München, München, Bavaria, Germany

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Publications (116)748.91 Total impact

  • BMC Urology 12/2015; 15(1). DOI:10.1186/s12894-015-0095-5 · 1.41 Impact Factor
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    ABSTRACT: The androgen receptor has been implicated in the development and progression of bladder cancer (BCa), largely based on studies of animal models. We investigated whether finasteride was associated with a reduced incidence of BCa as observed by self-report in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial. Cox proportional hazard regression analysis was performed to determine the association of finasteride use with time to diagnosis of BCa, controlling for age and tobacco use. Of the 72 370 male participants who met inclusion criteria, 6069 (8.4%) had reported the use of finasteride. BCa was diagnosed in 1.07% (65 of 6069) of those who reported finasteride compared with 1.46% (966 of 66 301) of those who reported no use during the trial. In a multiple Cox regression analysis, self-reported use of finasteride was associated with a decreased risk of development of BCa (hazard ratio: 0.634; 95% confidence interval, 0.493-0.816; p=0.0004), controlling for age and smoking. Limitations of this study include that it is observational and not randomized, that many of the confounding variables for BCa, such as alcohol use, were not available for use in the analysis, and that finasteride use was by annual self-report, which is subject to missing values and error. Finasteride is a common medication used to reduce the size of the prostate and to promote hair growth by manipulating testosterone in men. Men are more likely than women to develop bladder cancer (BCa), but our study noted that men using finasteride were less likely to have a BCa diagnosis. Copyright © 2015. Published by Elsevier B.V.
    European Urology 08/2015; DOI:10.1016/j.eururo.2015.08.029 · 13.94 Impact Factor
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    ABSTRACT: One in five prostate cancer patients have a positive family history. This report evaluated the association between family history and long-term outcomes following radical prostatectomy. Patients treated by radical prostatectomy were identified from a German registry, and separated into two groups: positive first-degree family history versus negative (strictly negative; requiring at least one male first-degree relative > 60 years and no prostate cancer in the family). Kaplan-Meier-curves and Cox-proportional hazards models were used for association analyses with biochemical recurrence-free and prostate cancer-specific survival. Median follow-up among 7,690 men included in the study was 8.4 years. 50.9% of the 754 younger patients < 55 years (n = 384) had a family history, compared to 40.4% of the older patients (n = 2,803, p <0.001). 10-year biochemical recurrence-free (62.5%) and prostate cancer-specific survival (96.1%) did not differ between patients with and without family history nor among the younger and older patient groups (all p > 0.05). Prostate-specific antigen, pathologic stage, node stage and Gleason score were the only significant predictors for biochemical recurrence-free survival, while pathologic stage, node stage (all p <0.005) and Gleason score (Gleason 7 versus ≤ 6: hazard ratio [HR] = 1.711; 95% confidence interval [CI] = 1.056 to 2.774; p = 0.03; Gleason ≥ 8 versus ≤ 6: HR = 4.516; 95% CI = 2.776 to 7.347; p < 0.0001) were the only predictors for prostate cancer-specific survival. Family history of prostate cancer has no bearing on long-term outcomes following radical prostatectomy. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.
    The Journal of urology 07/2015; DOI:10.1016/j.juro.2015.07.097 · 4.47 Impact Factor
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    ABSTRACT: Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of Biomedical Informatics 05/2015; 56. DOI:10.1016/j.jbi.2015.05.001 · 2.19 Impact Factor
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    The Journal of Urology 04/2015; 193(4):e799. DOI:10.1016/j.juro.2015.02.2316 · 4.47 Impact Factor
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    ABSTRACT: We aimed to compare nonlinear modeling methods for handling continuous predictors for reproducibility and transportability of prediction models. We analyzed four cohorts of previously unscreened men who underwent prostate biopsy for diagnosing prostate cancer. Continuous predictors of prostate cancer included prostate-specific antigen and prostate volume. The logistic regression models included linear terms, logarithmic terms, fractional polynomials of degree one or two (FP1 and FP2), or restricted cubic splines (RCS) with three or five knots (RCS3 and RCS5). The resulting models were internally validated by bootstrap resampling and externally validated in the cohorts not used at model development. Performance was assessed with the area under the receiver operating characteristic curve (AUC) and the calibration component of the Brier score (CAL). At internal validation models with FP2 or RCS5 showed slightly better performance than the other models (typically 0.004 difference in AUC and 0.001 in CAL). At external validation models containing logarithms, FP1, or RCS3 showed better performance (differences 0.01 and 0.002). Flexible nonlinear modeling methods led to better model performance at internal validation. However, when application of the model is intended across a wide range of settings, less flexible functions may be more appropriate to maximize external validity. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of Clinical Epidemiology 04/2015; 68(4):426-34. DOI:10.1016/j.jclinepi.2014.11.022 · 3.42 Impact Factor
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    Michael Matiu · Donna P. Ankerst · Annette Menzel
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    ABSTRACT: While the rise in global mean temperature over the past several decades is now widely acknowledged, the issue as to whether and to what extent temperature variability is changing continues to undergo debate. Here, variability refers to the spread of the temperature distribution. Much attention has been given to the effects that changes in mean temperature have on extremes, but these changes are accompanied by changes in variability, and it is actually the two together, in addition to all aspects of a changing climate pattern, that influence extremes. Since extremes have some of the largest impacts on society and ecology, changing temperature variability must be considered in tandem with a gradually increasing temperature mean. Previous studies of trends in temperature variability have produced conflicting results. Here we investigated ten long-term instrumental records in Europe of minimum, mean and maximum temperatures, looking for trends in seasonal, annual and decadal measures of variability (standard deviation and various quantile ranges) as well as asymmetries in the trends of extreme versus mean temperatures via quantile regression. We found consistent and accelerating mean warming during 1864–2012. In the last 40 years (1973–2012) trends for Tmax were higher than for Tmin, reaching up to 0.8 °C per 10a in spring. On the other hand, variability trends were not as uniform: significant changes occurred in opposing directions depending on the season, as well as when comparing 1864–2012 trends to those of 1973–2012. Moreover, if variability changed, then it changed asymmetrically, that is only in the part above or below the median. Consequently, trends in the extreme high and low quantiles differed. Regional differences indicated that in winter, high-alpine stations had increasing variability trends for Tmax especially at the upper tail compared to no changes or decreasing variability at low altitude stations. In contrast, summer variability increased at all stations studied.
    International Journal of Climatology 04/2015; DOI:10.1002/joc.4326 · 3.16 Impact Factor
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    The Journal of Urology 04/2015; 193(4):e995-e996. DOI:10.1016/j.juro.2015.02.549 · 4.47 Impact Factor
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    Jessica Goetz · Sonja Grill · Donna Ankerst · Timothy Tseng
    The Journal of Urology 04/2015; 193(4):e949. DOI:10.1016/j.juro.2015.02.2703 · 4.47 Impact Factor
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    ABSTRACT: Men on active surveillance (AS) face repeated biopsies. Most biopsy specimens will not show disease progression or change management. Such biopsies do not contribute to patient management and are potentially morbid and costly. To use a contemporary AS prospective trial to develop a tool to predict AS biopsy outcomes. Biopsy samples (median: 2; range: 2-9 per patient) from 859 men participating in the Canary Prostate Active Surveillance Study and with Gleason 6 prostate cancer (median follow-up: 35.8 mo; range: 3.0-148.7 mo) were analyzed. Logistic regression was used to predict progression, defined as an increase in Gleason score from ≤6 to ≥7 or increase in percentage of cores positive for cancer from <34% to ≥34%. Fivefold internal cross-validation was performed to evaluate the area under the receiver operating characteristic curve (AUC). Statistically significant risk factors for progression on biopsy were prostate-specific antigen (odds ratio [OR]: 1.045; 95% confidence interval [CI], 1.028-1.063), percentage of cores positive for cancer on most recent biopsy (OR: 1.401; 95% CI, 1.301-1.508), and history of at least one prior negative biopsy (OR: 0.524; 95% CI, 0.417-0.659). A multivariable predictive model incorporating these factors plus age and number of months since last biopsy achieved an AUC of 72.4%. A combination of readily available clinical measures can stratify patients considering AS prostate biopsy. Risk of progression or upgrade can be estimated and incorporated into clinical practice. The Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator, an online tool, can be used to guide patient decision making regarding follow-up prostate biopsy. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.
    European Urology 03/2015; DOI:10.1016/j.eururo.2015.03.023 · 13.94 Impact Factor
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    ABSTRACT: Prostate-specific antigen (PSA) screening is controversial, as a large number of men must be screened annually to achieve a benefit. We sought to determine if baseline PSA could reliably predict subsequent risk of prostate cancer (PCA) and risk of consequential PCA. A multi-ethnic cohort of 2923 PCA-free men, were recruited between 2000 and 2012 and followed for a median of 7.5 years Baseline PSA was stratified into 6 strata and relative hazards of PCA detection for each PSA strata were estimated, adjusting for ethnicity, family history, and age. There were 289 cases of PCA diagnosed in patients during follow-up. Men with baseline PSA in the lowest stratum PSA [0.1 to 1.0 ng/mL] were at greatly reduced risk of PCA during follow-up. this half of the cohort with PSA ≤1.0 ng/mL had a 3.4% (95% CI [2.1, 4.5]). 10-year risk of PCA; 90% of the cancers were low-risk. By comparison, the other half had a 15 to 39% risk of cancer detection with a 39% risk in the highest stratum (3-10 ng/mL). Optimal PSA screening frequency for men with PSA levels of 0.1 - 1.0 ng/mL may be up to every 10 years. This approach has the potential to dramatically reduce the cost of screening, reducing overdetection of inconsequential tumors, while maintaining detection of tumors for which treatment has been proven to reduce PCA mortality. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.
    The Journal of Urology 02/2015; 194(1). DOI:10.1016/j.juro.2015.02.043 · 4.47 Impact Factor
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    ABSTRACT: To evaluate whether annual updating of the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) would improve institutional validation over static use of the PCPTRC alone. Data from five international cohorts, SABOR, Cleveland Clinic, ProtecT, Tyrol, and Durham VA, comprising n = 18,400 biopsies were used to evaluate an institution-specific annual recalibration of the PCPTRC. Using all prior years as a training set and the current year as the test set, the annual recalibrations of the PCPTRC were compared to static use of the PCPTRC in terms of areas underneath the receiver operating characteristic curves (AUC) and Hosmer-Lemeshow goodness-of-fit statistic (H-L). For predicting high-grade disease, the median AUC (higher is better) of the recalibrated PCPTRC (static PCPTRC) across all test years for the five cohorts were 67.3 (67.5), 65.0 (60.4), 73.4 (73.4), 73.9 (74.1), 69.6 (67.2), respectively, and the median H-L statistics indicated better fit for recalibration compared to the static PCPTRC for Cleveland Clinic, ProtecT and the Durham VA, but not for SABOR and Tyrol. For predicting overall cancer, median AUCs were 63.5 (62.7), 61.0 (57.3), 62.1 (62.5), 66.9 (67.3), and 68.5 (65.5), respectively, and the median H-L statistics indicated better fit for recalibration on all cohorts except for Tyrol. A simple to implement method has been provided to tailor the PCPTRC to individual hospitals in order to optimize its accuracy for the patient population at hand. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.
    The Journal of Urology 01/2015; 194(1). DOI:10.1016/j.juro.2015.01.092 · 4.47 Impact Factor
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    ABSTRACT: To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC). A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds. In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively). The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men. Copyright © 2015 Elsevier Inc. All rights reserved.
    Journal of clinical epidemiology 01/2015; DOI:10.1016/j.jclinepi.2015.01.006 · 3.42 Impact Factor
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    ABSTRACT: Process-orientated, unmanaged forest remnants are not sufficient for halting the loss of forest biodiversity. Thus, integrated biodiversity-promoting management for forest inhabitants is needed. Microhabitats, such as tree cavities or bark pockets, are essential for the preservation of saproxylic species and of critical importance for endangered ones. This study investigates (1) which factors trigger the formation of microhabitats at both the individual tree and aggregated plot level, and (2) whether the co-occurrence of microhabitats differs between managed (=logged) and unmanaged forests. Relationships between the occurrence of 17 microhabitat types and individual tree features (e.g. light availability, and tree vitality) and plot characteristics (e.g. stand density index and stand age) in 398 plots dominated by Fagus sylvatica or Pseudotsuga menziesii in Germany and the USA were studied using random-effects logistic and normal regression modelling. Separate analyses were performed for German beech forests, German Douglas-fir forests, and the US Douglas-fir forests. Our results show that (1) tree diameter in breast height (DBH), tree vitality and branchiness or epicormic branches are highly related with the occurrence of one or more microhabitats on individual trees in managed and unmanaged beech and US Douglas-fir forests. In managed German Douglas-fir forests, vitality is not a predictor for the occurrence of microhabitats on a tree, but tree density and the maximum age of trees in a stand in addition to DBH and branchiness have an effect. Time since last management is not a statistically significant predictor for the presence of microhabitats at the tree level, but it is for German beech at the plot level. In Douglas-fir-dominated forests both in Germany and in the USA, the stand density index was the only common predictor at the plot level. (2) Unmanaged German beech and Douglas-fir forests exhibit more statistically significant and positive correlations with microhabitat groups than managed stands, implying that the presence of one microhabitat group on a tree is associated with the presence of other microhabitat groups. We finally conclude that measures for supporting microhabitat inhabitants in managed forests are scale and species dependent (tree versus plot level; beech versus Douglas-fir-dominated forests). Trees that carry microhabitats seem to have similar features independently of forest management. At the plot level, density management may trigger the accumulation of microhabitats. Our results indicate that in forest management, it is possible to consider the factors influencing the formation of microhabitats and implement adequate forest practices to advance their formation.
    European Journal of Forest Research 12/2014; 134(2). DOI:10.1007/s10342-014-0855-x · 2.10 Impact Factor
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    ABSTRACT: Purpose: A detailed family history provides an inexpensive alternative to genetic profiling for individual risk assessment. We updated the PCPT Risk Calculator to include detailed family histories. Materials and methods: The study included 55,168 prostate cancer cases and 638,218 controls from the Swedish Family Cancer Database who were 55 years old or older in 1999 and had at least 1 male first-degree relative 40 years old or older and 1 female first-degree relative 30 years old or older. Likelihood ratios, calculated as the ratio of risk of observing a specific family history pattern in a prostate cancer case compared to a control, were used to update the PCPT Risk Calculator. Results: Having at least 1 relative with prostate cancer increased the risk of prostate cancer. The likelihood ratio was 1.63 for 1 first-degree relative 60 years old or older at diagnosis (10.1% of cancer cases vs 6.2% of controls), 2.47 if the relative was younger than 60 years (1.5% vs 0.6%), 3.46 for 2 or more relatives 60 years old or older (1.2% vs 0.3%) and 5.68 for 2 or more relatives younger than 60 years (0.05% vs 0.009%). Among men with no diagnosed first-degree relatives the likelihood ratio was 1.09 for 1 or more second-degree relatives diagnosed with prostate cancer (12.7% vs 11.7%). Additional first-degree relatives with breast cancer, or first-degree or second-degree relatives with prostate cancer compounded these risks. Conclusions: A detailed family history is an independent predictor of prostate cancer compared to commonly used risk factors. It should be incorporated into decision making for biopsy. Compared with other costly biomarkers it is inexpensive and universally available.
    The Journal of Urology 09/2014; DOI:10.1016/j.juro.2014.09.018 · 4.47 Impact Factor
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    ABSTRACT: Individual tree mortality prediction is a key component of single tree-based stand simulators. However, accurate modeling of long-term research plot data is hampered by rare events, variable lengths of observation, and multiple sources of heterogeneity. This study makes use of a result from medicine that demonstrates the equivalence of logistic and Cox proportional hazards regression for modeling survival data in the case of large sample sizes, rare events, and variable interval periods of observation. Pooled logistic regression models are used to model tree mortality across multiple observation periods with random effects to account for heterogeneity due to plots and calendar year. The models are applied to data from 21,051 observation periods (each approximately 5 years) from 9,292 beech trees in a Bavarian long-term forest research plot network. Among the observation periods studied, 604 (2.9%) resulted in a mortality. Indices measuring competition from light, trees of the same species, conifer trees, and shading are significantly associated with mortality, whereas other variables, including dbh, fail to add additional predictive value. Analytic equations for predicting mortality in new trees are provided and yield an area underneath the receiver operating characteristic curve of 91.5%.
    Forest Science 08/2014; 60(4):613-623. DOI:10.5849/forsci.12-133 · 1.57 Impact Factor
  • Ian M Thompson · Robin J Leach · Donna P Ankerst
    JAMA The Journal of the American Medical Association 08/2014; 312(10). DOI:10.1001/jama.2014.9680 · 35.29 Impact Factor
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    ABSTRACT: Purpose: To evaluate progression-free survival (PFS), overall response rate (ORR) and disease control rate (DCR) as potential surrogate endpoints (SEP) for overall survival (OS) in second-line treatment for metastatic colorectal cancer (mCRC). Methods: A systematic literature search of randomised trials of second-line chemotherapy for mCRC reported from January 2000 to July 2013 was performed. Correlation coefficients weighted by number of patients in the treatment arms between median PFS, ORR and DCR with median OS were estimated. Results: Twenty-three trials reflecting 10 800 patients met the inclusion criteria. Median PFS and OS across all trials were 4.5 months and 11.5 months and median ORR and DCR were 11.4% and 65%, respectively. PFS showed moderate correlation with OS [RPFS = 0.73; 95% confidence interval (CI) 0.61-0.82]. In contrast, ORR only weakly correlated with OS (RORR = 0.58; 95% CI 0.38-0.72, n = 22). Despite a small number of studies (n = 10) reporting on DCR, moderate correlation with OS was observed (RDCR = 0.74; 95% CI 0.56-0.86). Conclusion: Based on the available trial-level data, PFS may serve as an appropriate SEP in second-line chemotherapy for mCRC. A small number of studies revealed moderate correlation of DCR with OS that justifies further investigation.
    Acta oncologica (Stockholm, Sweden) 07/2014; 54(2):1-7. DOI:10.3109/0284186X.2014.938830 · 3.00 Impact Factor
  • Donna P. Ankerst · Ian M. Thompson
    Urology 06/2014; 83(6):1368-1368. DOI:10.1016/j.urology.2014.02.037 · 2.19 Impact Factor
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    ABSTRACT: Objective: To modify the Prostate Cancer Prevention Trial risk calculator (PCPTRC) to predict low- vs high-grade (Gleason grade≥7) prostate cancer and incorporate percent free-prostate-specific antigen (PSA). Methods: Data from 6664 Prostate Cancer Prevention Trial placebo arm biopsies (5826 individuals), where prostate-specific antigen and digital rectal examination results were available within 1 year before the biopsy and PSA was ≤10 ng/mL, were used to develop a nominal logistic regression model to predict the risk of no vs low-grade (Gleason grade<7) vs high-grade cancer (Gleason grade≥7). Percent free-PSA was incorporated into the model based on likelihood ratio analysis of a San Antonio Biomarkers of Risk cohort. Models were externally validated on 10 Prostate Biopsy Collaborative Group cohorts and 1 Early Detection Research Network reference set. Results: Of all the Prostate Cancer Prevention Trial biopsies, 5468 (82.1%) were negative for prostate cancer, 942 (14.1%) detected low-grade, and 254 (3.8%) detected high-grade disease. Significant predictors were (log base 2) PSA (odds ratio for low-grade vs no cancer, 1.29*; high-grade vs no cancer, 2.02*; high-grade vs low-grade cancer, 1.57*), digital rectal examination (0.96, 1.49*, 1.55*, respectively), age (1.02*, 1.05*, 1.03*, respectively), African American race (1.13, 2.83*, 2.51*, respectively), prior biopsy (0.63*, 0.81, 1.27, respectively), and family history (1.31*, 1.25, 0.95, respectively), where * indicates P value<.05. The new PCPTRC 2.0 either with or without percent free-PSA (also significant by the likelihood ratio method) validated well externally. Conclusion: By differentiating the risk of low- vs high-grade disease on biopsy, PCPTRC 2.0 better enables physician-patient counseling concerning whether to proceed to biopsy.
    Urology 06/2014; 83(6):1362-8. DOI:10.1016/j.urology.2014.02.035 · 2.19 Impact Factor

Publication Stats

3k Citations
748.91 Total Impact Points


  • 2007–2015
    • Technische Universität München
      • • Department of Mathematical Statistics
      • • Chair of Ecoclimatology
      München, Bavaria, Germany
    • Texas A&M University - Galveston
      Galveston, Texas, United States
    • University of Utah
      • Department of Anesthesiology
      Salt Lake City, Utah, United States
  • 2006–2015
    • University of Texas Health Science Center at San Antonio
      • • Department of Epidemiology and Biostatistics
      • • Department of Urology
      San Antonio, Texas, United States
    • University of Pittsburgh
      Pittsburgh, Pennsylvania, United States
  • 2008–2009
    • Ludwig-Maximilians-University of Munich
      • Institute for Medical Informatics, Biometry and Epidemiology
      München, Bavaria, Germany
  • 2006–2007
    • University of Texas at San Antonio
      San Antonio, Texas, United States
  • 2004–2007
    • Fred Hutchinson Cancer Research Center
      • Division of Public Health Sciences
      Seattle, Washington, United States