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Austin

Austin is the vibrant capital city of Texas, known for its thriving arts, music, and technology scenes.
Situated on the Colorado River, this dynamic metropolis offers a unique blend of Southern charm and progressive culture.
With a population of over 950,000, Austin boasts a diverse range of attractions, from the iconic live music venues of 6th Street to the serene hiking trails of the Barton Creek Greenbelt.
The city is home to the University of Texas at Austin, a renowned research institution that contributes to the city's vibrant intellectual atmosphere.
Austin is also a hub for startups and innovative companies, making it a desireable destination for those in the tech industry.
Wether you're exploring the city's fámous food trucks, attending one of its lively festivals, or immersing yourself in the great outdoors, Austin provides an unparalleled experience for visitors and residents alike.

Most cited protocols related to «Austin»

1
Propensity Score Methods for Causal Inference
The propensity score was defined by Rosenbaum and Rubin (1983a) to be the probability of treatment assignment conditional on observed baseline covariates: ei = Pr(Zi = 1|Xi). The propensity score is a balancing score: conditional on the propensity score, the distribution of measured baseline covariates is similar between treated and untreated subjects. Thus, in a set of subjects all of whom have the same propensity score, the distribution of observed baseline covariates will be the same between the treated and untreated subjects.
The propensity score exists in both randomized experiments and in observational studies. In randomized experiments the true propensity score is known and is defined by the study design. In observational studies, the true propensity score is not, in general, known. However, it can be estimated using the study data. In practice, the propensity score is most often estimated using a logistic regression model, in which treatment status is regressed on observed baseline characteristics. The estimated propensity score is the predicted probability of treatment derived from the fitted regression model. Although logistic regression appears to be the most commonly used method for estimating the propensity score, the use of bagging or boosting (Lee, Lessler, & Stuart, 2010; (link) McCaffrey, Ridgeway, & Morral, 2004 (link)), recursive partitioning or tree-based methods (Lee et al., 2010 (link); Setoguchi, Schneeweiss, Brookhart, Glynn, & Cook, 2008 (link)), random forests (Lee et al., 2010 (link)), and neural networks (Setoguchi et al., 2008 (link)) for estimating the propensity score have been examined.
Four different propensity score methods are used for removing the effects of confounding when estimating the effects of treatment on outcomes: propensity score matching, stratification (or subclassification) on the propensity score, inverse probability of treatment weighting (IPTW) using the propensity score, and covariate adjustment using the propensity score (Austin & Mamdani, 2006 (link); Rosenbaum, 1987a ; Rosenbaum & Rubin, 1983a ). I describe each of these methods separately in the following subsections.
Rosenbaum and Rubin (1983a) defined treatment assignment to be strongly ignorable if the following two conditions hold: (a) (Y (1), Y(0)) ╩ Z|X and (b) 0 < P(Z = 1|X) < 1. The first condition says that treatment assignment is independent of the potential outcomes conditional on the observed baseline covariates. The second condition says that every subject has a nonzero probability to receive either treatment. They demonstrated that if treatment assignment is strongly ignorable, conditioning on the propensity score allows one to obtain unbiased estimates of average treatment effects. The aforementioned first condition is also referred to as the “no unmeasured confounders” assumption: the assumption that all variables that affect treatment assignment and outcome have been measured. Because this is the crucial assumption that underlies propensity score analyses, Rosenbaum and Rubin (1983b) proposed analyses to assess the sensitivity of study conclusions to the assumption that there were no unmeasured confounders that influenced treatment assignment. Furthermore, Rosenbaum (1987b) proposed the use of a second control group to examine the plausibility that adjustment for measured covariates has eliminated bias in estimating treatment effects. It should be noted that although the assumption of strongly ignorable treatment assignment/no unmeasured confounding is explicitly stated in the context of propensity score analyses, this assumption also underlies regression-based approaches for estimating treatment effects in observational studies.
, & Austin P.C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46(3), 399-424.
Publication 2011
austin Hypersensitivity Trees
2
Propensity Score Matching for Statin Exposure
The propensity score was estimated using logistic regression to regress receipt of a statin prescription at discharge on the 24 baseline covariates described in Table I. The estimated propensity score was the predicted probability of statin exposure derived from the fitted logistic regression model. In the propensity-score model we assumed a linear relationship between continuous covariates and the log-odds of receiving a statin prescription. Furthermore, the propensity-score model did not include any interactions.
We created a matched sample by matching treated and untreated subjects on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score [3 (link), 17 (link), 18 (link)]. A greedy, nearest-neighbour matching algorithm was employed to form pairs of treated and untreated subjects.
, & Austin P.C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083-3107.
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Publication 2009
Hydroxymethylglutaryl-CoA Reductase Inhibitors Patient Discharge
3
Cerebrospinal Fluid Biomarkers in Alzheimer's Disease
The ADNI is a large, multicenter, longitudinal neuroimaging study, launched in 2004 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies, and nonprofit organizations. ADNI includes 819 adult subjects, 55 to 90 years old, who meet entry criteria for a clinical diagnosis of amnestic MCI (n = 397), probable AD (n = 193), or normal cognition (n = 229). Participants receive baseline and periodic physical and neurological examinations and standardized neuropsychological assessments, and provide biological samples (blood, urine, and in a subset, CSF) throughout the study. Imaging (magnetic resonance imaging and for a subset, F-fluorodeoxyglucose positron emission tomography and Pittsburgh compound B positron emission tomography) is performed at baseline and at regular intervals thereafter (for reviews and more details, see Shaw and colleagues,7 Mueller and coauthors,11 (link) and http://www.adni-info.org/). All AD subjects met National Institute of Neurological and Communication Disorders/Alzheimer’s Disease and Related Disorders Association criteria for probable AD with a Mini-Mental State Examination score between 20 and 26, a global Clinical Dementia Rating of 0.5 or 1, a sum-of-boxes Clinical Dementia Rating of 1.0 to 9.0, and, therefore, are only mildly impaired. Entry criteria for amnestic MCI subjects include a Mini-Mental State Examination score of 24 to 30 and a Memory Box score of at least 0.5, whereas other details on the ADNI cohort can be found online at: http://www.nia.nih.gov/Alzheimers/ResearchInformation/ClinicalTrials/ADNI.htm. In brief, exclusion criteria included any serious neurological disease other than possible AD, any history of brain lesions or head trauma, or psychoactive medication use (including antidepressants, neuroleptics, chronic anxiolytics, or sedative hypnotics).
Baseline CSF samples were obtained in the morning after an overnight fast from 416 ADNI subjects (AD = 102, MCI = 200, NC = 114 with average [± standard deviation] ages of 75 ± 8, 75 ± 7, and 76 ± 5 years, respectively; Table 1) from individuals enrolled at 56 participating centers at the time the subjects entered ADNI (ie, baseline). Their demographic, clinical, and APOε genotyping results are comparable with that in the full ADNI patient population (http://www.adni-info.org/index). Lumbar puncture was performed with a 20- or 24-gauge spinal needle as described in the ADNI procedures manual (http://www.adni-info.org/). In brief, CSF was collected into collection tubes provided to each site, then transferred into polypropylene transfer tubes followed by freezing on dry ice within 1 hour after collection, and shipped overnight to the ADNI Biomarker Core laboratory at the University of Pennsylvania Medical Center on dry ice. Aliquots (0.5ml) were prepared from these samples after thawing (1 hour) at room temperature and gentle mixing. The aliquots were stored in bar code–labeled polypropylene vials at −80°C. Written informed consent was obtained for participation in these studies, as approved by the institutional review board at each participating center.
An independent set of premortem CSF samples from 56 autopsy-confirmed AD cases and 52 cognitively normal elderly subjects followed by the University of Pennsylvania Alzheimer’s Disease Clinical Core provided an independent analysis sample set that was matched with the ADNI samples with respect to age (mean ± standard deviation [95% confidence interval]: 71 ± 10 [69–74] and 70 ± 11 [67–73] years, respectively) at the time of their lumbar puncture. The cases and control subjects were evaluated and followed as described previously,12 (link)–14 and all of these CSF samples were collected at University of Pennsylvania Alzheimer’s Disease Clinical Core using standardized methodology including storage of aliquots in polypropylene vials maintained in the repository at −80°C.12 (link)–14 Written informed consent was obtained for participation in these studies, which was approved by the University of Pennsylvania Institutional Review Board.
Aβ1-42, t-tau, and p-tau181p were measured in each of the 416 CSF ADNI baseline aliquots using the multiplex xMAP Luminex platform (Luminex Corp, Austin, TX) with Innogenetics (INNO-BIA AlzBio3; Ghent, Belgium; for research use–only reagents) immunoassay kit–based reagents. Full details of this combination of immunoassay reagents and analytical platform are provided elsewhere.15 (link),16 In brief, Innogenetics kit reagents included well-characterized capture monoclonal antibodies specific for Aβ1-42(4D7A3), t-tau(AT120), and p-tau181p (AT270), each chemically bonded to unique sets of color-coded beads, and analyte-specific detector antibodies (HT7, 3D6). Calibration curves were produced for each biomarker using aqueous buffered solutions that contained the combination of three biomarkers at concentrations ranging from 56 to 1,948pg/ml for recombinant tau, 27 to 1,574pg/ml for synthetic Aβ1-42 peptide, and 8 to 230pg/ml for a tau synthetic peptide phosphorylated at the threonine 181 position (ie, the p-tau181p standard). Before performing these analyses of the ADNI and the independent autopsy-based CSF samples in the ADNI University of Pennsylvania ADNI Biomarker Core laboratory, an interlaboratory study was conducted to qualify the performance conditions, including all major variables that can affect the test results, for the immunoassay reagents and analytical platform. These studies were conducted using strategies and procedures to standardize the assay similar to those that Vanderstichele and colleagues16 described. This investigation (Shaw and colleagues, manuscript in preparation, but see summary of these data online at: http://www.adni-info.org/) provided the basis for achieving day-to-day reproducibility for the three biomarkers of less than 10% variation for CSF pool samples and less than 7% for aqueous quality controls. The ADNI baseline CSF samples were analyzed over a 14-day period and included test–retest analyses of 29 of the samples that further substantiated the analytical performance (r2 values for comparison of initial test result with retest result of 0.98, 0.90, and 0.85 for t-tau, Aβ1-42, and p-tau181p, respectively for 29 randomly selected samples). Only subjects with a valid test result for all 3 biomarkers are included in this study, that is, 114 NC, 196 MCI, and 100 AD subjects.
APOε genotyping was done for all ADNI study candidates using EDTA blood samples collected at the screening visit (see Table 1). TaqMan quantitative polymerase chain reaction assays were used for genotyping APOε nucleotides 334 T/C and 472 CT with an ABI 7900 real-time thermo-cycler (Applied Biosystems, Foster City, CA) using DNA freshly prepared from EDTA whole blood. A total of 96 samples randomly selected from the total of 1,131 subjects screened before inclusion (or exclusion) into the ADNI study were retested by sequencing using an ABI 3130 sequencer (Applied Biosystems). Except for the 5 samples that failed to sequence, the remaining 91 were concordant with the Taq-Man genotyping results.
Receiver operating characteristic curve (ROC) and logistic regression (LR) analyses were done using SAS v 9.1.3 (SAS Institute, Cary, NC) and R v 2.7.1. Between-group differences for each biomarker were assessed by the Mann–Whitney U test using GraphPad Prism, v 5.
Shaw L.M., Vanderstichele H., Knapik-Czajka M., Clark C.M., Aisen P.S., Petersen R.C., Blennow K., Soares H., Simon A., Lewczuk P., Dean R., Siemers E., Potter W., Lee V.M, & Trojanowski J.Q. (2009). Cerebrospinal Fluid Biomarker Signature in Alzheimer’s Disease Neuroimaging Initiative Subjects. Annals of neurology, 65(4), 403-413.
Publication 2009
4
Comparing Propensity Score Estimation Methods
Several studies have demonstrated that propensity score matching eliminates a greater proportion of the systematic differences in baseline characteristics between treated and untreated subjects than does stratification on the propensity score or covariate adjustment using the propensity score (Austin, 2009a ; Austin, Grootendorst, & Anderson, 2007; (link) Austin & Mamdani, 2006 (link)). In some settings propensity score matching and IPTW removed systematic differences between treated and untreated subjects to a comparable degree; however, in some settings, propensity score matching removed modestly more imbalance than did IPTW (Austin, 2009a ). Lunceford and Davidian (2004) (link) demonstrated that stratification results in estimates of average treatment effects with greater bias than does a variety of weighted estimators.
Propensity score matching, stratification on the propensity score, and IPTW differ from covariate adjustment using the propensity score in that the three former methods separate the design of the study from the analysis of the study; this separation does not occur when covariate adjustment using the propensity score is used. Appropriate diagnostics exist for each of the four propensity score methods to assess whether the propensity score model has been adequately specified. However, with propensity score matching, stratification on the propensity score, and IPTW, once one is satisfied with the specification of the propensity score model, one can directly estimate the effect of treatment on outcomes in the matched, stratified, or weighted sample. Specification of a regression model relating the outcome to treatment is not necessary. In contrast, when using covariate adjustment using the propensity score, once one is satisfied that the propensity score model has been adequately specified, one must fit a regression model relating the outcome to an indicator variable denoting treatment status and to the propensity score. In specifying the regression model, one must correctly model the relationship between the propensity score and the outcome (e.g., specifying whether the relationship is linear or nonlinear). In doing so, the outcome is always in sight because the outcome model contains both the propensity score and the outcome. As Rubin (2001) notes, when using regression modeling, the temptation to work toward the desired or anticipated result is always present. Another difference between the four propensity score approaches is that covariate adjustment using the propensity score and IPTW may be more sensitive to whether the propensity score has been accurately estimated (Rubin, 2004 ).
The reader is referred elsewhere to empirical studies comparing the results of analyses using the different propensity score methods on the same data set (Austin & Mamdani, 2006 (link); Kurth et al., 2006 (link)). Prior Monte Carlo studies have compared the relative performance of the different propensity score methods for estimating risk differences, relative risks, and marginal and conditional odds ratios (Austin, 2007b (link), 2008c (link), 2010 (link); Austin, Grootendorst, Normand, & Anderson, 2007 (link)). It is important to note that two of these studies found that stratification, matching, and covariate adjustment using the propensity score resulted in biased estimation of both conditional and marginal odds ratios.
, & Austin P.C. (2011). An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behavioral Research, 46(3), 399-424.
Publication 2011
austin Diagnosis Vision
5
Estimating Incidence with Competing Risks
We assume that there is a well-defined baseline time in the cohort and that T denotes the time from baseline time until the occurrence of the event of interest. In the absence of competing risks, the survival function, S(t), describes the distribution of event times: S(t) = Pr(Tt). One minus the survival function (ie, the complement of the survival function), F(t) = 1 − S(t) = Pr(T ≤ t) describes the incidence of the event over the duration of follow-up. Two key properties of the survival function are that S(0) = 1 (ie, at the beginning of the study, the event has not yet occurred for any subjects) and (ie, eventually the event of interest occurs for all subjects). In practice, the latter assumption may not be required, because the probability of the event over a restricted follow-up period may be <1.
Estimating the incidence of an event as a function of follow-up time provides important information on the absolute risk of an event. In the absence of competing risks, the Kaplan-Meier estimate of the survival function is frequently used for estimating the survival function. One minus the Kaplan-Meier estimate of the survival function provides an estimate of the cumulative incidence of events over time. In the case study that follows, we examine the incidence of cardiovascular death in patients hospitalized with heart failure. When the complement of the Kaplan-Meier function was used, the estimated incidence of cardiovascular death within 5 years of hospital admission was 43.0%. However, using the Kaplan-Meier estimate of the survival function to estimate the incidence function in the presence of competing risks generally results in upward biases in the estimation of the incidence function.9 (link),10 (link),12 (link) In particular, the sum of the Kaplan-Meier estimates of the incidence of each individual outcome will exceed the Kaplan-Meier estimate of the incidence of the composite outcome defined as any of the event types. Even when the competing events are independent, the Kaplan-Meier estimator yields biases in the probability of the event of interest. The problem here is that the Kaplan-Meier estimator estimates the probability of the event of interest in the absence of competing risks, which is generally larger than that in the presence of competing risks. Furthermore, the hypothetical population in which competing risks do not exist may not be the population of greatest interest for clinical and/or policy making,13 (link) as in the cardiovascular setting where noncardiovascular death may be an important consideration.
The Cumulative Incidence Function (CIF), as distinct from 1 – S(t), allows for estimation of the incidence of the occurrence of an event while taking competing risk into account. This allows one to estimate incidence in a population where all competing events must be accounted for in clinical decision making. The cumulative incidence function for the kth cause is defined as: CIFk(t) = Pr(T ≤ t,D = k), where D is a variable denoting the type of event that occurred. A key point is that, in the competing risks setting, only 1 event type can occur, such that the occurrence of 1 event precludes the subsequent occurrence of other event types. The function CIFk(t) denotes the probability of experiencing the kth event before time t and before the occurrence of a different type of event. The CIF has the desirable property that the sum of the CIF estimates of the incidence of each of the individual outcomes will equal the CIF estimates of the incidence of the composite outcome consisting of all of the competing events. Unlike the survival function in the absence of competing risks, CIFk(t) will not necessarily approach unity as time becomes large, because of the occurrence of competing events that preclude the occurrence of events of type k. In the case study that follows, when using the CIF, the estimated incidence of cardiovascular death within 5 years of hospital admission was 36.8%. This estimate was 6.2% lower than the estimate obtained using the complement of the Kaplan-Meier function. This illustrates the upward bias that can be observed when naively using Kaplan-Meier estimate in the presence of competing risks.
Austin P.C., Lee D.S, & Fine J.P. (2016). Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation, 133(6), 601-609.
Publication 2016
Cardiovascular System Congestive Heart Failure Patients Population at Risk

Most recents protocols related to «Austin»

1
Healthcare Networks Recruitment Protocol
Participants are recruited from four metropolitan university teaching healthcare networks in Melbourne, Victoria, Australia: Austin Health, Eastern Health, Epworth Health, and Western Health. Central ethics approval has been granted by the Austin Health Human Research Ethics Committee (lead site, HREC/16/Austin/45), with site-specific governance approval from others (most recent approval 07 September 2023, Protocol_v14). Austin, Eastern and Western Health sites are public hospital networks, whilst Epworth Health is a private hospital network with sites throughout the state of Victoria.
Brodtmann A., Billett A., Telfer R., Adkins K., White L., McCambridge L.J., Burrell L.M., Thijs V., Kramer S., Werden E., Cardoso B.R., Pase M., Hung S.H., Churilov L., Bernhardt J., Hayward K, & Johnson L. (2024). ZOom Delivered Intervention Against Cognitive decline (ZODIAC) COVID-19 pandemic adaptations to the Post-Ischaemic Stroke Cardiovascular Exercise Study (PISCES): protocol for a randomised controlled trial of remotely delivered fitness training for brain health. Trials, 25, 329.
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Publication 2024
2
Ethics Approved Informed Consent Study
Written informed consent was provided by all participants (or their legal guardian) before study procedures. Ethics for this study were approved by the Austin Health Human Research Ethics Committee (HREC/17/Austin/202). All practices were conducted in accordance with the relevant guidelines and regulations approved by the Austin Health Human Research Ethics Committee.
Symons G.F., Gregg M.C., Hicks A.J., Rowe C.C., Shultz S.R., Ponsford J.L, & Spitz G. (2024). Altered grey matter structural covariance in chronic moderate–severe traumatic brain injury. Scientific Reports, 14, 1728.
Full text: Click here
Publication 2024
3
Pilot Study of Informed Consent
This pilot study was reviewed and approved by the Human Research Ethics Committee (HREC) of Austin Health under the application number HREC/68362/Austin‐2022.
Written informed consent was obtained from all people participating in the pilot study.
Marquina C., Foster E., Chen Z., Vaughan D.N., Abbott D.F., Tailby C., Jackson G.D., Kwan P, & Ademi Z. (2024). Work productivity, quality of life, and care needs: An unfolding epilepsy burden revealed in the Australian Epilepsy Project pilot study. Epilepsia Open, 9(2), 739-749.
Publication 2024
4
Parasite and Bacterial Identification
Not available on PMC !
The collected samples from the two areas of study were also subjected to parasitological and bacteriological examinations, according to Eissa (2016) and Austin and Austin (2012) (link), respectively, to precisely identify the causative agent or agents of deaths.
El‐Zayat S.A., Abdel‐Motaal F.F., Mohamed S., & Hamouda A.H. (2024). Efficiency of Persicaria salicifolia as a natural alternative antifungal against Aspergillus flavus infection in Oreochromis niloticus from Aswan Governorate, Egypt. Aquaculture International.
Publication 2024
5
Analgosedation Comparison: Fentanyl vs Morphine

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Publication 2024

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More about "Austin"

Discover the vibrant city of Austin, the dynamic capital of Texas, known for its thriving arts, music, and technology scenes.
Situated on the picturesque Colorado River, this progressive metropolis blends Southern charm with a cutting-edge culture.
With a population exceeding 950,000, Austin boasts a diverse array of attractions, from the iconic live music venues of 6th Street to the serene hiking trails of the Barton Creek Greenbelt.
The city is home to the renowned University of Texas at Austin, a leading research institution that contributes to the city's intellectually stimulating environment.
Austin has also emerged as a hub for startups and innovative companies, making it a desirable destination for those in the tech industry.
Whether you're exploring the city's famous food trucks, attending one of its lively festivals, or immersing yourself in the great outdoors, Austin provides an unparalleled experience for visitors and residents alike.
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