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Los Angeles, CA, USA: Seeking role in a TV show. [April 2008]
Plays a character whose name is Cassidy in the Middle and a character who speaks to a character whose fiancee name is Cassidy in this Is Us.
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Galadriel Stineman became a familiar face to many after her several season arc as Cassidy on ABC's The Middle. She's also well-known as superhero Gwen Tennyson in the live-action Ben 10 film, and as Ashley on PS4's smash hit video game Until Dawn. Galadriel has guest-starred and recurred on countless hit shows including Glee, Shameless, NCIS: LA, Bones, True Blood and many more. Most recently, you could spot her in memorable roles on This Is Us, The Kids Are Alright, 9-1-1 and the comedy feature Blowing Up Right Now. In her spare time, Galadriel loves to read everything she can get her hands on, garden and write. You can often find her exploring National Parks; camping and hiking with her family. Galadriel is a long-time supporter of the American Foundation for Suicide Prevention, where she raises awareness and speaks at events.
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Affiliation
1 Centre for Statistics in Medicine, Institute of Health Sciences, Oxford, UK.
J J Deeks et al.
Health Technol Assess .
2003 .
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1 Centre for Statistics in Medicine, Institute of Health Sciences, Oxford, UK.
Harmer B, Lee S, Duong TVH, Saadabadi A.
Harmer B, et al.
2022 May 18. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan–.
2022 May 18. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan–.
PMID: 33351435
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Soll RF, Ovelman C, McGuire W.
Soll RF, et al.
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
Early Hum Dev. 2020.
PMID: 33036834
Anglemyer A, Horvath HT, Bero L.
Anglemyer A, et al.
Cochrane Database Syst Rev. 2014 Apr 29;2014(4):MR000034. doi: 10.1002/14651858.MR000034.pub2.
Cochrane Database Syst Rev. 2014.
PMID: 24782322
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Rank CU, Lynggaard LS, Als-Nielsen B, Stock W, Toft N, Nielsen OJ, Frandsen TL, Tuckuviene R, Schmiegelow K.
Rank CU, et al.
Cochrane Database Syst Rev. 2020 Oct 10;10(10):CD013399. doi: 10.1002/14651858.CD013399.pub2.
Cochrane Database Syst Rev. 2020.
PMID: 33038027
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Page MJ, McKenzie JE, Kirkham J, Dwan K, Kramer S, Green S, Forbes A.
Page MJ, et al.
Cochrane Database Syst Rev. 2014 Oct 1;2014(10):MR000035. doi: 10.1002/14651858.MR000035.pub2.
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J Biomed Phys Eng. 2023 Feb 1;13(1):3-16. doi: 10.31661/jbpe.v0i0.2109-1398. eCollection 2023 Feb.
J Biomed Phys Eng. 2023.
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Objectives:
To consider methods and related evidence for evaluating bias in non-randomised intervention studies.
Data sources:
Systematic reviews and methodological papers were identified from a search of electronic databases; handsearches of key medical journals and contact with experts working in the field. New empirical studies were conducted using data from two large randomised clinical trials.
Methods:
Three systematic reviews and new empirical investigations were conducted. The reviews considered, in regard to non-randomised studies, (1) the existing evidence of bias, (2) the content of quality assessment tools, (3) the ways that study quality has been assessed and addressed. (4) The empirical investigations were conducted generating non-randomised studies from two large, multicentre randomised controlled trials (RCTs) and selectively resampling trial participants according to allocated treatment, centre and period.
Results:
In the systematic reviews, eight studies compared results of randomised and non-randomised studies across multiple interventions using meta-epidemiological techniques. A total of 194 tools were identified that could be or had been used to assess non-randomised studies. Sixty tools covered at least five of six pre-specified internal validity domains. Fourteen tools covered three of four core items of particular importance for non-randomised studies. Six tools were thought suitable for use in systematic reviews. Of 511 systematic reviews that included non-randomised studies, only 169 (33%) assessed study quality. Sixty-nine reviews investigated the impact of quality on study results in a quantitative manner. The new empirical studies estimated the bias associated with non-random allocation and found that the bias could lead to consistent over- or underestimations of treatment effects, also the bias increased variation in results for both historical and concurrent controls, owing to haphazard differences in case-mix between groups. The biases were large enough to lead studies falsely to conclude significant findings of benefit or harm. Four strategies for case-mix adjustment were evaluated: none adequately adjusted for bias in historically and concurrently controlled studies. Logistic regression on average increased bias. Propensity score methods performed better, but were not satisfactory in most situations. Detailed investigation revealed that adequate adjustment can only be achieved in the unrealistic situation when selection depends on a single factor.
Conclusions:
Results of non-randomised studies sometimes, but not always, differ from results of randomised studies of the same intervention. Non-randomised studies may still give seriously misleading results when treated and control gro
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