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Dear all,</div>
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My DSRA research project has been approved by the SNSF and I am currently looking for a PhD student and a PostDoc.</div>
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I am providing the abstract of the DSRA project and the links to both advertisements below.</div>
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Please spread the word if you know someone who might be interested.</div>
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Thank you! & Best regards,</div>
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Malgorzata Roos</div>
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######################### DSRA project #########################</div>
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Title: Credibility of Recruitment Rates, Counts, and Duration: Statistical Methods and Open-Source Software for Design-Stage Recruitment Analyses</div>
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Duration: 4 years</div>
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Application PhD: https://jobs.uzh.ch/job-vacancies/phd-position-in-biostatistics-for-4-years/407fc5ee-3783-43cf-898f-d0f367223ce1</div>
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Application PostDoc: https://jobs.uzh.ch/job-vacancies/postdoctoral-position-in-biostatistics-for-4-years/7ddd5dda-bd49-4d7e-8346-888b65b69104</div>
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Abstract:</div>
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For statistical analyses to draw valid and reliable final conclusions, an adequate number of observations is the essential prerequisite. The ability of a study to collect these observations relies on sufficient recruitment. Specifying an accurate recruitment
rate at the planning stage of a study increases the likelihood that the study design is realistic, adequate observations are collected, and the final statistical inference and conclusions are valid. Thus, the credibility of specified recruitment rates should
be assessed as early as possible, either by investigators at the design stage or by the funding agency early during proposal review.</div>
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Design-stage recruitment analyses impact study designs in all areas of scientific research. They include specifying recruitment rates and the interplay of recruitment rates with the number of observations (counts) and recruitment duration, three characteristics
of recruitment that are difficult to specify the design stage. Inaccurate recruitment projections at the design stage can have far-reaching consequences. For example, overly optimistic and overly confident recruitment projections can result in a study not
collecting sufficient observations on time, thus leading to inconclusive statistical results, and causing unnecessary waste of funds. </div>
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The often-used deterministic simple method to specify recruitment at the design stage forces researchers to rely on one fixed value for the anticipated recruitment rate. It does not allow for any uncertainty in this projection, thus leading to overconfident
recruitment projections. Alternatively, recruitment rates, counts and recruitment duration can be specified by various frequentist and Bayesian probabilistic methods that have accumulated since the 1980s. Because these methods have never been consolidated
into a freely accessible, open-source form, they are difficult to find and rarely used to support planning and predict recruitment at the design stage. Statistical methods for design-stage recruitment analysis should be implemented in freely accessible, free-of-charge
open-source software accompanied by user-friendly documentation and illustrative examples.</div>
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To adequately address these needs, the proposed DSRA project reviews, unifies, consolidates, validates, implements, and clarifies the scope of application of frequentist and Bayesian probabilistic statistical methods for design-stage recruitment analyses. Because
design-stage recruitment analyses can critically depend on assumptions, this project also develops statistical credibility measures: proper scoring rules, informativeness, and sensitivity estimates. Together with simulations and real-world retrospective and
prospective applications, these credibility measures clarify the usability of the statistical methods reviewed. The DSRA project offers investigators and funding agencies worldwide free access to well-documented, user-friendly software implementations for
both offline and interactive online use. </div>
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These developments can impact the training of new generations of scientists and study managers in many areas of research, help investigators worldwide make realistic recruitment predictions, and impact the decisions of funding agencies. Realistic recruitment
specifications at the design stage can increase the likelihood of achieving an adequate number of observations upon completion, thereby considerably reducing the probability of study discontinuation. Such developments can increase the number of convincingly
justified and successfully completed studies, optimizing the use of financial resources, increasing the validity of statistical inference, directly impacting the quality of future research, and accelerating scientific knowledge accumulation worldwide.</div>
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<div>Malgorzata Roos, PD Dr. phil.</div>
<div>Department of Biostatistics, EBPI, Universität Zürich </div>
<div>Hirschengraben 84, CH-8001 Zürich, Schweiz</div>
<div>Büro: HRS F-22.2</div>
<div>Email: Malgorzata.Roos@uzh.ch</div>
<div>www.ebpi.uzh.ch</div>
<div>Phone: +41 (44) 63 44 648</div>
<div>http://www.ebpi.uzh.ch/en/aboutus/ourlocation.html</div>
<div>http://map.search.ch/zuerich/hirschengraben-84</div>
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