Strategies for Optimal Time Management in Biostatistical Practice最新文献

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Strategies for Optimal Time Management in Biostatistical Practice 生物统计学实践中最佳时间管理策略

Strategies for Optimal Time Management in Biostatistical Practice Pub Date : 1900-01-01 DOI: 10.37421/jbmbs.2019.10.432

A. Veroniki, L. Thabane

{"title":"Strategies for Optimal Time Management in Biostatistical Practice","authors":"A. Veroniki, L. Thabane","doi":"10.37421/jbmbs.2019.10.432","DOIUrl":"https://doi.org/10.37421/jbmbs.2019.10.432","url":null,"abstract":"Early-career biostatisticians need to spend a lot of time and energy on enhancing their research and methodology skills to establish themselves as independent investigators. To accomplish these goals, they follow several strategies (e.g., publishing their work in high impact factor journals), which help enhance potential impact of their research and build new collaborations. However, these approaches can be time consuming, and hence time management approaches are necessary. However, time management is not usually taught in typical (bio)statistics courses, and biostatisticians often learn such a skill through a trial-and-error process or mentorship support. The aim of this paper was to discuss key questions that biostatisticians may come up during their career development and to offer potential strategies to tackle them. We searched PubMed and JSTOR from inception until October 19, 2018, as well as the Google search engine to identify articles discussing or assessing empirically time management in (bio) statistics. We included any study design, but restricted to English articles only. Our search retrieved no relevant articles. This highlights the gap in the existing literature of biostatistics. In this paper, we discuss and provide potential strategies for key issues (e.g., how to prioritize projects in short timeframes) commonly raised by biostatisticians who begin their career and would like to enhance their time management. Overall, time management can result in greater productivity, higher efficiency, less stress, and better opportunities to achieve career goals and advancement. We encourage biostatisticians to make a good plan of their workload, manage their expectations, and always set goals.","PeriodicalId":223284,"journal":{"name":"Strategies for Optimal Time Management in Biostatistical Practice","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127390295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Clinical Significance of Effect Sizes for Survival and Tumor Response Endpoints Using the Empirical Rule Effect Size 使用经验规则效应量对生存和肿瘤反应终点效应量的临床意义

Strategies for Optimal Time Management in Biostatistical Practice Pub Date : 1900-01-01 DOI: 10.37421/jbmbs.2019.10.433

Major-Elechi Bt, Sloand Ja, Novotny Pj, Sargent Dj, Grothey A, Lafky Jm, Dueck Ac

{"title":"The Clinical Significance of Effect Sizes for Survival and Tumor Response Endpoints Using the Empirical Rule Effect Size","authors":"Major-Elechi Bt, Sloand Ja, Novotny Pj, Sargent Dj, Grothey A, Lafky Jm, Dueck Ac","doi":"10.37421/jbmbs.2019.10.433","DOIUrl":"https://doi.org/10.37421/jbmbs.2019.10.433","url":null,"abstract":"Context: In planning oncology phase II and phase III clinical trials, the size of the expected effect for endpoints such as tumor response and overall survival are key parameters driving the sample size. We applied the empirical rule effect size approach, also known as the ½ standard deviation (SD) method, to define clinically significant effect sizes for overall survival and tumor response endpoints in a series of clinical trials. \u0000 \u0000Methods: The observed effect size was calculated for 12 phase II and 27 phase III completed cancer clinical trials identified by experts as being notable. \u0000 \u0000Results: The effect sizes of the phase II and phase III clinical trials ranged from -0.32 to 0.84 and 0.01 to 0.44 SDs respectively. Effect sizes for all but four of the phase II trials were less than a ½ SD. For phase III studies, the effect sizes for all but one study were below 0.4 SD and roughly 67% of them had an effect size smaller than 0.2 SDs. There were no differences across disease sites, although colorectal and breast trials did have slightly larger effect sizes. \u0000 \u0000Conclusions: Even highly noteworthy existing phase II and phase III oncology clinical trials rarely achieve the ½ SD level of clinical significance. This method allows for more ready interpretation of the clinical significance of overall survival and tumor response endpoints. It allows for cross-study comparison across different endpoints. The method also facilitates study design as it directly builds clinical significance into the study.","PeriodicalId":223284,"journal":{"name":"Strategies for Optimal Time Management in Biostatistical Practice","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131923987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0