बायोमेट्रिक्स और बायोस्टैटिस्टिक्स जर्नल

For approval of generic drug products, the United States Food and Drug Administration (FDA) has published several regulatory guidance to assist the sponsors in preparing documents, which provide substantial evidence for demonstration of bioequivalence between a generic (test) product and its innovative (reference) product (e.g., FDA, 1992, 2003) through the conduct of bioavailability and bioequivalence studies. Bioavailability and bioequivalence studies are usually conducted under crossover designs such as a standard 2x2 crossover design or a higher-order crossover design. Under a crossover design, bioequivalence is commonly evaluated using a two one-sided tests procedure (each at a 5% level of significance) or a 90% confidence interval approach. Bioequivalence is claimed if the constructed 90% confidence interval for the geometric mean ratio falls entirely within the bioequivalence limit of (80%, 125%). Statistical methods for bioequivalence evaluation are well established and widely accepted in the pharmaceutical industry since the publication of the FDA guidance in 2003. However, several practical issues are commonly encountered during the review of regulatory submissions of generic drug products. In this article, these issues are described. In addition, some recommendations for possible clarification and/or resolutions are made.

Background: Adequate biostatistics knowledge among healthcare professionals is imperative for understanding medical literature and practicing evidence-based medicine. This study assessed the basic and advanced knowledge in biostatistics and clinical research among healthcare workers at the King Fahad Medical City (KFMC), Riyadh, Saudi Arabia.

Methods: In this cross-sectional survey, data was collected from healthcare providers using a self-administered questionnaire, having questions related to demographics, biostatistics and clinical research. Data analysis was performed using statistical package SPSS 22.

Results: Of 194 participants (63 [32.5%] consultants, 52 [26.8%] residents, and 79 [40.7%] allied healthcare providers), 45.4% had positive attitude towards learning biostatistics. Only 35.1% correctly answered biostatistics and clinical research instrument-related questions. Half participants had low score, 33% had good score, and 18-19% had excellent score of basic and advanced knowledge of biostatistics and clinical research. The highest degree and number of years of experience in biostatistics after medical school graduation were significantly (χ2 (2)=16.589, p<0.001) associated with basic and advanced biostatistics knowledge scores.

Conclusion: Timely and painstaking training courses in biostatistics and clinical research are needed to improve the research standards in Saudi Arabia. Interested candidates should collaborated with statisticians to improve quality of their work and enhance their statistical skills.

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.

Methods: The observed effect size was calculated for 12 phase II and 27 phase III completed cancer clinical trials identified by experts as being notable.

Results: 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.

Conclusions: 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.

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.