Learn about the main challenges facing biostatisticians today, and how to deal with them effectively. Improve your data skills and career prospects in biostatistics.

In clinical trials, all key (primary, secondary) analyses must be planned in the SAP. Planning analyses requires anticipating and addressing FUTURE problems, i.e. prior to seeing the data, which can be VERY challenging. Every single aspect of the analysis may be a source of serious problems, compromising the analysis and interpretation. We need to anticipate and address them. The more problems we prepare for (violated assumptions, excessive number of missing observations, especially with non-random(!) patterns in multiple endpoints, test/models convergence failure, presence of extreme observations, strongly distorted distributions) the less likely the trial will fail at the last stage: analysis. And ALL this must comply with the guidelines!

What are the biggest challenges facing biostatisticians today?

Biostatistics is the application of statistics to biological and health-related problems, such as designing and analyzing clinical trials, epidemiological studies, and genomic data. Biostatisticians play a vital role in advancing scientific knowledge and improving human health, but they also face many challenges in their work. In this article, we will discuss some of the biggest challenges facing biostatisticians today, and how they can overcome them.

1 Data quality and availability

One of the main challenges for biostatisticians is to ensure the quality and availability of the data they use for their analyses. Data quality can be affected by various factors, such as measurement errors, missing values, outliers, confounding variables, and bias. Data availability can be limited by ethical, legal, and logistical issues, such as privacy, consent, data sharing, and data storage. Biostatisticians need to apply appropriate methods and tools to check, clean, manage, and protect their data, and to deal with any data quality and availability issues that may arise.

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Stelios Lamprou, PhD

Researcher/Analyst @ University of Glasgow | PhD in Precision Medicine | Molecular-Computational Biologist & ML Developer
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In biostatistics, one of the challenges I faced was ensuring data quality, particularly when identifying differentially expressed miRNAs in Disease 1 versus Disease 2 for biomarker discovery. The dataset I worked with had good statistical power, but it was retrospectively generated. This meant there was no way to ascertain how the samples were stored and handled, which raised questions about the reliability of the data. To address this issue, I repeated the analysis on a different, independently collected, prospectively collected cohort. However, this process was complex, costly, and time-consuming. It underscored the importance of meticulous data collection and storage protocols in ensuring the reliability of data analysis outcomes.

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Anne Woods

Lady IDO (Intermediate Data Object)
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Statistics-esque degrees, titles, certification. The most reliable indicator of a statistician is how much mathematical statistics training he/she has.

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2 Data complexity and heterogeneity

Another challenge for biostatisticians is to cope with the complexity and heterogeneity of the data they encounter in biostatistics. Data complexity refers to the high dimensionality, nonlinearity, and interdependence of the data, which can pose computational and statistical challenges. Data heterogeneity refers to the diversity and variability of the data sources, types, formats, and scales, which can require integration and harmonization. Biostatisticians need to use advanced techniques and software to handle, analyze, and interpret complex and heterogeneous data, and to account for any uncertainty and variability in their results.

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Eren Unlu

Co-Founder
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For those who work with high dimensional gene expression or similar datasets, I advise to have a look at Similarity Weighted Non-negative Embedding (SWNE) which uses NMF to embed both in datapoint and feature axes to create a representative 2D mapping.

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3 Data communication and visualization

A third challenge for biostatisticians is to communicate and visualize their data and findings effectively to different audiences, such as researchers, clinicians, policy makers, and the public. Data communication and visualization involve choosing the right methods, formats, and media to present the data and the statistical analyses in a clear, accurate, and meaningful way. Biostatisticians need to have good communication and visualization skills, and to use appropriate tools and platforms to convey their messages and insights.

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4 Data ethics and reproducibility

A fourth challenge for biostatisticians is to adhere to the ethical and reproducible standards of their profession and their field. Data ethics and reproducibility involve following the principles and guidelines of responsible conduct of research, such as honesty, integrity, transparency, accountability, and quality. Biostatisticians need to respect the rights and interests of the data subjects, the data providers, and the data users, and to ensure the validity, reliability, and replicability of their data and analyses.

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5 Data innovation and collaboration

A fifth challenge for biostatisticians is to keep up with the innovation and collaboration opportunities in biostatistics. Data innovation and collaboration involve exploring new methods, techniques, applications, and domains of biostatistics, and engaging with other disciplines, sectors, and stakeholders. Biostatisticians need to have a curiosity and a passion for learning, and to participate in interdisciplinary and cross-sectoral projects and networks that can enhance their skills, knowledge, and impact.

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6 Data skills and career development

A sixth challenge for biostatisticians is to develop and maintain their data skills and career prospects in biostatistics. Data skills and career development involve acquiring and updating the technical, analytical, and soft skills that are essential for biostatisticians, and seeking and creating the professional and personal opportunities that can advance their careers in biostatistics. Biostatisticians need to have a clear and realistic vision of their goals and aspirations, and to pursue the education, training, mentoring, and networking that can support their growth and success.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Adrian Olszewski

Clinical Trials Biostatistician at 2KMM - 100% R-based CRO ⦿ Frequentist (non-Bayesian) paradigm ⦿ NOT a Data Scientist (no ML/AI) ⦿ Against anti-car/-meat/-cash and C40 restrictions
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In clinical trials, all key (primary, secondary) analyses must be planned in the SAP. Planning analyses requires anticipating and addressing FUTURE problems, i.e. prior to seeing the data, which can be VERY challenging. Every single aspect of the analysis may be a source of serious problems, compromising the analysis and interpretation. We need to anticipate and address them. The more problems we prepare for (violated assumptions, excessive number of missing observations, especially with non-random(!) patterns in multiple endpoints, test/models convergence failure, presence of extreme observations, strongly distorted distributions) the less likely the trial will fail at the last stage: analysis. And ALL this must comply with the guidelines!

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What are the biggest challenges facing biostatisticians today?

1

2

3

4

5

6

7

1 Data quality and availability

2 Data complexity and heterogeneity

3 Data communication and visualization

4 Data ethics and reproducibility

5 Data innovation and collaboration

6 Data skills and career development

7 Here’s what else to consider

Statistics

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What are the biggest challenges facing biostatisticians today?

1

2

3

4

5

6

7

1 Data quality and availability

2 Data complexity and heterogeneity

3 Data communication and visualization

4 Data ethics and reproducibility

5 Data innovation and collaboration

6 Data skills and career development

7 Here’s what else to consider

Statistics

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