Development of Biostatistics: From Past to Future

Abstract

Biostatistics which is the application of statistics in the field of health and biology; provides powerful tools for creating questions, designing studies, developing measurements, and analyzing data and has an important place in determining the efficacy and safety of products such as drugs and vaccines. The impact of statistical sciences on medical and biological sciences has increased rapidly during the last few decades. Clinicians need to understand statistics well enough to follow up and evaluate empirical studies that provide an evidence base for clinical practice. Recent advances in biomedical research have created both new challenges and opportunities for statisticians and data scientists. Big data analytics, precision medicine, artificial intelligence, causal inference, and other new research resources inspire data scientists to develop modern statistical methods and innovative inference procedures. Therefore new philosophies such as causal models and prediction, new models such as graphical chain models and random effects models, faster computers and new clever algorithms for integration and maximization are needed. Without adequate investment in biostatistics, all medical research is at a significant risk of “drowning in data, but starving for knowledge”.

Keywords

• Biostatistics
• data science
• big data

Biyoistatistiğin Gelişimi: Geçmişten Geleceğe

Abstract

İstatistiğin sağlık ve biyoloji alanındaki uygulaması olan biyoistatistik; soru oluşturma, çalışma tasarlama, ölçüm geliştirme ve verilerin analizi için güçlü araçlar sağlar ve ilaç, aşı gibi ürünlerin etkinlik ve güvenliğinin belirlenmesinde önemli bir yere sahiptir. İstatistik biliminin tıp ve biyolojik bilimler üzerindeki etkisi son yıllarda hızla artmıştır. Klinisyenlerin, klinik uygulama için kanıtlar sağlayan deneysel çalışmaları izlemek ve değerlendirmek için istatistiği yeterince iyi anlamaları gerekir. Biyomedikal araştırmalardaki son gelişmeler, istatistikçiler ve veri bilimcileri için hem yeni zorluklar hem de fırsatlar yaratmıştır. Büyük veri analitiği, hassas (kişiselleştirilmiş) tıp, yapay zeka, nedensel çıkarım ve diğer yeni araştırma kaynakları; veri bilimcilerine modern istatistiksel teknikler ve yenilikçi çıkarım yöntemleri geliştirme konusunda ilham vermektedir. Bu nedenle; nedensel modeller ve tahmin gibi yeni felsefelere, grafik zincir ve rastgele etki gibi yeni modellere, daha hızlı bilgisayarlara, entegrasyon ve maksimizasyon için yeni akıllı algoritmalara ihtiyaç vardır. Biyoistatistiğe yeterli yatırım yapılmazsa, tüm sağlık araştırmaları önemli bir “veride boğulma, ancak bilgi açlığından ölme” riski altındadır.

Keywords

• Biyoistatistik
• veri bilimi
• büyük veri

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