Biostatistical Methodologies in Clinical Trials: An Overview of Recent Developments and Pitfalls

Abstract

Biostatistical analysis has emerged as one of the most crucial applications for clinical trial systems. The relevance of statistical analysis in clinical trials cannot be overstated in the advancement of novel pharmaceuticals and therapeutics. Data volumes, including effectiveness and safety summaries, are produced throughout the process of developing and approving novel medicines. Data standards developed by the Clinical Data Interchange Standards Consortium (CDISC) are well-established, widely accepted, and widely utilized by the pharmaceutical sector for submissions to regulatory bodies. Biostatistical programs like PRM, ODM, CDASH, SDTM, ADaM, SAS, and ARDM have come a long way in recent years, and this article highlights those developments. The present study has made an effort to report on the typical difficulties encountered when using biostatistical methods, as with every new discovery, there is always a potential pitfall. Even the main endpoint of the experiment might be improperly analysed without adequate validation of model assumptions. Biostatisticians must collaborate closely with physicians to choose the most suitable statistical methods for data assessment; otherwise, the results may be ambiguous or misleading. To guarantee that a clinical trial satisfies the requirements of regulatory agencies in drug approvals, future research should concentrate on strategies that can enhance the compliance and validation processes.