Journal of Biometrics & Biostatistics

Quantum computing is a cutting-edge field of computing that harnesses the principles of quantum mechanics to perform certain types of calculations much faster than classical computers. Unlike classical bits, which can represent either a 0 or a 1, quantum bits or qubits can exist in a superposition of states, allowing them to represent both 0 and 1 simultaneously. Additionally, qubits can be entangled, meaning the state of one qubit is dependent on the state of another, even if they are physically separated. Qubits can exist in multiple states at once, which enables quantum computers to explore many possibilities simultaneously. Classical computers, in contrast, process data sequentially.

The reported study was motivated by the necessity to select 30 soybean lines from a total of 137 for a sophisticated 3-D phenotyping analysis of the Root System Architecture (RSA), which would not allow that all the lines be included and replicated. A representative subset of size 30 was found after performing four cluster analyses and comparing the results of two more particularly. These two cluster analyses are based on the data for 12 RSA-related traits previously collected in 2D on three replicates of the 137 soybean lines and the first six principal components representing 95% of the total dispersion after data standardization in a preliminary Principal Component Analysis (PCA). The two cluster analysis procedures provided 16 soybean lines that were the closest to the centroid of their respective cluster in both cases. Fourteen more were found to be common and at a distance from the centroid below a pre-set threshold value without being the closest. The final selection of 30 excludes two soybean lines that were the second member selected from their cluster, and includes instead two soybean lines that are the closest and second closest to their respective centroid in the cluster analysis after PCA on standardized data, but are not well represented in the other cluster analysis. In conclusion, the 93.3% overlap between the two sets of results shows a robust clustering structure in RSA 2-D phenotyping in soybean. Our statistical approaches and procedures can be followed and applied in other biological frameworks than plant phenotyping.

Artificial Intelligence (AI) and biostatistics are two distinct but interconnected fields that play a crucial role in healthcare, medical research, and the life sciences. Biostatistics is primarily concerned with collecting, analyzing, and interpreting data in the life sciences. AI techniques, such as machine learning, can be used to automate the analysis of large and complex biological and medical datasets. AI algorithms can identify patterns, correlations, and insights from these datasets that may not be apparent through traditional statistical methods.

Ethical conduct in biostatistics is essential to ensure the integrity, credibility and trustworthiness of research in the field of healthcare and biomedical sciences. Biostatisticians play a crucial role in the design, analysis, and interpretation of research studies, and they must adhere to high ethical standards. Biostatisticians should respect the principles of informed consent when working with human subjects in research. They must ensure that participants understand the purpose, risks, and benefits of the study, especially when handling sensitive or personal data.

Biostatistics, a field that specializes in the analysis of data arising from biomedical research, remains a vibrant and ever-evolving discipline. Recent breakthroughs in biomedical research have ushered in a new era of complexity and opened up fresh challenges and opportunities for statisticians and data scientists. Notable areas of advancement in biostatistics include the analysis of complex time-to-event data and addressing issues related to missing data. These challenges have become particularly prominent in application areas such as medicine, genetics, neuroscience, and engineering. Biostatistics is indeed a highly dynamic and evolving field that continually adapts to the challenges and opportunities presented by advances in biomedical research.

Childhood mortality in India has declined substantially in during last three decades (1992-2021) from 119 to 42 per 1000 live births. However, this decline does not necessarily imply reduction in the inequalities which remains both in accesses to quality care and health outcomes among under-five children in Uttar Pradesh (India). Objective: To estimate and quantify the prevailing socio-economic inequalities contributing to Under-five mortality in Uttar Pradesh along with the temporal trends over 2005–2021. Methods: The last three rounds of National Family Health Survey (NFHS) were used to estimate and quantify the socioeconomic inequalities and factors contributing in the under-five mortalities by using concentration indexes (CI), concentration curves (CCs) and decomposition analysis. Results: It was observed that during the period 2019-21 and 2015-16, high concentration of socio-economic inequalities for U5MR among women of age 35 years or more, had primary education, and belonged to Scheduled caste/tribe and Hindus. While during the period 2005-06, high concentration of inequalities was found among women of age 25-34 years, belonged to SC/ST and OBC caste groups, and among Hindus. Overall, mother’s education and place of residence mostly explained the U5MR inequality in all three time periods. Conclusion: The findings suggest that more efforts are needed in the state of Uttar Pradesh to narrow the income related U5MR inequalities. An effective way to reduce inequality is not only to reduce the gap of income but also focus should be made on increasing the level of education of mothers as educational attainment is critical in imparting the feelings of self-worth and confidence which are critical in bringing the changes in health-related behaviour.

Every new day come up with different challenges in healthcare sector in developing country like us. So this review article tells us the role of meta-analysis in current healthcare share and current health problems dealing with ‘evidence based medicine practices’. This article is the combination of healthcare practices and meta-analysis in the field of medicine. Consideration of current trends and scenario demonstrates a consistently increase in use of meta-analysis especially in randomized controlled trials and interventional studies. Meta-analyses look for new information in existing data. Comparing the results of meta-analyses with subsequent findings from large-scale, well-conducted, randomized controlled trials (so-called RCT’s) is one way to assess the validity of this new knowledge. Such comparisons have yielded mixed findings thus far, with good agreement in the majority of cases but notable inconsistencies in others. One such exercise, for example, resulted in the publication of a paper titled "Lessons from a "successful, safe, simple intervention" that wasn't" misleading meta-analysis (use of metformin after diabetes mellitus). The inadequacies in meta-analyses that have been later challenged by data from RCT’s can often be discovered with the benefit of hindsight. So this article directly or indirectly helps to researchers to adopt new knowledge in Meta-analysis especially for current healthcare practice. We can’t separate them as healthcare and meta-analysis both are the two sides of a same coin.