Brijesh Sathian

Meta-analysis is a quantitative approach for systematically combining the results of previous researches in order to arrive at useful conclusions from a body of research. Meta-analyses offer a systematic and quantitative approach to synthesising evidence to answer important therapeutic questions. Nevertheless, pitfalls abound in the execution of meta-analyses and they are fundamentally limited by the quality of the underlying studies. For healthcare managers and clinicians, careful reviewing of published meta-analyses and a balanced assessment of their deficiencies is likely to become an increasingly important in resolving therapeutic uncertainty. It is most useful in summarizing prior research findings when individual studies are too small to yield a valid conclusion. Meta-analysis is most often applied to combine the results of Randomised Control trials (RCTs). For non-experimental studies, this method is powerful when there are many studies with low statistical power. Meta-analyses are now a hallmark of evidence-based medicine.

This paper illustrates the growing importance of mixed-methods research to many health disciplines ranging from nursing to epidemiology. Mixed-methods approaches requires not only the skills of the individual quantitative and qualitative methods but also a skill set to bring two methods/datasets/findings together in the most appropriate way. Health researchers need to pay careful attention to the 'best' approach to designing, implementing, analysing, integrating both quantitative (number) and qualitative (word) information and writing this up in a way offers greater insights and enhances its applicability. This paper highlights the strengths and weaknesses of mixed-methods approaches as well as some of the common mistakes made by researchers applying mixed-methods for the first time.

The World Health Organization has reported more than 31,186,000 confirmed cases of coronavirus disease-19 (COVID-19), including 962,343 deaths, worldwide as on September 21, 2020. The current COVID-19 pandemic is affecting clinical research activities in most parts of the world. The focus on developing a vaccine for SARS-CoV-2 and the treatment of COVID-19 is, in fact, disrupting many upcoming and/or ongoing clinical trials on other diseases around the globe. On March 18, 2020, the United States Food and Drug Administration (FDA) issued an updated guideline for the conduct of clinical trials during the current health emergency situation. The potential challenges, such as social distancing and quarantines, result in study participants' inaccessibility and trial personnel for in-person scheduled study visits and/or follow-up. Due to the sudden onset and wide-spread impact of COVID-19, its influence on the management of clinical trials and research necessitates urgent attention. Therefore, our systematic review of the literature aims to assess the impact of the COVID-19 pandemic on the conduction of clinical trials and research. The search for the relevant articles for review included the keywords "COVID-19" AND "clinical trial" in PubMed, MEDLINE, Embase, Google scholar and Google electronic databases. Key findings include: delaying subject enrollment and operational gaps in most ongoing clinical trials, which in turn has a negative impact on trial programmes and data integrity. Globally, most sites conducting clinical trials other than COVID-19 are experiencing a delay in timelines and a complete halt of operations in lieu of this pandemic, thus affecting clinical research outcomes.

Sample size determination is one of the central tenets of medical research. If the sample size is inadequate, then the study will fail to detect a real difference between the effects of two clinical approaches. On the contrary, if the sample size is larger than what is needed, the study will become cumbersome and ethically prohibitive. Apart from this, the study will become expensive, time consuming and will have no added advantages. A study which needs a large sample size to prove any significant difference in two treatments must ensure the appropriate sample size. It is better to terminate such a study when the required sample size cannot be attained so that the funds and manpower can be conserved. When dealing with multiple sub-groups in a population the sample size should be increased the adequate level for each sub-group. To ensure the reliability of final comparison of the result, the significant level and power must be fixed before the sample size determination. Sample size determination is very important and always a difficult process to handle. It requires the collaboration of a specialist who has good scientific knowledge in the art and practice of medical statistics. A few suggestions are made in this paper regarding the methods to determine an optimum sample size in descriptive and analytical studies.Key Words: Sample size; Power analysis; Medical researchDOI: 10.3126/nje.v1i1.4100Nepal Journal of Epidemiology 2010;1 (1):4-10

Breast cancer is the most common malignancy in women around the world. About one in 12 women in the West develop breast cancer at some point in life. It is estimated that 5%-10% of all breast cancer cases in women are linked to hereditary susceptibility due to mutations in autosomal dominant genes. The two key players associated with high breast cancer risk are mutations in BRCA 1 and BRCA 2. Another highly important mutation can occur in TP53 resulting in a triple negative breast cancer. However, the great majority of breast cancer cases are not related to a mutated gene of high penetrance, but to genes of low penetrance such as CHEK2, CDH1, NBS1, RAD50, BRIP1 and PALB2, which are frequently mutated in the general population. In this review, we discuss the entire spectrum of mutations which are associated with breast cancer.