 My name is Mawuto Mukaka. I am a medical statistician. I work at the Mahidun Oxford Tropical Medicine Research Unit in Thailand. My work involves designing of research studies, performing data analysis, as well as interpretation of research findings. I work in collaboration with medical researchers. Within the model, my team and I are statisticians, and we are involved in the design of studies, which involves working out the number of individuals that need to be enrolled in a study. We are also involved in statistical analysis of data. We perform data cleaning. We do data aggregation, which is combining datasets from several sites. And in the end, we make a report, which we submit to investigators, and we do interpret the findings of the research. And we also take part in manuscript development for publications. Most recently, we in our team have worked on a study that involved malaria elimination. And our role was to combine datasets, do data cleaning, and then perform analysis. So this study was done in four countries in Southeast Asia, which are Cambodia, Vietnam, Laos and Myanmar. That was TME, which was giving mass drug administration of dihydroatomestanine perperakwine with a single dose perperakwine. In our field, there are so many methods that have been developed in the area of medical research. And these include time-to-evit analysis methods. And it is the responsibility of us as statisticians in my team to identify these methods and recommend to medical researchers. In the area of medical research, there were traditional methods of analyzing data, which was using proportions to assess efficacy or effectiveness. But more recently, researchers recommend that we use survival methods or time-to-event methods, which are able to capture issues that relate to missing data. So these methods are not easily accessible to medical researchers, but it takes statisticians to go into literature, identify these methods and make recommendations to the medical researchers. So that is where we come in as medical statisticians. So like in the past, if you use proportions, you would end up losing some data because if some people did not complete the follow-up period, they may not be part of the analysis. But these new methods allow those individuals that have partial information to be included in research or analysis. Medical statistics is very, very important in research. A poor design study will not answer the research question that a medical researcher intended to answer. And unfortunately, you cannot reverse or use any statistics to compensate for a poor design study. So it means it is worth of money. And similarly, if you use wrong methods to analyze your data, you end up with wrong conclusions. And therefore, you need to involve a statistician right from the design stage and throughout the conduct of the trial up to reporting and interpretation of findings. This means that we need to make sure that statisticians are funded and included in any activity that involves medical research. Medical research aims at using research findings to translate into medical practice and policy change. And as statisticians, we are part and parcel of that research activity. We are therefore an important component of translational medicine as statisticians.