Efficient and effective use of longitudinal data

医学研究中收集的信息往往没有得到充分利用。有时，这是因为数据收集的问题，参与者拒绝回答某些问题，或者决定在药物试验结束前停止治疗。其他时候，缺乏提取相关发现的简单工具会限制研究人员深入研究数据的能力。许多现有的数据是纵向的，研究对象被跟踪了几周、几个月或几年。纵向数据特别有价值，因为它可以观察生活事件或临床干预导致的健康状况变化。理解纵向数据的一个方便方法是关注这些变化，使用统计模型将过去生活方式和治疗决策的可预测影响与人类生物学和行为的不可预测方面分开。该奖学金将提供统计工具，以更充分地利用纵向研究。这些工具旨在简单地处理不完整患者记录的不确定性，从而得出有关疾病预后和进展的有意义的结论。使用这些新工具重新检查现有数据具有相当大的潜力，可以通过更好的科学理解来改善人类健康。

Information gathered in medical studies is often underused. Sometimes, this is because of problemswith data collection, where participants decline to provide answers to certain questions, or decide todiscontinue treatment before the end of a drug trial. Other times, a lack of simple tools to extractrelevant findings can restrict a researcher‘s ability to delve deeply into the data.Much existing data is longitudinal, where study subjects are followed over several weeks, months oryears. Longitudinal data is particularly valuable because it is possible to observe changes in healthconditions resulting from life events or clinical interventions. One convenient approach tounderstanding longitudinal data is to focus on these changes, using statistical models to separateout the predictable effects of past lifestyle and treatment decisions from the unpredictable aspectsof human biology and behaviour.This fellowship will provide statistical tools to make fuller use of longitudinal studies. These tools aredesigned to deal simply with the uncertainty surrounding incomplete patient records, allowingmeaningful conclusions to be drawn about disease prognosis and progression. Re-examining existingdata using these new tools has considerable potential to improve human health through betterscientific understanding.