4.5.2 The effect of subgroups on sample size

Decision-making for AMR can be strengthened by analysing subgroups of data to gain a deeper understanding of how resistance patterns and trends vary based on clinical parameters such as sex, gender, age, socioeconomic context and other axes of inequity. (You can learn more about the need for disaggregated AMR data and the current biases and inequities in AMR surveillance systems in the Gender and equity in AMR surveillance course.)

Analysing subgroups of data within a study typically requires a significantly larger sample size compared to analysing the overall group. The greater the number of subgroups to be analysed the larger the overall sample size needs to be to reliably interpret results within each subgroup.