To handle vectors of different lengths, you can make time_to_event a cell array:
time_to_event = cell(numel(files),1);
for ii = 1:numel(files)
% dateset = data from the ii-th file
time_to_event{ii} = dataset.time_to_event;
end
Then the median of all elements of all time_to_event vectors can be found like this (assuming each one is a column vector like you show in your examples):
median_time_to_event = nanmedian(vertcat(time_to_event{:}));
And the median of each element of time_to_event (i.e., each time-to-event vector) can be found like this:
median_each = cellfun(@nanmedian,time_to_event)
Here's running a concrete case not based on any data:
time_to_event = cell(4,1);
for ii = 1:4
time_to_event{ii} = rand(randi(10),1);
end
disp(time_to_event)
median_time_to_event = nanmedian(vertcat(time_to_event{:}))
median_each = cellfun(@nanmedian,time_to_event)
