Trim-and-Fill Lite

Problem

Return a simplified trim-and-fill adjusted mean: compute the fixed-effect pooled mean, count studies below and above it, remove the excess studies from the majority side (the most extreme ones), and recompute the pooled mean. Return 0.0 when fewer than two studies are given, any se <= 0, or the kept set is empty.

Hint

Trimming the excess on one side of the funnel shifts the pooled estimate.

Test cases

case 1

in: [[1,1.2,1.4,0.8,0.9],[0.1,0.1,0.1,0.1,0.1]]

expected: 1.1249999999999998

case 2

in: [[1,1.2,1.4],[0.1,0.1,0.1]]

expected: 1.2

case 3

in: [[5,5,5],[0.1,0.2,0.3]]

expected: 5

case 4

in: [[0.1,0.2],[0.1,0.1]]

expected: 0.15000000000000002

case 5

in: [[],[]]

expected: 0

Starter codePython

def trim_and_fill_lite(yi, sei):
    # Your code here
    pass

5 test cases · run your code against them in the editor