Selection bias in clinical sciences
# 20221025 Raku programming solution
enum <UNTREATED REGULAR IRREGULAR>;
my \DOSE_FOR_REGULAR = 100;
my ($nSubjects,$duration,$interval) = 10000, 180, 30;
my (@dosage,@category,@hadcovid) := (0,UNTREATED,False)>>.&{($_ xx $nSubjects).Array};
sub update($pCovid=1e-3, $pStartTreatment=5e-3, $pRedose=¼, @dRange=<3 6 9>) {
for 0 ..^ @dosage.elems -> \i {
unless @hadcovid[i] {
if rand < $pCovid {
@hadcovid[i] = True
} else {
my $dose = @dosage[i];
if $dose==0 && rand < $pStartTreatment or $dose > 0 && rand < $pRedose {
@dosage[i] = $dose += @dRange.roll;
@category[i] = ($dose > DOSE_FOR_REGULAR) ?? REGULAR !! IRREGULAR
}
}
}
}
}
sub kruskal (@sets) {
my $n = ( my @ranked = @sets>>.List.flat.sort ).elems;
my @sr = 0 xx @sets.elems;
my $ix = (@ranked.first: * == True, :k)+1,
my ($arf,$art) = ($ix, $ix+$n) >>/>> 2;
for @sets.kv -> \i,@set { for @set -> $b { @sr[i] += $b ?? $art !! $arf } }
my $H = [+] @sr.kv.map: -> \i,\s { s*s/@sets[i].elems }
return 12/($n*($n+1)) * $H - 3 * ($n + 1)
}
say "Total subjects: $nSubjects\n";
my ($midpoint,$unt,$irr,$reg,$hunt,$hirr,$hreg,@sunt,@sirr,@sreg)=$duration div 2;
for 1 .. $duration -> \day {
update();
if day %% $interval or day == $duration or day == $midpoint {
@sunt = @hadcovid[ @category.grep: UNTREATED,:k ];
@sirr = @hadcovid[ @category.grep: IRREGULAR,:k ];
@sreg = @hadcovid[ @category.grep: REGULAR, :k ];
($unt,$hunt,$irr,$hirr,$reg,$hreg)=(@sunt,@sirr,@sreg).map:{|(.elems,.sum)}
}
if day %% $interval {
printf "Day %d:\n",day;
printf "Untreated: N = %4d, with infection = %4d\n", $unt,$hunt;
printf "Irregular Use: N = %4d, with infection = %4d\n", $irr,$hirr;
printf "Regular Use: N = %4d, with infection = %4d\n\n",$reg,$hreg
}
if day == $midpoint | $duration {
my $stage = day == $midpoint ?? 'midpoint' !! 'study end';
printf "At $stage, Infection case percentages are:\n";
printf " Untreated : %f\n", 100*$hunt/$unt;
printf " Irregulars: %f\n", 100*$hirr/$irr;
printf " Regulars : %f\n\n",100*$hreg/$reg
}
}
printf "Final statistics: H = %f\n", kruskal ( @sunt, @sirr, @sreg )
Output:
Total subjects: 10000
Day 30:
Untreated: N = 8616, with infection = 314
Irregular Use: N = 1383, with infection = 27
Regular Use: N = 1, with infection = 0
Day 60:
Untreated: N = 7511, with infection = 561
Irregular Use: N = 2308, with infection = 87
Regular Use: N = 181, with infection = 1
Day 90:
Untreated: N = 6571, with infection = 760
Irregular Use: N = 2374, with infection = 141
Regular Use: N = 1055, with infection = 19
At midpoint, Infection case percentages are:
Untreated : 11.565972
Irregulars: 5.939343
Regulars : 1.800948
Day 120:
Untreated: N = 5757, with infection = 909
Irregular Use: N = 2097, with infection = 189
Regular Use: N = 2146, with infection = 61
Day 150:
Untreated: N = 5092, with infection = 1054
Irregular Use: N = 1832, with infection = 238
Regular Use: N = 3076, with infection = 132
Day 180:
Untreated: N = 4530, with infection = 1172
Irregular Use: N = 1615, with infection = 282
Regular Use: N = 3855, with infection = 229
At study end, Infection case percentages are:
Untreated : 25.871965
Irregulars: 17.461300
Regulars : 5.940337
Final statistics: H = 248.419454
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