-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathnested_model_case_control.html
1411 lines (1373 loc) · 192 KB
/
nested_model_case_control.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="author" content="Avery Richards" />
<title>Nested and Functional Programming For Case Control Analysis</title>
<style type="text/css">
a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;}
a.anchor-section::before {content: '#';}
.hasAnchor:hover a.anchor-section {visibility: visible;}
</style>
<script>// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020.
document.addEventListener('DOMContentLoaded', function() {
// Do nothing if AnchorJS is used
if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) {
return;
}
const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6');
// Do nothing if sections are already anchored
if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) {
return null;
}
// Use section id when pandoc runs with --section-divs
const section_id = function(x) {
return ((x.classList.contains('section') || (x.tagName === 'SECTION'))
? x.id : '');
};
// Add anchors
h.forEach(function(x) {
const id = x.id || section_id(x.parentElement);
if (id === '') {
return null;
}
let anchor = document.createElement('a');
anchor.href = '#' + id;
anchor.classList = ['anchor-section'];
x.classList.add('hasAnchor');
x.appendChild(anchor);
});
});
</script>
<script>$(document).ready(function(){
if (typeof $('[data-toggle="tooltip"]').tooltip === 'function') {
$('[data-toggle="tooltip"]').tooltip();
}
if ($('[data-toggle="popover"]').popover === 'function') {
$('[data-toggle="popover"]').popover();
}
});
</script>
<style type="text/css">
.lightable-minimal {
border-collapse: separate;
border-spacing: 16px 1px;
width: 100%;
margin-bottom: 10px;
}
.lightable-minimal td {
margin-left: 5px;
margin-right: 5px;
}
.lightable-minimal th {
margin-left: 5px;
margin-right: 5px;
}
.lightable-minimal thead tr:last-child th {
border-bottom: 2px solid #00000050;
empty-cells: hide;
}
.lightable-minimal tbody tr:first-child td {
padding-top: 0.5em;
}
.lightable-minimal.lightable-hover tbody tr:hover {
background-color: #f5f5f5;
}
.lightable-minimal.lightable-striped tbody tr:nth-child(even) {
background-color: #f5f5f5;
}
.lightable-classic {
border-top: 0.16em solid #111111;
border-bottom: 0.16em solid #111111;
width: 100%;
margin-bottom: 10px;
margin: 10px 5px;
}
.lightable-classic tfoot tr td {
border: 0;
}
.lightable-classic tfoot tr:first-child td {
border-top: 0.14em solid #111111;
}
.lightable-classic caption {
color: #222222;
}
.lightable-classic td {
padding-left: 5px;
padding-right: 5px;
color: #222222;
}
.lightable-classic th {
padding-left: 5px;
padding-right: 5px;
font-weight: normal;
color: #222222;
}
.lightable-classic thead tr:last-child th {
border-bottom: 0.10em solid #111111;
}
.lightable-classic.lightable-hover tbody tr:hover {
background-color: #F9EEC1;
}
.lightable-classic.lightable-striped tbody tr:nth-child(even) {
background-color: #f5f5f5;
}
.lightable-classic-2 {
border-top: 3px double #111111;
border-bottom: 3px double #111111;
width: 100%;
margin-bottom: 10px;
}
.lightable-classic-2 tfoot tr td {
border: 0;
}
.lightable-classic-2 tfoot tr:first-child td {
border-top: 3px double #111111;
}
.lightable-classic-2 caption {
color: #222222;
}
.lightable-classic-2 td {
padding-left: 5px;
padding-right: 5px;
color: #222222;
}
.lightable-classic-2 th {
padding-left: 5px;
padding-right: 5px;
font-weight: normal;
color: #222222;
}
.lightable-classic-2 tbody tr:last-child td {
border-bottom: 3px double #111111;
}
.lightable-classic-2 thead tr:last-child th {
border-bottom: 1px solid #111111;
}
.lightable-classic-2.lightable-hover tbody tr:hover {
background-color: #F9EEC1;
}
.lightable-classic-2.lightable-striped tbody tr:nth-child(even) {
background-color: #f5f5f5;
}
.lightable-material {
min-width: 100%;
white-space: nowrap;
table-layout: fixed;
font-family: Roboto, sans-serif;
border: 1px solid #EEE;
border-collapse: collapse;
margin-bottom: 10px;
}
.lightable-material tfoot tr td {
border: 0;
}
.lightable-material tfoot tr:first-child td {
border-top: 1px solid #EEE;
}
.lightable-material th {
height: 56px;
padding-left: 16px;
padding-right: 16px;
}
.lightable-material td {
height: 52px;
padding-left: 16px;
padding-right: 16px;
border-top: 1px solid #eeeeee;
}
.lightable-material.lightable-hover tbody tr:hover {
background-color: #f5f5f5;
}
.lightable-material.lightable-striped tbody tr:nth-child(even) {
background-color: #f5f5f5;
}
.lightable-material.lightable-striped tbody td {
border: 0;
}
.lightable-material.lightable-striped thead tr:last-child th {
border-bottom: 1px solid #ddd;
}
.lightable-material-dark {
min-width: 100%;
white-space: nowrap;
table-layout: fixed;
font-family: Roboto, sans-serif;
border: 1px solid #FFFFFF12;
border-collapse: collapse;
margin-bottom: 10px;
background-color: #363640;
}
.lightable-material-dark tfoot tr td {
border: 0;
}
.lightable-material-dark tfoot tr:first-child td {
border-top: 1px solid #FFFFFF12;
}
.lightable-material-dark th {
height: 56px;
padding-left: 16px;
padding-right: 16px;
color: #FFFFFF60;
}
.lightable-material-dark td {
height: 52px;
padding-left: 16px;
padding-right: 16px;
color: #FFFFFF;
border-top: 1px solid #FFFFFF12;
}
.lightable-material-dark.lightable-hover tbody tr:hover {
background-color: #FFFFFF12;
}
.lightable-material-dark.lightable-striped tbody tr:nth-child(even) {
background-color: #FFFFFF12;
}
.lightable-material-dark.lightable-striped tbody td {
border: 0;
}
.lightable-material-dark.lightable-striped thead tr:last-child th {
border-bottom: 1px solid #FFFFFF12;
}
.lightable-paper {
width: 100%;
margin-bottom: 10px;
color: #444;
}
.lightable-paper tfoot tr td {
border: 0;
}
.lightable-paper tfoot tr:first-child td {
border-top: 1px solid #00000020;
}
.lightable-paper thead tr:last-child th {
color: #666;
vertical-align: bottom;
border-bottom: 1px solid #00000020;
line-height: 1.15em;
padding: 10px 5px;
}
.lightable-paper td {
vertical-align: middle;
border-bottom: 1px solid #00000010;
line-height: 1.15em;
padding: 7px 5px;
}
.lightable-paper.lightable-hover tbody tr:hover {
background-color: #F9EEC1;
}
.lightable-paper.lightable-striped tbody tr:nth-child(even) {
background-color: #00000008;
}
.lightable-paper.lightable-striped tbody td {
border: 0;
}
</style>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css" data-origin="pandoc">
a.sourceLine { display: inline-block; line-height: 1.25; }
a.sourceLine { pointer-events: none; color: inherit; text-decoration: inherit; }
a.sourceLine:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode { white-space: pre; position: relative; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
code.sourceCode { white-space: pre-wrap; }
a.sourceLine { text-indent: -1em; padding-left: 1em; }
}
pre.numberSource a.sourceLine
{ position: relative; left: -4em; }
pre.numberSource a.sourceLine::before
{ content: attr(data-line-number);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; pointer-events: all; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ }
@media screen {
a.sourceLine::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; } /* Alert */
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
code span.at { color: #7d9029; } /* Attribute */
code span.bn { color: #40a070; } /* BaseN */
code span.bu { } /* BuiltIn */
code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
code span.ch { color: #4070a0; } /* Char */
code span.cn { color: #880000; } /* Constant */
code span.co { color: #60a0b0; font-style: italic; } /* Comment */
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
code span.do { color: #ba2121; font-style: italic; } /* Documentation */
code span.dt { color: #902000; } /* DataType */
code span.dv { color: #40a070; } /* DecVal */
code span.er { color: #ff0000; font-weight: bold; } /* Error */
code span.ex { } /* Extension */
code span.fl { color: #40a070; } /* Float */
code span.fu { color: #06287e; } /* Function */
code span.im { } /* Import */
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
code span.kw { color: #007020; font-weight: bold; } /* Keyword */
code span.op { color: #666666; } /* Operator */
code span.ot { color: #007020; } /* Other */
code span.pp { color: #bc7a00; } /* Preprocessor */
code span.sc { color: #4070a0; } /* SpecialChar */
code span.ss { color: #bb6688; } /* SpecialString */
code span.st { color: #4070a0; } /* String */
code span.va { color: #19177c; } /* Variable */
code span.vs { color: #4070a0; } /* VerbatimString */
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
/* A workaround for https://github.com/jgm/pandoc/issues/4278 */
a.sourceLine {
pointer-events: auto;
}
</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
var sheets = document.styleSheets;
for (var i = 0; i < sheets.length; i++) {
if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
try { var rules = sheets[i].cssRules; } catch (e) { continue; }
for (var j = 0; j < rules.length; j++) {
var rule = rules[j];
// check if there is a div.sourceCode rule
if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
var style = rule.style.cssText;
// check if color or background-color is set
if (rule.style.color === '' && rule.style.backgroundColor === '') continue;
// replace div.sourceCode by a pre.sourceCode rule
sheets[i].deleteRule(j);
sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
}
}
})();
</script>
<style type="text/css">@font-face{font-family:"Open Sans";font-style:normal;font-weight:400;src:local("Open Sans"),local("OpenSans"),url(data:application/font-woff;base64,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) format("woff")}@font-face{font-family:"Open Sans";font-style:normal;font-weight:700;src:local("Open Sans Bold"),local("OpenSans-Bold"),url(data:application/font-woff;base64,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) format("woff")}body{margin-top:26px;font-size:16px}*,:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:active,a:hover{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}blockquote{margin:0}img{max-width:100%;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}table{width:100%}::-moz-selection{background-color:rgba(200,200,200,.8);color:rgba(34,34,34,.8);text-shadow:none}::selection{background-color:rgba(200,200,200,.8);color:rgba(34,34,34,.8);text-shadow:none}.wrap{margin:0 auto}.all-caps{text-transform:uppercase}.image-left{float:none}@media only screen and (min-width:48em){.image-left{float:left}}.image-right{float:none}@media only screen and (min-width:48em){.image-right{float:right}}.unstyled-list{list-style:none;margin-left:0;padding-left:0}.unstyled-list li{list-style-type:none}.inline-list{list-style:none;margin-left:0;padding-left:0}.inline-list li{list-style-type:none;display:inline}a,b,blockquote,em,figure,h1,h2,header,i,img,input,p,q,span,strong{transition:all .2s ease}body{font-family:"Open Sans",Lato,Calibri,Arial,sans-serif;color:rgba(34,34,34,.8)}h1,h2,h3,h4,h5,h6{font-family:"Open Sans",Lato,Calibri,Arial,sans-serif}h1{font-size:28px;font-size:1.75rem}@media only screen and (min-width:48em){h1{font-size:32px;font-size:2rem}}a{text-decoration:none;color:rgba(13,69,198,.8)}a:visited{color:rgba(70,122,243,.8)}a:hover{color:rgba(7,36,102,.8)}a:focus{outline:thin dotted;color:rgba(7,36,102,.8)}a:active,a:hover{outline:0}blockquote{font-family:serif;font-style:italic;border-left:8px solid rgba(187,187,187,.8);padding-left:20px}@media only screen and (min-width:48em){blockquote{margin-left:-28px}}code,kbd,pre,samp,tt{font-family:monospace}li code,p code{line-height:1.5;white-space:nowrap;margin:0 2px;padding:0 5px;border:1px solid #e6e6e6;background-color:#f2f2f2;border-radius:3px}pre{font-size:.9em;line-height:1.5;overflow-x:auto}pre::-webkit-scrollbar{height:12px;background-color:#34362e;border-radius:0 0 4px 4px}pre::-webkit-scrollbar-thumb:horizontal{background-color:#6a6d5d;border-radius:4px}pre{padding:1em;margin-bottom:1.5em;line-height:1.5;color:#d0d0d0;color:#525252;border:1px solid #dbdbdb;background-color:#272822;background-color:#f8f8f8;border-radius:3px;position:relative;margin:1em 0}hr{display:block;margin:1em 0;padding:0;height:1px;border:0;border-top:1px solid #ccc;border-bottom:1px solid #fff}table{border-collapse:collapse;border-spacing:0;margin:20px auto}td,th{border-bottom:1px solid #bbb;text-align:left;padding:10px}th{background-color:#7887ab;color:#fff}tr:nth-child(odd){background-color:#ddd}tr:nth-child(even){background-color:#f5f5f5}body{margin:0;padding:0;width:100%;background-color:#e8e8e8}.entry:after,.entry:before,.hentry:after,.hentry:before{display:table;content:"";line-height:0}.entry:after,.hentry:after{clear:both}.entry h1,.entry h2,.entry h3,.entry h4,.entry h5,.entry h6,.entry li,.entry p,.hentry h1,.hentry h2,.hentry h3,.hentry h4,.hentry h5,.hentry h6,.hentry li,.hentry p{word-wrap:break-word}.entry-content{font-size:16px;font-size:1rem;line-height:1.625;margin-bottom:26px;margin-bottom:1.625rem}@media only screen and (min-width:48em){.entry-content{font-size:17.6px;font-size:1.1rem}}.entry-content li>a,.entry-content p>a{border-bottom:1px dotted rgba(214,225,252,.8)}.entry-content li>a:hover,.entry-content p>a:hover{border-bottom-style:solid}.entry-content li{margin-bottom:7px}.entry-content .footnotes li,.entry-content .footnotes ol,.entry-content .footnotes p{font-size:14px;font-size:.875rem;line-height:1.8571;margin-bottom:26px;margin-bottom:1.625rem}.entry-header{width:100%;overflow:hidden;position:relative}.header-title{text-align:center;margin:30px 0 0}.header-title h1{margin:10px 20px;font-weight:700;font-size:32px;font-size:2rem;color:rgba(85,85,85,.8)}@media only screen and (min-width:48em){.header-title h1{font-size:36px;font-size:2.25rem}}@media only screen and (min-width:62.5em){.header-title h1{font-size:40px;font-size:2.5rem}}.header-title h2{margin:0;font-size:24px;font-size:1.5rem;text-transform:uppercase;color:rgba(85,85,85,.8)}@media only screen and (min-width:48em){.header-title h2{font-size:28px;font-size:1.75rem}}.header-title h3{font-size:18px;font-size:1.125rem}@media only screen and (min-width:48em){.header-title h3{font-size:20px;font-size:1.25rem}}.header-title p{color:rgba(85,85,85,.8)}.header-title{position:absolute;top:0;display:table;margin-top:0;width:100%;height:300px;overflow:hidden}.header-title .header-title-wrap{display:table-cell;vertical-align:middle;margin:0 auto;text-align:center}.header-title h1{margin:10px;font-weight:700;margin:10px 60px;color:#fff;text-shadow:1px 1px 4px rgba(34,34,34,.6)}.header-title h1 a{color:#fff}.header-title h2{margin:0;color:#fff;text-transform:uppercase}@media only screen and (min-width:48em){.header-title h2 a{color:#fff}}.header-title h3{color:#fff}.header-title p{color:#fff}.entry-image{min-height:300px;background-image:linear-gradient(-90deg,#52adaa,#a752ad)}.entry-content{margin:10px 2px 10px 2px;padding:10px 15px;background-color:#fff;box-shadow:0 0 0 0,0 6px 12px rgba(0,0,0,.1);border-radius:3px}@media only screen and (min-width:48em){.entry-content{margin-top:10px;margin-left:10px;margin-right:10px;padding:20px 30px}}@media only screen and (min-width:62.5em){.entry-content{max-width:900px;margin:50px auto 30px auto;padding:50px 60px}.entry-content>p:first-child{font-size:20px;font-size:1.25rem;line-height:1.3;margin-bottom:26px;margin-bottom:1.625rem}}.toc{width:80%;margin:0 auto;padding:20px;border:solid 1px #bbb}.toc .toc-title{margin:0 0 16px;text-align:center;color:#666}.toc li,.toc ul{margin:0}@media only screen and (min-width:48em){.toc{width:60%}}
code span.kw { color: #a71d5d; font-weight: normal; }
code span.dt { color: #795da3; }
code span.dv { color: #0086b3; }
code span.bn { color: #0086b3; }
code span.fl { color: #0086b3; }
code span.ch { color: #4070a0; }
code span.st { color: #183691; }
code span.co { color: #969896; font-style: italic; }
code span.ot { color: #007020; }
</style>
</head>
<body>
<div class="entry-header">
<div class="entry-image">
</div><!-- /.entry-image -->
</div><!-- /.entry-header -->
<div id="main" role="main">
<article class="hentry">
<header class="header-title">
<div class="header-title-wrap">
<h1 class="title toc-ignore entry-title">Nested and Functional Programming For Case Control Analysis</h1>
<h3 class="author">Avery Richards</h3>
<h3 class="date">12/9/2021</h3>
</div><!-- /.header-title-wrap -->
</header>
<div class="entry-content">
<p>In epidemiology, outbreak investigations often rely on case control studies to test hypotheses around a probable source of contagion among many exposures. In this blog I walkthrough a tidy approach to case control analysis, using the a nesting and many models approach I picked up from the <a href="https://r4ds.had.co.nz/many-models.html">R for Data Science book</a> and the de-identified results of a CDC survey used during a classic investigation of an E. coli O157:H7 outbreak in the United States, circa 2009.</p>
<p>To begin, we will need to load and install a variety of libraries before we import our raw dataset.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="co"># installing and loading packages</span></a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="cf">if</span> (<span class="op">!</span><span class="kw">require</span>(<span class="st">"pacman"</span>)) <span class="kw">install.packages</span>(<span class="st">"pacman"</span>)</a>
<a class="sourceLine" id="cb1-3" data-line-number="3"></a>
<a class="sourceLine" id="cb1-4" data-line-number="4">pacman<span class="op">::</span><span class="kw">p_load</span>(</a>
<a class="sourceLine" id="cb1-5" data-line-number="5"> rio, <span class="co"># import data</span></a>
<a class="sourceLine" id="cb1-6" data-line-number="6"> tidyverse, janitor, <span class="co"># shape data</span></a>
<a class="sourceLine" id="cb1-7" data-line-number="7"> survival, <span class="co"># statistics </span></a>
<a class="sourceLine" id="cb1-8" data-line-number="8"> broom, <span class="co"># model evaluation</span></a>
<a class="sourceLine" id="cb1-9" data-line-number="9"> kableExtra, <span class="co">#table output</span></a>
<a class="sourceLine" id="cb1-10" data-line-number="10"> prettydoc <span class="co">#knitting</span></a>
<a class="sourceLine" id="cb1-11" data-line-number="11"> )</a>
<a class="sourceLine" id="cb1-12" data-line-number="12"></a>
<a class="sourceLine" id="cb1-13" data-line-number="13"><span class="co"># turn off scipen</span></a>
<a class="sourceLine" id="cb1-14" data-line-number="14"><span class="kw">options</span>(<span class="dt">scipen =</span> <span class="dv">999</span>)</a>
<a class="sourceLine" id="cb1-15" data-line-number="15"></a>
<a class="sourceLine" id="cb1-16" data-line-number="16"><span class="co">#import dataset, clean names</span></a>
<a class="sourceLine" id="cb1-17" data-line-number="17">ecoli_cc <-<span class="st"> </span><span class="kw">import</span>(<span class="st">"case_control_study_readacted.xlsx"</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb1-18" data-line-number="18"><span class="st"> </span>janitor<span class="op">::</span><span class="kw">clean_names</span>()</a></code></pre></div>
<p>Information from this study does not come to us in a pristine form. There are cases <em>(people who became ill)</em> observed in the dataset who do not have an equivalent control <em>(not sick person with identical exposure)</em>. Having identified the cases without controls, we can make a list of the <code>cdcid</code> values and filter those cases out of an updated line list, or <code>dataframe</code>, etc.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="co">## first we make list of cases without matched controls</span></a>
<a class="sourceLine" id="cb2-2" data-line-number="2">unmatched_cases <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"CDC001"</span>, <span class="st">"CDC006"</span>, <span class="st">"CDC009"</span>, <span class="st">"CDC012"</span>,</a>
<a class="sourceLine" id="cb2-3" data-line-number="3"> <span class="st">"CDC015"</span>, <span class="st">"CDC022"</span>, <span class="st">"CDC023"</span>, <span class="st">"CDC024"</span>,</a>
<a class="sourceLine" id="cb2-4" data-line-number="4"> <span class="st">"CDC037"</span>, <span class="st">"CDC043"</span>, <span class="st">"CDC045"</span>, <span class="st">"CDC046"</span>,</a>
<a class="sourceLine" id="cb2-5" data-line-number="5"> <span class="st">"CDC048"</span>, <span class="st">"CDC049"</span>, <span class="st">"CDC067"</span>, <span class="st">"CDC068"</span>)</a></code></pre></div>
<p>One situation has multiple controls assigned to a single case. We need to pluck that case from the data frame before we can continue.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="co"># some cases have multiple controls assigned.</span></a>
<a class="sourceLine" id="cb3-2" data-line-number="2">ecoli_cc <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="st"> </span><span class="kw">select</span>(cdcid, case, controlletter) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="kw">filter</span>(cdcid <span class="op">==</span><span class="st"> "CDC047"</span>)</a></code></pre></div>
<pre><code>## cdcid case controlletter
## 1 CDC047 1 <NA>
## 2 CDC047 0 A
## 3 CDC047 0 B</code></pre>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="co"># filter out unmatched cases. </span></a>
<a class="sourceLine" id="cb5-2" data-line-number="2">ecoli_cc_match <-<span class="st"> </span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="st"> </span>ecoli_cc <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="kw">filter</span>(<span class="op">!</span>(<span class="kw">str_detect</span>(cdcid, </a>
<a class="sourceLine" id="cb5-5" data-line-number="5"> <span class="kw">paste</span>(unmatched_cases, <span class="dt">collapse =</span> <span class="st">"|"</span>)))) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span>(cdcid <span class="op">==</span><span class="st"> "CDC047"</span> <span class="op">&</span><span class="st"> </span>cdccaseid <span class="op">==</span><span class="st"> "CDC047_B"</span>)) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-7" data-line-number="7"></a>
<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># create a strata number from cdcid strings </span></a>
<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">strata_num =</span> <span class="kw">as.numeric</span>(stringr<span class="op">::</span><span class="kw">str_extract_all</span>(cdcid,<span class="st">"(..$)"</span>)))</a>
<a class="sourceLine" id="cb5-10" data-line-number="10"> </a>
<a class="sourceLine" id="cb5-11" data-line-number="11"><span class="co"># count to verify the case numbers match, 36 strata </span></a>
<a class="sourceLine" id="cb5-12" data-line-number="12">ecoli_cc_match <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-13" data-line-number="13"><span class="st"> </span><span class="kw">distinct</span>(strata_num) <span class="op">%>%</span><span class="st"> </span><span class="kw">tally</span>()</a></code></pre></div>
<pre><code>## n
## 1 36</code></pre>
<p>After verifying the 1:1 matching pattern of cases and controls present in the dataset, we select the food exposure variables, renaming the more cryptic values into human friendly identifiers. We must also recode the <code>99</code> and <code>3</code> values sprinkled throughout all the exposure data. These represent missing data in the survey response as well as the always troubling, <em>“I’m not sure”</em> survey responses recieved from participants.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="co"># Select food exposure variables from CDC questionaire </span></a>
<a class="sourceLine" id="cb7-2" data-line-number="2">cc_exposures <-<span class="st"> </span>ecoli_cc_match <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="st"> </span><span class="kw">select</span>(strata_num, case, strawberry, </a>
<a class="sourceLine" id="cb7-4" data-line-number="4"> apples, rollup, gb, rawcd, milk, </a>
<a class="sourceLine" id="cb7-5" data-line-number="5"> smoothie, cchip, carrot, cucumber, </a>
<a class="sourceLine" id="cb7-6" data-line-number="6"> raspberry, watermelon, nocdfzndes, </a>
<a class="sourceLine" id="cb7-7" data-line-number="7"> shopsmoothie, cantaloupe, mandarin, </a>
<a class="sourceLine" id="cb7-8" data-line-number="8"> grapes, bologna, hotdog, bacon) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb7-9" data-line-number="9"><span class="st"> </span></a>
<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="st"> </span><span class="co"># rename variables</span></a>
<a class="sourceLine" id="cb7-11" data-line-number="11"><span class="st"> </span><span class="kw">rename</span>(<span class="dt">ground_beef =</span> gb, <span class="dt">raw_cookie_dough =</span> rawcd, </a>
<a class="sourceLine" id="cb7-12" data-line-number="12"> <span class="dt">fruit_rollup =</span> rollup,</a>
<a class="sourceLine" id="cb7-13" data-line-number="13"> <span class="dt">chocolate_chips =</span> cchip, </a>
<a class="sourceLine" id="cb7-14" data-line-number="14"> <span class="dt">frozen_dessert =</span> nocdfzndes, </a>
<a class="sourceLine" id="cb7-15" data-line-number="15"> <span class="dt">storebought_smoothie =</span> shopsmoothie)</a>
<a class="sourceLine" id="cb7-16" data-line-number="16"></a>
<a class="sourceLine" id="cb7-17" data-line-number="17"><span class="co"># Replace 99 and 3 to NA in dataframe</span></a>
<a class="sourceLine" id="cb7-18" data-line-number="18">cc_exposures <-<span class="st"> </span><span class="kw">map_df</span>(cc_exposures, <span class="op">~</span><span class="st"> </span><span class="kw">na_if</span>(.,<span class="st">"99"</span>)) </a>
<a class="sourceLine" id="cb7-19" data-line-number="19">cc_exposures <-<span class="st"> </span><span class="kw">map_df</span>(cc_exposures, <span class="op">~</span><span class="st"> </span><span class="kw">na_if</span>(.,<span class="st">"3"</span>))</a>
<a class="sourceLine" id="cb7-20" data-line-number="20"></a>
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co">#evaluate structure of new dataframe</span></a>
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="kw">str</span>(cc_exposures)</a></code></pre></div>
<pre><code>## tibble [72 × 22] (S3: tbl_df/tbl/data.frame)
## $ strata_num : num [1:72] 5 5 7 7 8 8 10 10 13 13 ...
## $ case : num [1:72] 1 0 1 0 1 0 1 0 1 0 ...
## $ strawberry : num [1:72] NA 1 1 0 0 0 NA 1 0 1 ...
## $ apples : num [1:72] NA 1 1 1 1 0 1 1 NA 0 ...
## $ fruit_rollup : num [1:72] 0 0 1 0 0 0 0 0 0 0 ...
## $ ground_beef : num [1:72] 1 0 1 1 1 0 0 0 1 1 ...
## $ raw_cookie_dough : num [1:72] 0 0 1 0 1 0 1 0 NA 0 ...
## $ milk : num [1:72] NA 0 NA 1 1 1 NA 0 1 0 ...
## $ smoothie : num [1:72] NA 0 NA 0 0 0 NA 1 0 1 ...
## $ chocolate_chips : num [1:72] NA 1 NA 1 1 0 NA 0 1 0 ...
## $ carrot : num [1:72] NA 1 NA 1 1 0 NA 1 0 0 ...
## $ cucumber : num [1:72] NA 1 NA 0 NA 0 NA 1 1 0 ...
## $ raspberry : num [1:72] NA 1 NA 0 NA 0 NA 0 0 0 ...
## $ watermelon : num [1:72] NA 0 NA 0 NA 0 NA 1 0 0 ...
## $ frozen_dessert : num [1:72] NA 1 NA 1 NA 1 NA 0 1 1 ...
## $ storebought_smoothie: num [1:72] NA 0 NA 0 0 0 NA 0 0 1 ...
## $ cantaloupe : num [1:72] NA 0 NA 0 NA 0 NA 0 0 0 ...
## $ mandarin : num [1:72] NA 0 NA 0 NA 0 NA 0 0 0 ...
## $ grapes : num [1:72] NA 1 NA 1 NA 0 NA 0 0 1 ...
## $ bologna : num [1:72] NA 0 NA 0 0 0 NA 0 1 0 ...
## $ hotdog : num [1:72] NA 0 NA 0 1 0 NA 0 1 1 ...
## $ bacon : num [1:72] NA 0 NA 0 0 0 NA 0 1 1 ...</code></pre>
<p>Here we are with a cleaned dataset of food exposures between case and control survey participants. Our next step would be to conduct a statistical model of some sort, generate odds ratios to determine an estimate of disease given exposure to the food items. There are 20 distinct food exposures to analyze. <em>So how can we conduct our tests in an organized and comparable way without repeating a stat function 20 times, wrestling and herding the outputs together someplace to observe?</em> The best answer I can think of is using the tidyverse <code>nest()</code> function with <code>purrr::map</code>.</p>
<p>To do that, we first we need to restructure the data so we can <code>group_by</code> the individual food exposures. Pivoting the wide exposure matrices into a long format works well.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1"><span class="co"># pivot longer to create category for each food. </span></a>
<a class="sourceLine" id="cb9-2" data-line-number="2">cc_pivot <-<span class="st"> </span>cc_exposures <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb9-3" data-line-number="3"><span class="st"> </span><span class="kw">pivot_longer</span>(<span class="dt">cols =</span> <span class="dv">3</span><span class="op">:</span><span class="dv">22</span>, <span class="dt">values_to =</span> <span class="st">"exposure"</span>,</a>
<a class="sourceLine" id="cb9-4" data-line-number="4"> <span class="dt">names_to =</span> <span class="st">"food"</span>)</a>
<a class="sourceLine" id="cb9-5" data-line-number="5"></a>
<a class="sourceLine" id="cb9-6" data-line-number="6"><span class="kw">dim</span>(cc_pivot)</a></code></pre></div>
<pre><code>## [1] 1440 4</code></pre>
<p>After pivoting, the dataframe contains identical data but is expressed differently: each observation, per exposure and case number, is treated as a row in the long format. <strong>72 (observations) * 20 (exposures) = 1440 rows</strong>. Now we can <code>group_by</code> the distinct exposures and <code>nest()</code> our data via those groups.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1"><span class="co"># group and nest by food category. </span></a>
<a class="sourceLine" id="cb11-2" data-line-number="2">cc_nested <-<span class="st"> </span>cc_pivot <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb11-3" data-line-number="3"><span class="st"> </span><span class="kw">group_by</span>(food) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb11-4" data-line-number="4"><span class="st"> </span><span class="kw">nest</span>()</a>
<a class="sourceLine" id="cb11-5" data-line-number="5"></a>
<a class="sourceLine" id="cb11-6" data-line-number="6"><span class="kw">head</span>(cc_nested)</a></code></pre></div>
<pre><code>## # A tibble: 6 x 2
## # Groups: food [6]
## food data
## <chr> <list>
## 1 strawberry <tibble [72 × 3]>
## 2 apples <tibble [72 × 3]>
## 3 fruit_rollup <tibble [72 × 3]>
## 4 ground_beef <tibble [72 × 3]>
## 5 raw_cookie_dough <tibble [72 × 3]>
## 6 milk <tibble [72 × 3]></code></pre>
<p>After pivoting, grouping, and nesting up our data we have a <em>dataframe of dataframes</em> sort of object, with each value in the <code>cc_nest$data</code> column being a <code>tibble</code> in itself that contains the exposure, case and control counts for each <code>food</code> item we categorized with <code>group_by</code>.</p>
<p>The next step is to put togther a function that runs a conditional logistic regression model in a way that we can operate on all our nested dataframes without repeating the process for each exposure.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="co"># function to map clogit model on dataframe. </span></a>
<a class="sourceLine" id="cb13-2" data-line-number="2">clogit_model <-<span class="st"> </span><span class="cf">function</span>(data){</a>
<a class="sourceLine" id="cb13-3" data-line-number="3"> survival<span class="op">::</span><span class="kw">clogit</span>(case <span class="op">~</span><span class="st"> </span>exposure <span class="op">+</span><span class="st"> </span></a>
<a class="sourceLine" id="cb13-4" data-line-number="4"><span class="st"> </span><span class="kw">strata</span>(strata_num), <span class="dt">data =</span> data) }</a></code></pre></div>
<p>Once the function is put together, we can use a <code>mutate</code> function to create a new column that <code>map</code>-s the <code>clogit</code> operation to all the nested dataframes in one call. But wait, not so fast…</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1"><span class="co"># map clogit onto nested "data" column to create model a new row of model outputs.</span></a>
<a class="sourceLine" id="cb14-2" data-line-number="2">cc_logit <-<span class="st"> </span>cc_nested <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb14-3" data-line-number="3"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">model =</span> <span class="kw">map</span>(data, clogit_model))</a></code></pre></div>
<pre><code>## Warning in fitter(X, Y, istrat, offset, init, control, weights = weights, : Ran
## out of iterations and did not converge</code></pre>
<p>…we are getting a warning from R after running the models.</p>
<p>Due to the structure and limited amount of exposure data, one of our 20 models was unable to converge and will give us wild OR outputs that do not seem plausable (<em>because they aren’t</em>). There is a variation of the conditional logistic model, an “exact” method that is unavailable to the R computing environment at this time. Having done our homework, we learn that the <a href="https://academic.oup.com/cid/article-pdf/54/4/511/1105929/cir831.pdf">original analysis of this data</a> used STATA software, which has an exact method available and was able to converge and return the following values below. So we can assemble those values to insert into our final tables.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" data-line-number="1"><span class="co"># outputs from a conditional exact model for the dataframe that did not converge. </span></a>
<a class="sourceLine" id="cb16-2" data-line-number="2">added_output <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">food =</span> <span class="st">"raw_cookie_dough"</span>, </a>
<a class="sourceLine" id="cb16-3" data-line-number="3"> <span class="dt">estimate =</span> <span class="fl">41.34</span>,</a>
<a class="sourceLine" id="cb16-4" data-line-number="4"> <span class="dt">p.value =</span> <span class="fl">.001</span>,</a>
<a class="sourceLine" id="cb16-5" data-line-number="5"> <span class="dt">conf.low =</span> <span class="fl">7.37</span>,</a>
<a class="sourceLine" id="cb16-6" data-line-number="6"> <span class="dt">conf.high =</span> <span class="ot">Inf</span>)</a></code></pre></div>
<p>Once we have made a new column with the 20 model outputs, one for each nested food exposure, (<em>including the erronious one in there</em>),we can extract outputs using the <code>broom</code> package. Exponentiation is key to getting correct odds ratios in a logistic regression model, and confidence intervals are also necessary to evalutate the strength of our estimates beyond p-value thresholds, so we are sure to include those arguments in the <code>broom::tidy</code> function that we <code>map</code> on our nested data frames.</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1"><span class="co"># summary of outputs.</span></a>
<a class="sourceLine" id="cb17-2" data-line-number="2">cc_summary <-<span class="st"> </span>cc_logit <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-3" data-line-number="3"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">outputs =</span> <span class="kw">map</span>(model, broom<span class="op">::</span>tidy, </a>
<a class="sourceLine" id="cb17-4" data-line-number="4"> <span class="co"># exponentiate and add confidence intervals. </span></a>
<a class="sourceLine" id="cb17-5" data-line-number="5"> <span class="dt">exponentiate =</span> <span class="ot">TRUE</span>, <span class="dt">conf.int =</span> <span class="ot">TRUE</span>)) </a>
<a class="sourceLine" id="cb17-6" data-line-number="6"></a>
<a class="sourceLine" id="cb17-7" data-line-number="7"><span class="kw">head</span>(cc_summary)</a></code></pre></div>
<pre><code>## # A tibble: 6 x 4
## # Groups: food [6]
## food data model outputs
## <chr> <list> <list> <list>
## 1 strawberry <tibble [72 × 3]> <clogit> <tibble [1 × 7]>
## 2 apples <tibble [72 × 3]> <clogit> <tibble [1 × 7]>
## 3 fruit_rollup <tibble [72 × 3]> <clogit> <tibble [1 × 7]>
## 4 ground_beef <tibble [72 × 3]> <clogit> <tibble [1 × 7]>
## 5 raw_cookie_dough <tibble [72 × 3]> <clogit> <tibble [1 × 7]>
## 6 milk <tibble [72 × 3]> <clogit> <tibble [1 × 7]></code></pre>
<p>We have the information from our models at the ready, we then <code>unnest()</code> our model objects, select the relevant outputs, and organize them in a table object, including replacing data from our model that was unable to converge with <code>survival::clogit()</code>.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" data-line-number="1"><span class="co"># table of output summary of models</span></a>
<a class="sourceLine" id="cb19-2" data-line-number="2">cc_summary <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-3" data-line-number="3"><span class="kw">unnest</span>(outputs) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-4" data-line-number="4"><span class="st"> </span><span class="co"># select the outputs relevant to our analysis</span></a>
<a class="sourceLine" id="cb19-5" data-line-number="5"><span class="st"> </span><span class="kw">select</span>(estimate, p.value, conf.low, conf.high) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-6" data-line-number="6"><span class="st"> </span><span class="co"># round these values for legibility sake</span></a>
<a class="sourceLine" id="cb19-7" data-line-number="7"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">p.value =</span> <span class="kw">round</span>(p.value, <span class="dv">3</span>),</a>
<a class="sourceLine" id="cb19-8" data-line-number="8"> <span class="dt">estimate =</span> <span class="kw">round</span>(estimate, <span class="dv">2</span>),</a>
<a class="sourceLine" id="cb19-9" data-line-number="9"> <span class="dt">conf.low =</span> <span class="kw">round</span>(conf.low, <span class="dv">3</span>),</a>
<a class="sourceLine" id="cb19-10" data-line-number="10"> <span class="dt">conf.high =</span> <span class="kw">round</span>(conf.high, <span class="dv">3</span>)) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-11" data-line-number="11"><span class="st"> </span><span class="co"># remove the row (or model) that was unable to converge</span></a>
<a class="sourceLine" id="cb19-12" data-line-number="12"><span class="st"> </span><span class="kw">filter</span>(food <span class="op">!=</span><span class="st"> "raw_cookie_dough"</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-13" data-line-number="13"><span class="st"> </span></a>
<a class="sourceLine" id="cb19-14" data-line-number="14"><span class="co"># add fixed outputs from published stata analysis </span></a>
<a class="sourceLine" id="cb19-15" data-line-number="15"><span class="st"> </span><span class="kw">bind_rows</span>(added_output) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-16" data-line-number="16"><span class="st"> </span><span class="kw">arrange</span>(estimate) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-17" data-line-number="17"><span class="st"> </span><span class="co"># create print quality table object for ease of viewing</span></a>
<a class="sourceLine" id="cb19-18" data-line-number="18"><span class="st"> </span><span class="kw">kbl</span>(<span class="dt">caption =</span> <span class="st">"Output of Conditional Logistic Regression Models"</span>, </a>
<a class="sourceLine" id="cb19-19" data-line-number="19"> <span class="dt">col.names =</span> <span class="kw">c</span>(<span class="st">"Food Consumed"</span>, </a>
<a class="sourceLine" id="cb19-20" data-line-number="20"> <span class="st">"Estimated OR"</span>,</a>
<a class="sourceLine" id="cb19-21" data-line-number="21"> <span class="st">"p-value"</span>, </a>
<a class="sourceLine" id="cb19-22" data-line-number="22"> <span class="st">"CI-low"</span>, </a>
<a class="sourceLine" id="cb19-23" data-line-number="23"> <span class="st">"CI-high"</span>)) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb19-24" data-line-number="24"><span class="st"> </span><span class="kw">kable_classic_2</span>(<span class="dt">full_width =</span> F) <span class="op">%>%</span><span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">2</span>, <span class="dt">bold =</span> T) </a></code></pre></div>
<table class=" lightable-classic-2" style="font-family: "Arial Narrow", "Source Sans Pro", sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
Output of Conditional Logistic Regression Models
</caption>
<thead>
<tr>
<th style="text-align:left;">
Food Consumed
</th>
<th style="text-align:right;">
Estimated OR
</th>
<th style="text-align:right;">
p-value
</th>
<th style="text-align:right;">
CI-low
</th>
<th style="text-align:right;">
CI-high
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
frozen_dessert
</td>
<td style="text-align:right;font-weight: bold;">
0.25
</td>
<td style="text-align:right;">
0.215
</td>
<td style="text-align:right;">
0.028
</td>
<td style="text-align:right;">
2.237
</td>
</tr>
<tr>
<td style="text-align:left;">
smoothie
</td>
<td style="text-align:right;font-weight: bold;">
0.50
</td>
<td style="text-align:right;">
0.423
</td>
<td style="text-align:right;">
0.092
</td>
<td style="text-align:right;">
2.730
</td>
</tr>
<tr>
<td style="text-align:left;">
mandarin
</td>
<td style="text-align:right;font-weight: bold;">
0.50
</td>
<td style="text-align:right;">
0.423
</td>
<td style="text-align:right;">
0.092
</td>
<td style="text-align:right;">
2.730
</td>
</tr>
<tr>
<td style="text-align:left;">
bologna
</td>
<td style="text-align:right;font-weight: bold;">
0.50
</td>
<td style="text-align:right;">
0.423
</td>
<td style="text-align:right;">
0.092
</td>
<td style="text-align:right;">
2.730
</td>
</tr>
<tr>
<td style="text-align:left;">
hotdog
</td>
<td style="text-align:right;font-weight: bold;">
0.50
</td>
<td style="text-align:right;">
0.327
</td>
<td style="text-align:right;">
0.125
</td>
<td style="text-align:right;">
1.999
</td>
</tr>
<tr>
<td style="text-align:left;">
carrot
</td>
<td style="text-align:right;font-weight: bold;">
0.67
</td>
<td style="text-align:right;">
0.530
</td>
<td style="text-align:right;">
0.188
</td>
<td style="text-align:right;">
2.362
</td>
</tr>
<tr>
<td style="text-align:left;">
cucumber
</td>
<td style="text-align:right;font-weight: bold;">
0.75
</td>
<td style="text-align:right;">
0.706
</td>
<td style="text-align:right;">
0.168
</td>
<td style="text-align:right;">
3.351
</td>
</tr>
<tr>
<td style="text-align:left;">
raspberry
</td>
<td style="text-align:right;font-weight: bold;">
1.00
</td>
<td style="text-align:right;">
1.000
</td>
<td style="text-align:right;">
0.141
</td>
<td style="text-align:right;">
7.099
</td>
</tr>
<tr>
<td style="text-align:left;">
storebought_smoothie
</td>
<td style="text-align:right;font-weight: bold;">
1.00
</td>
<td style="text-align:right;">
1.000
</td>
<td style="text-align:right;">
0.141
</td>
<td style="text-align:right;">
7.099
</td>
</tr>
<tr>
<td style="text-align:left;">
grapes
</td>
<td style="text-align:right;font-weight: bold;">
1.00
</td>
<td style="text-align:right;">
1.000
</td>
<td style="text-align:right;">
0.323
</td>
<td style="text-align:right;">
3.101
</td>
</tr>
<tr>
<td style="text-align:left;">
cantaloupe
</td>
<td style="text-align:right;font-weight: bold;">
1.33
</td>
<td style="text-align:right;">
0.706
</td>
<td style="text-align:right;">
0.298
</td>
<td style="text-align:right;">
5.957
</td>
</tr>
<tr>
<td style="text-align:left;">
apples
</td>
<td style="text-align:right;font-weight: bold;">
1.40
</td>
<td style="text-align:right;">
0.566
</td>
<td style="text-align:right;">
0.444
</td>
<td style="text-align:right;">
4.411
</td>
</tr>
<tr>
<td style="text-align:left;">
fruit_rollup
</td>
<td style="text-align:right;font-weight: bold;">
1.50
</td>
<td style="text-align:right;">
0.530
</td>
<td style="text-align:right;">
0.423
</td>
<td style="text-align:right;">
5.315
</td>
</tr>
<tr>
<td style="text-align:left;">
watermelon
</td>
<td style="text-align:right;font-weight: bold;">
1.50
</td>
<td style="text-align:right;">
0.657
</td>
<td style="text-align:right;">
0.251
</td>
<td style="text-align:right;">
8.977
</td>
</tr>
<tr>
<td style="text-align:left;">
bacon
</td>
<td style="text-align:right;font-weight: bold;">
1.67
</td>
<td style="text-align:right;">
0.484
</td>
<td style="text-align:right;">
0.398
</td>
<td style="text-align:right;">
6.974
</td>
</tr>
<tr>
<td style="text-align:left;">
milk
</td>
<td style="text-align:right;font-weight: bold;">
2.00
</td>
<td style="text-align:right;">
0.571
</td>
<td style="text-align:right;">
0.181
</td>
<td style="text-align:right;">
22.056
</td>
</tr>
<tr>
<td style="text-align:left;">
chocolate_chips
</td>
<td style="text-align:right;font-weight: bold;">
2.00
</td>
<td style="text-align:right;">
0.327
</td>
<td style="text-align:right;">
0.500
</td>
<td style="text-align:right;">
7.997
</td>
</tr>
<tr>
<td style="text-align:left;">
strawberry
</td>
<td style="text-align:right;font-weight: bold;">
2.20
</td>
<td style="text-align:right;">
0.144
</td>
<td style="text-align:right;">
0.764
</td>
<td style="text-align:right;">
6.332
</td>
</tr>
<tr>
<td style="text-align:left;">
ground_beef
</td>
<td style="text-align:right;font-weight: bold;">
3.50
</td>
<td style="text-align:right;">
0.118
</td>
<td style="text-align:right;">
0.727
</td>
<td style="text-align:right;">
16.848
</td>
</tr>
<tr>
<td style="text-align:left;">
raw_cookie_dough
</td>
<td style="text-align:right;font-weight: bold;">
41.34
</td>
<td style="text-align:right;">
0.001
</td>
<td style="text-align:right;">
7.370
</td>
<td style="text-align:right;">
Inf
</td>
</tr>
</tbody>
</table>
<p>It is also relevant to explore a count and proportion of cases and controls who consumed the food items. Here we can filter our pivoted data based on case or control status, grouping again on the food exposure, counting and creating a proportional measure.</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" data-line-number="1"><span class="co"># count and proportions of controls per food exposure</span></a>
<a class="sourceLine" id="cb20-2" data-line-number="2">controls_tab <-<span class="st"> </span>cc_pivot <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb20-3" data-line-number="3"><span class="st"> </span><span class="kw">filter</span>(case <span class="op">==</span><span class="st"> </span><span class="dv">0</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb20-4" data-line-number="4"><span class="st"> </span><span class="kw">group_by</span>(food) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb20-5" data-line-number="5"><span class="st"> </span><span class="kw">summarise</span>(<span class="dt">controls =</span> <span class="kw">sum</span>(exposure, <span class="dt">na.rm =</span> T)) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb20-6" data-line-number="6"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">controls_percent =</span> <span class="kw">round</span>(controls <span class="op">/</span><span class="st"> </span><span class="dv">36</span>, <span class="dv">2</span>))</a></code></pre></div>
<p>Now we have created a table for the controls, we repeat the process for cases and <code>inner_join</code> the tables together. Finally we add a similar wrapper for print quality table object.</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1"><span class="co"># join controls with cases </span></a>
<a class="sourceLine" id="cb21-2" data-line-number="2"> cc_pivot <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-3" data-line-number="3"><span class="st"> </span><span class="kw">filter</span>(case <span class="op">==</span><span class="st"> </span><span class="dv">1</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-4" data-line-number="4"><span class="st"> </span><span class="kw">group_by</span>(food) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-5" data-line-number="5"><span class="st"> </span><span class="kw">summarise</span>(<span class="dt">cases =</span> <span class="kw">sum</span>(exposure, <span class="dt">na.rm =</span> T)) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-6" data-line-number="6"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">cases_percent =</span> <span class="kw">round</span>(cases <span class="op">/</span><span class="st"> </span><span class="dv">36</span>, <span class="dv">2</span>)) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-7" data-line-number="7"><span class="st"> </span><span class="kw">inner_join</span>(controls_tab, <span class="dt">by =</span> <span class="st">"food"</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-8" data-line-number="8"><span class="st"> </span><span class="kw">arrange</span>(cases_percent) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-9" data-line-number="9"><span class="st"> </span><span class="kw">kbl</span>(<span class="dt">caption =</span> <span class="st">"Counts and Percentages of Exposed Cases"</span>,</a>
<a class="sourceLine" id="cb21-10" data-line-number="10"> <span class="dt">col.names =</span> <span class="kw">c</span>(<span class="st">"Food Consumed"</span>,</a>
<a class="sourceLine" id="cb21-11" data-line-number="11"> <span class="st">"Cases"</span>,</a>
<a class="sourceLine" id="cb21-12" data-line-number="12"> <span class="st">"No."</span>, </a>
<a class="sourceLine" id="cb21-13" data-line-number="13"> <span class="st">"Controls"</span>, </a>
<a class="sourceLine" id="cb21-14" data-line-number="14"> <span class="st">"No."</span>)) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-15" data-line-number="15"><span class="st"> </span><span class="kw">kable_classic_2</span>(<span class="dt">full_width =</span> F)</a></code></pre></div>
<table class=" lightable-classic-2" style="font-family: "Arial Narrow", "Source Sans Pro", sans-serif; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
Counts and Percentages of Exposed Cases
</caption>
<thead>
<tr>
<th style="text-align:left;">
Food Consumed
</th>
<th style="text-align:right;">