Welcome
visR - A package for Effective Visualizations in Pharma
This tutorial will introduce you to the basic usage of visR, an R
package for effective visual communication. The package presents an
easy-to-use interface for visualizations relevant to the clinical
development process, implementing several best practices. It was
developed as part of the openpharma
initiative, an effort for open-source cross-industry collaboration.
Further reading
- The graphical principles that
visRaims to implement [graphicsprinciples.github.io] - visR Documentation [openpharma.github.io/visR]
- visR @ GitHub [github.com/openpharma/visR]
- visR @ CRAN [cran.r-project.org/web/packages/visR]
- openpharma overview [openpharma.github.io]
Package architecture
The visR package
implements a simple-to-use interface by adhering to consistent naming
conventions.
Overview
In principle, extimate_XXX functions allow to estimate
statistical models from the data (e.g. estimate_KM
estimates a survival model), get_XXX functions get all
kinds of summary statistics (e.g. get_tableone or
get_pvalue), render and visr
display plots and tables, and add_XXX functions add
specific information to a plot or table (e.g. add_CI adds
confidence intervals to a plot.)
Outline
This tutorial will walk you through a basic survival analysis
show-casing some principles and features of visR.
- We will start off by introducing a data set provided by the
COVID-19 India Org Data Operations Groupthat we will use throughout the tutorial. - Then we will introduce a so-called
table 1which gives an overview of the patient population. - We will
estimate_a survival model and introduce functions toget_summary statistics. - We will plot the survival model and show how you can
add_additional information to it. - We will integrate summary statistics into the plots.
The study and the data
Risk and Survival Analysis from COVID Outbreak Data :Lessons from India
The goal of this paper was to assess the mortality in India due to
COVID-19. To understand the survival of COVID-19 patients, a
time-to-event analysis was performed based on Kaplan-Meier estimates
using gender and age group (separately and combined) as strata.
According to the authors (Bankar et al.,
2021), this was probably the biggest retrospective-cohort survival
analysis conducted in India, according to the authors. This paper used
publicly available data COVID-19 India Org Data Operations Group.
The below figure roughly described trends and distributions of the
patient population.
The data
Patients who were admitted to Karnakata, South India, were recruited. The final dataset consists of 26,741 patients. Age and gender of the patient, the number of days in the hospital per stay, the status of the patient (1=censored/alive, 2=dead) and the age category (<18 yr, 18 yr – 60 yr, >60 yr) were collected.
# First, we set the default ggplot2 theme
theme_set(theme_bw())
# Then, we directly load the data from their GitHub repository
data = read.csv("https://raw.githubusercontent.com/vntkumar8/covid-survival/main/data/final.csv")
data Age Sex Stay Status Age_Cat
1 46.00 M 2 2 2
2 90.00 M 7 2 3
3 72.00 F 1 2 3
4 54.00 M 0 2 2
5 51.00 F 4 2 2
6 65.00 M 0 2 3
7 56.00 M 0 2 2
8 39.00 M 1 2 2
9 66.00 F 0 2 3
10 53.00 F 10 2 2
11 62.00 M 0 2 3
12 55.00 M 2 2 2
13 70.00 M 0 2 3
14 46.00 M 1 2 2
15 60.00 F 5 2 3
16 90.00 M 0 2 3
17 45.00 M 3 2 2
18 38.00 F 1 2 2
19 70.00 M 0 2 3
20 70.00 M 13 2 3
21 47.00 F 6 2 2
22 85.00 F 5 2 3
23 81.00 M 3 2 3
24 32.00 M 0 2 2
25 67.00 M 1 2 3
26 40.00 M 1 2 2
27 85.00 F 5 2 3
28 59.00 F 14 2 2
29 54.00 F 6 2 2
30 59.00 M 3 2 2
31 73.00 M 2 2 3
32 78.00 M 1 2 3
33 55.00 M 1 2 2
34 43.00 M 0 2 2
35 28.00 M 1 2 2
36 56.00 M 1 2 2
37 53.00 M 0 2 2
38 60.00 M 3 2 3
39 70.00 F 0 2 3
40 50.00 F 0 2 2
41 68.00 M 0 2 3
42 61.00 M 1 2 3
43 68.00 F 2 2 3
44 87.00 M 0 2 3
45 80.00 F 3 2 3
46 60.00 F 0 2 3
47 38.00 M 3 2 2
48 43.00 F 7 2 2
49 42.00 M 4 2 2
50 75.00 F 2 2 3
51 57.00 M 0 2 2
52 71.00 M 0 2 3
53 55.00 M 1 2 2
54 65.00 F 0 2 3
55 45.00 M 0 2 2
56 63.00 F 3 2 3
57 66.00 F 9 2 3
58 83.00 M 16 2 3
59 65.00 M 10 2 3
60 70.00 M 8 2 3
61 72.00 M 3 2 3
62 74.00 F 5 2 3
63 95.00 F 2 2 3
64 50.00 M 1 2 2
65 75.00 M 0 2 3
66 73.00 M 2 2 3
67 65.00 M 0 2 3
68 73.00 F 0 2 3
69 71.00 F 18 2 3
70 60.00 F 12 2 3
71 66.00 M 4 2 3
72 62.00 M 7 2 3
73 55.00 M 9 2 2
74 55.00 M 6 2 2
75 48.00 F 2 2 2
76 70.00 M 1 2 3
77 76.00 F 1 2 3
78 40.00 M 0 2 2
79 65.00 M 5 2 3
80 50.00 M 1 2 2
81 57.00 M 0 2 2
82 65.00 M 1 2 3
83 31.00 M 1 2 2
84 51.00 F 2 2 2
85 67.00 M 15 2 3
86 80.00 F 1 2 3
87 60.00 M 5 2 3
88 60.00 F 4 2 3
89 59.00 M 4 2 2
90 48.00 M 1 2 2
91 60.00 M 1 2 3
92 50.00 M 1 2 2
93 70.00 M 3 2 3
94 63.00 F 0 2 3
95 40.00 M 0 2 2
96 49.00 F 1 2 2
97 60.00 F 0 2 3
98 56.00 M 2 2 2
99 31.00 M 0 2 2
100 41.00 M 1 2 2
101 56.00 M 0 2 2
102 43.00 M 0 2 2
103 42.00 F 1 2 2
104 31.00 F 1 2 2
105 75.00 F 6 2 3
106 85.00 F 7 2 3
107 83.00 M 6 2 3
108 44.00 M 6 2 2
109 63.00 M 3 2 3
110 55.00 F 2 2 2
111 45.00 M 2 2 2
112 60.00 F 6 2 3
113 63.00 F 1 2 3
114 64.00 F 0 2 3
115 50.00 M 0 2 2
116 50.00 F 1 2 2
117 52.00 M 4 2 2
118 69.00 M 0 2 3
119 32.00 M 0 2 2
120 36.00 M 0 2 2
121 56.00 M 1 2 2
122 58.00 F 2 2 2
123 58.00 F 0 2 2
124 50.00 F 1 2 2
125 50.00 M 4 2 2
126 72.00 M 0 2 3
127 72.00 M 0 2 3
128 80.00 M 3 2 3
129 63.00 M 11 2 3
130 30.00 M 1 2 2
131 66.00 F 12 2 3
132 75.00 M 0 2 3
133 43.00 M 7 2 2
134 79.00 M 8 2 3
135 76.00 M 6 2 3
136 48.00 M 4 2 2
137 75.00 F 3 2 3
138 48.00 M 13 2 2
139 74.00 M 7 2 3
140 61.00 M 1 2 3
141 50.00 M 2 2 2
142 70.00 F 2 2 3
143 65.00 M 1 2 3
144 60.00 M 0 2 3
145 36.00 M 1 2 2
146 55.00 M 0 2 2
147 45.00 M 1 2 2
148 68.00 F 5 2 3
149 31.00 M 0 2 2
150 50.00 F 8 2 2
151 58.00 M 6 2 2
152 73.00 M 14 2 3
153 56.00 M 0 2 2
154 55.00 M 0 2 2
155 63.00 M 0 2 3
156 52.00 M 6 2 2
157 54.00 M 4 2 2
158 87.00 M 3 2 3
159 60.00 M 0 2 3
160 58.00 M 0 2 2
161 65.00 F 2 2 3
162 75.00 F 5 2 3
163 73.00 F 1 2 3
164 80.00 M 8 2 3
165 25.00 F 5 2 2
166 62.00 M 1 2 3
167 70.00 F 1 2 3
168 75.00 M 1 2 3
169 46.00 F 1 2 2
170 48.00 M 0 2 2
171 80.00 F 3 2 3
172 72.00 F 1 2 3
173 66.00 M 2 2 3
174 51.00 M 0 2 2
175 84.00 M 2 2 3
176 82.00 M 6 2 3
177 29.00 M 10 2 2
178 45.00 M 0 2 2
179 57.00 M 4 2 2
180 63.00 M 1 2 3
181 68.00 M 5 2 3
182 65.00 M 4 2 3
183 66.00 F 0 2 3
184 50.00 F 1 2 2
185 52.00 F 7 2 2
186 42.00 F 3 2 2
187 60.00 F 0 2 3
188 39.00 M 0 2 2
189 56.00 M 2 2 2
190 66.00 F 3 2 3
191 53.00 M 4 2 2
192 78.00 F 3 2 3
193 62.00 M 0 2 3
194 60.00 M 1 2 3
195 68.00 F 1 2 3
196 73.00 M 1 2 3
197 47.00 M 3 2 2
198 84.00 M 1 2 3
199 32.00 M 2 2 2
200 80.00 M 0 2 3
201 60.00 F 0 2 3
202 67.00 M 0 2 3
203 65.00 F 0 2 3
204 45.00 F 2 2 2
205 75.00 M 1 2 3
206 73.00 M 1 2 3
207 55.00 M 0 2 2
208 24.00 F 0 2 2
209 70.00 F 2 2 3
210 50.00 M 0 2 2
211 70.00 F 1 2 3
212 31.00 M 1 2 2
213 57.00 M 14 2 2
214 70.00 M 4 2 3
215 55.00 F 17 2 2
216 50.00 F 9 2 2
217 64.00 M 5 2 3
218 58.00 M 0 2 2
219 73.00 M 0 2 3
220 95.00 M 2 2 3
221 48.00 M 1 2 2
222 63.00 M 3 2 3
223 64.00 F 27 2 3
224 54.00 M 8 2 2
225 48.00 M 1 2 2
226 71.00 M 8 2 3
227 62.00 M 0 2 3
228 68.00 M 3 2 3
229 58.00 F 2 2 2
230 77.00 M 1 2 3
231 69.00 M 1 2 3
232 64.00 M 0 2 3
233 50.00 F 5 2 2
234 53.00 M 1 2 2
235 48.00 M 0 2 2
236 78.00 M 0 2 3
237 67.00 F 4 2 3
238 66.00 M 0 2 3
239 70.00 M 0 2 3
240 25.00 F 2 2 2
241 45.00 M 0 2 2
242 54.00 M 2 2 2
243 63.00 M 1 2 3
244 45.00 M 1 2 2
245 60.00 F 0 2 3
246 65.00 M 6 2 3
247 32.00 F 1 2 2
248 59.00 M 0 2 2
249 60.00 M 0 2 3
250 65.00 M 0 2 3
251 44.00 M 11 2 2
252 58.00 M 12 2 2
253 54.00 M 7 2 2
254 79.00 M 4 2 3
255 62.00 M 6 2 3
256 59.00 F 7 2 2
257 59.00 M 12 2 2
258 74.00 M 7 2 3
259 68.00 M 7 2 3
260 73.00 M 3 2 3
261 52.00 M 6 2 2
262 49.00 M 4 2 2
263 60.00 F 3 2 3
264 50.00 M 3 2 2
265 64.00 M 2 2 3
266 78.00 M 1 2 3
267 60.00 M 0 2 3
268 80.00 M 0 2 3
269 90.00 F 0 2 3
270 75.00 M 0 2 3
271 70.00 M 0 2 3
272 22.00 M 1 2 2
273 65.00 F 0 2 3
274 65.00 F 1 2 3
275 40.00 M 0 2 2
276 46.00 M 2 2 2
277 75.00 M 2 2 3
278 38.00 F 0 2 2
279 58.00 M 0 2 2
280 30.00 M 0 2 2
281 80.00 M 2 2 3
282 48.00 M 2 2 2
283 68.00 M 1 2 3
284 75.00 F 2 2 3
285 47.00 M 1 2 2
286 52.00 M 0 2 2
287 55.00 F 0 2 2
288 55.00 M 1 2 2
289 71.00 M 0 2 3
290 40.00 M 2 2 2
291 68.00 F 1 2 3
292 70.00 M 0 2 3
293 70.00 F 0 2 3
294 65.00 M 1 2 3
295 90.00 F 16 2 3
296 42.00 M 4 2 2
297 73.00 M 11 2 3
298 56.00 F 6 2 2
299 37.00 M 10 2 2
300 44.00 M 6 2 2
301 34.00 M 10 2 2
302 60.00 M 7 2 3
303 70.00 M 0 2 3
304 67.00 F 12 2 3
305 60.00 M 5 2 3
306 68.00 M 2 2 3
307 65.00 M 1 2 3
308 55.00 M 8 2 2
309 58.00 F 8 2 2
310 58.00 F 11 2 2
311 70.00 M 5 2 3
312 36.00 M 6 2 2
313 14.00 M 2 2 1
314 50.00 F 10 2 2
315 67.00 M 7 2 3
316 85.00 M 8 2 3
317 65.00 M 1 2 3
318 58.00 M 0 2 2
319 61.00 F 1 2 3
320 29.00 M 4 2 2
321 57.00 M 7 2 2
322 60.00 F 6 2 3
323 35.00 M 13 2 2
324 69.00 M 3 2 3
325 47.00 M 2 2 2
326 34.00 M 5 2 2
327 65.00 M 6 2 3
328 65.00 M 5 2 3
329 65.00 M 13 2 3
330 56.00 M 0 2 2
331 60.00 F 3 2 3
332 70.00 F 8 2 3
333 40.00 M 6 2 2
334 50.00 M 1 2 2
335 80.00 M 3 2 3
336 54.00 M 1 2 2
337 52.00 F 0 2 2
338 48.00 F 1 2 2
339 58.00 F 0 2 2
340 55.00 F 0 2 2
341 48.00 F 1 2 2
342 85.00 F 0 2 3
343 62.00 M 0 2 3
344 65.00 M 1 2 3
345 54.00 F 1 2 2
346 70.00 M 1 2 3
347 65.00 M 0 2 3
348 45.00 F 12 2 2
349 55.00 M 6 2 2
350 88.00 M 2 2 3
351 42.00 M 0 2 2
352 65.00 M 5 2 3
353 77.00 M 3 2 3
354 67.00 M 4 2 3
355 50.00 F 3 2 2
356 78.00 F 0 2 3
357 76.00 F 3 2 3
358 64.00 M 0 2 3
359 52.00 M 0 2 2
360 53.00 M 1 2 2
361 73.00 M 1 2 3
362 54.00 M 3 2 2
363 85.00 M 2 2 3
364 53.00 M 4 2 2
365 59.00 F 15 2 2
366 75.00 M 9 2 3
367 32.00 F 2 2 2
368 70.00 F 9 2 3
369 51.00 M 0 2 2
370 37.00 M 6 2 2
371 71.00 M 4 2 3
372 51.00 M 0 2 2
373 65.00 M 5 2 3
374 65.00 M 2 2 3
375 52.00 M 0 2 2
376 45.00 M 0 2 2
377 90.00 M 1 2 3
378 72.00 M 8 2 3
379 48.00 F 1 2 2
380 65.00 M 0 2 3
381 56.00 M 0 2 2
382 69.00 F 0 2 3
383 31.00 F 1 2 2
384 51.00 M 4 2 2
385 63.00 M 4 2 3
386 55.00 F 0 2 2
387 55.00 M 0 2 2
388 48.00 M 10 2 2
389 45.00 M 3 2 2
390 47.00 M 9 2 2
391 70.00 M 1 2 3
392 42.00 M 7 2 2
393 72.00 M 1 2 3
394 62.00 M 0 2 3
395 68.00 F 0 2 3
396 55.00 F 3 2 2
397 44.00 F 4 2 2
398 66.00 M 6 2 3
399 83.00 M 6 2 3
400 75.00 M 7 2 3
401 62.00 M 8 2 3
402 58.00 F 2 2 2
403 52.00 M 3 2 2
404 40.00 M 3 2 2
405 57.00 M 4 2 2
406 61.00 M 5 2 3
407 61.00 M 4 2 3
408 72.00 M 2 2 3
409 71.00 F 1 2 3
410 55.00 M 1 2 2
411 42.00 M 1 2 2
412 60.00 M 33 2 3
413 35.00 M 1 2 2
414 52.00 M 0 2 2
415 50.00 F 1 2 2
416 57.00 M 1 2 2
417 65.00 F 0 2 3
418 72.00 M 1 2 3
419 49.00 M 1 2 2
420 48.00 M 0 2 2
421 82.00 M 0 2 3
422 65.00 M 14 2 3
423 30.00 M 14 2 2
424 66.00 M 0 2 3
425 58.00 M 2 2 2
426 58.00 F 0 2 2
427 68.00 M 2 2 3
428 63.00 F 3 2 3
429 68.00 M 0 2 3
430 76.00 M 8 2 3
431 62.00 M 5 2 3
432 65.00 F 2 2 3
433 60.00 F 0 2 3
434 83.00 M 1 2 3
435 59.00 M 1 2 2
436 59.00 F 1 2 2
437 58.00 M 0 2 2
438 57.00 F 0 2 2
439 67.00 M 0 2 3
440 54.00 M 4 2 2
441 70.00 F 1 2 3
442 56.00 F 1 2 2
443 49.00 F 0 2 2
444 73.00 M 0 2 3
445 88.00 M 0 2 3
446 54.00 M 1 2 2
447 61.00 M 0 2 3
448 65.00 M 0 2 3
449 79.00 M 1 2 3
450 51.00 F 0 2 2
451 73.00 F 5 2 3
452 40.00 M 8 2 2
453 55.00 F 1 2 2
454 80.00 M 6 2 3
455 51.00 M 4 2 2
456 75.00 M 6 2 3
457 78.00 F 5 2 3
458 84.00 M 6 2 3
459 59.00 M 2 2 2
460 68.00 M 5 2 3
461 73.00 M 1 2 3
462 65.00 M 2 2 3
463 67.00 M 0 2 3
464 55.00 M 2 2 2
465 51.00 F 1 2 2
466 65.00 M 3 2 3
467 58.00 F 0 2 2
468 31.00 M 14 2 2
469 72.00 M 4 2 3
470 65.00 F 0 2 3
471 55.00 M 0 2 2
472 45.00 F 6 2 2
473 58.00 F 1 2 2
474 47.00 M 1 2 2
475 65.00 M 0 2 3
476 54.00 M 1 2 2
477 58.00 M 1 2 2
478 66.00 M 2 2 3
479 55.00 M 11 2 2
480 62.00 M 0 2 3
481 12.00 F 1 2 1
482 46.00 F 0 2 2
483 26.00 F 0 2 2
484 57.00 F 1 2 2
485 62.00 F 5 2 3
486 70.00 M 4 2 3
487 60.00 F 0 2 3
488 68.00 M 1 2 3
489 50.00 M 0 2 2
490 71.00 M 0 2 3
491 58.00 M 1 2 2
492 46.00 M 6 2 2
493 0.00 F 0 2 1
494 70.00 M 11 2 3
495 65.00 M 3 2 3
496 17.00 F 8 2 1
497 45.00 F 13 2 2
498 64.00 M 11 2 3
499 61.00 M 7 2 3
500 59.00 F 2 2 2
501 63.00 M 0 2 3
502 36.00 M 14 2 2
503 60.00 M 3 2 3
504 49.00 F 2 2 2
505 60.00 M 11 2 3
506 45.00 M 3 2 2
507 71.00 F 2 2 3
508 42.00 F 6 2 2
509 84.00 M 0 2 3
510 57.00 M 1 2 2
511 60.00 M 9 2 3
512 33.00 M 23 2 2
513 75.00 F 1 2 3
514 50.00 F 1 2 2
515 60.00 F 6 2 3
516 45.00 F 7 2 2
517 70.00 F 0 2 3
518 60.00 M 9 2 3
519 69.00 M 13 2 3
520 82.00 M 10 2 3
521 68.00 M 0 2 3
522 49.00 M 1 2 2
523 56.00 M 4 2 2
524 33.00 F 2 2 2
525 80.00 F 3 2 3
526 74.00 M 1 2 3
527 65.00 M 2 2 3
528 48.00 M 0 2 2
529 52.00 F 11 2 2
530 75.00 F 0 2 3
531 26.00 M 3 2 2
532 57.00 M 5 2 2
533 62.00 M 7 2 3
534 65.00 M 3 2 3
535 71.00 M 1 2 3
536 56.00 M 1 2 2
537 76.00 F 1 2 3
538 49.00 F 0 2 2
539 57.00 M 1 2 2
540 79.00 M 3 2 3
541 59.00 F 0 2 2
542 54.00 F 0 2 2
543 65.00 F 0 2 3
544 48.00 M 1 2 2
545 70.00 F 8 2 3
546 72.00 M 6 2 3
547 78.00 M 4 2 3
548 53.00 M 2 2 2
549 45.00 F 1 2 2
550 52.00 M 3 2 2
551 50.00 M 2 2 2
552 42.00 M 1 2 2
553 68.00 M 0 2 3
554 58.00 M 3 2 2
555 47.00 M 0 2 2
556 55.00 F 1 2 2
557 60.00 F 1 2 3
558 58.00 F 0 2 2
559 42.00 F 0 2 2
560 60.00 M 1 2 3
561 80.00 F 0 2 3
562 57.00 M 2 2 2
563 45.00 F 10 2 2
564 55.00 F 3 2 2
565 61.00 F 1 2 3
566 52.00 M 4 2 2
567 57.00 M 11 2 2
568 35.00 M 0 2 2
569 36.00 F 1 2 2
570 50.00 F 3 2 2
571 55.00 F 17 2 2
572 48.00 M 0 2 2
573 52.00 M 2 2 2
574 36.00 M 0 2 2
575 48.00 M 0 2 2
576 59.00 M 1 2 2
577 46.00 F 8 2 2
578 45.00 M 0 2 2
579 72.00 F 1 2 3
580 84.00 M 2 2 3
581 29.00 M 10 2 2
582 45.00 M 0 2 2
583 57.00 M 4 2 2
584 65.00 M 3 2 3
585 50.00 F 1 2 2
586 52.00 F 7 2 2
587 42.00 F 3 2 2
588 39.00 M 0 2 2
589 40.00 M 0 2 2
590 66.00 F 3 2 3
591 78.00 F 5 2 3
592 62.00 M 0 2 3
593 55.00 F 1 2 2
594 36.00 F 1 2 2
595 80.00 F 3 2 3
596 63.00 M 2 2 3
597 75.00 F 2 2 3
598 65.00 F 9 2 3
599 48.00 F 1 2 2
600 60.00 F 0 2 3
601 90.00 F 6 2 3
602 80.00 M 6 2 3
603 77.00 M 4 2 3
604 70.00 M 4 2 3
605 62.00 M 2 2 3
606 59.00 M 0 2 2
607 65.00 M 0 2 3
608 63.00 M 1 2 3
609 79.00 M 3 2 3
610 50.00 M 0 2 2
611 65.00 M 4 2 3
612 50.00 M 0 2 2
613 63.00 M 0 2 3
614 52.00 M 0 2 2
615 70.00 M 0 2 3
616 45.00 M 4 2 2
617 20.00 M 3 2 2
618 86.00 F 0 2 3
619 85.00 F 6 2 3
620 39.00 M 6 2 2
621 74.00 M 2 2 3
622 70.00 M 0 2 3
623 51.00 M 1 2 2
624 68.00 M 0 2 3
625 76.00 M 1 2 3
626 52.00 M 2 2 2
627 72.00 M 0 2 3
628 45.00 M 2 2 2
629 45.00 M 1 2 2
630 82.00 F 0 2 3
631 58.00 F 0 2 2
632 69.00 M 2 2 3
633 68.00 M 2 2 3
634 63.00 M 0 2 3
635 38.00 M 2 2 2
636 45.00 F 16 2 2
637 59.00 M 1 2 2
638 47.00 M 0 2 2
639 40.00 M 1 2 2
640 29.00 F 0 2 2
641 50.00 F 0 2 2
642 59.00 M 2 2 2
643 74.00 M 1 2 3
644 58.00 F 1 2 2
645 47.00 M 1 2 2
646 67.00 M 1 2 3
647 73.00 M 2 2 3
648 42.00 F 2 2 2
649 23.00 F 2 2 2
650 55.00 M 0 2 2
651 35.00 M 1 2 2
652 55.00 M 6 2 2
653 64.00 M 7 2 3
654 76.00 F 0 2 3
655 84.00 M 1 2 3
656 54.00 M 3 2 2
657 48.00 M 0 2 2
658 60.00 M 5 2 3
659 60.00 F 0 2 3
660 75.00 F 0 2 3
661 68.00 M 1 2 3
662 53.00 M 2 2 2
663 85.00 M 0 2 3
664 60.00 M 3 2 3
665 55.00 F 0 2 2
666 67.00 F 0 2 3
667 49.00 F 0 2 2
668 68.00 F 2 2 3
669 80.00 F 1 2 3
670 70.00 M 1 2 3
671 35.00 F 1 2 2
672 40.00 F 2 2 2
673 70.00 F 0 2 3
674 49.00 M 0 2 2
675 48.00 M 0 2 2
676 48.00 F 1 2 2
677 42.00 F 3 2 2
678 75.00 F 0 2 3
679 72.00 M 2 2 3
680 52.00 M 4 2 2
681 60.00 M 6 2 3
682 61.00 M 5 2 3
683 50.00 M 0 2 2
684 70.00 F 2 2 3
685 49.00 M 0 2 2
686 38.00 M 0 2 2
687 34.00 F 1 2 2
688 50.00 M 1 2 2
689 41.00 M 1 2 2
690 76.00 M 1 2 3
691 40.00 M 0 2 2
692 55.00 M 1 2 2
693 57.00 M 2 2 2
694 53.00 M 7 2 2
695 61.00 M 0 2 3
696 60.00 M 0 2 3
697 90.00 M 3 2 3
698 41.00 M 1 2 2
699 68.00 M 6 2 3
700 40.00 F 0 2 2
701 50.00 F 2 2 2
702 70.00 M 2 2 3
703 75.00 F 3 2 3
704 70.00 M 22 2 3
705 49.00 F 4 2 2
706 70.00 M 0 2 3
707 56.00 F 2 2 2
708 56.00 M 1 2 2
709 52.00 M 2 2 2
710 76.00 F 0 2 3
711 51.00 F 2 2 2
712 65.00 M 0 2 3
713 85.00 F 3 2 3
714 48.00 M 3 2 2
715 68.00 F 0 2 3
716 60.00 M 0 2 3
717 63.00 F 1 2 3
718 60.00 M 0 2 3
719 71.00 M 0 2 3
720 69.00 M 0 2 3
721 70.00 F 1 2 3
722 58.00 M 0 2 2
723 46.00 M 9 2 2
724 78.00 M 4 2 3
725 56.00 M 4 2 2
726 60.00 F 15 2 3
727 41.00 M 1 2 2
728 66.00 M 6 2 3
729 60.00 F 0 2 3
730 70.00 M 11 2 3
731 52.00 F 13 2 2
732 48.00 M 6 2 2
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1900 69.00 M 20 2 3
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1911 60.00 M 0 2 3
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1914 50.00 F 2 2 2
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1918 62.00 F 2 2 3
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1920 60.00 F 1 2 3
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1930 65.00 M 0 2 3
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1932 55.00 M 0 2 2
1933 72.00 F 2 2 3
1934 50.00 M 7 2 2
1935 47.00 M 0 2 2
1936 47.00 M 2 2 2
1937 54.00 M 2 2 2
1938 65.00 F 0 2 3
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1940 60.00 M 0 2 3
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1945 65.00 M 3 2 3
1946 54.00 M 2 2 2
1947 62.00 M 8 2 3
1948 45.00 M 2 2 2
1949 66.00 M 11 2 3
1950 59.00 M 5 2 2
1951 58.00 M 2 2 2
1952 60.00 M 25 2 3
1953 58.00 M 5 2 2
1954 60.00 F 3 2 3
1955 75.00 M 3 2 3
1956 50.00 F 9 2 2
1957 60.00 M 9 2 3
1958 55.00 F 1 2 2
1959 54.00 F 1 2 2
1960 26.00 F 3 2 2
1961 60.00 M 9 2 3
1962 53.00 F 7 2 2
1963 35.00 F 0 2 2
1964 59.00 M 8 2 2
1965 62.00 M 3 2 3
1966 55.00 M 8 2 2
1967 60.00 M 8 2 3
1968 63.00 F 2 2 3
1969 98.00 M 4 2 3
1970 68.00 F 1 2 3
1971 68.00 F 5 2 3
1972 72.00 M 4 2 3
1973 80.00 M 1 2 3
1974 72.00 M 2 2 3
1975 68.00 F 1 2 3
1976 60.00 F 5 2 3
1977 48.00 M 4 2 2
1978 50.00 M 6 2 2
1979 56.00 M 1 2 2
1980 56.00 M 4 2 2
1981 52.00 M 2 2 2
1982 60.00 M 1 2 3
1983 78.00 M 0 2 3
1984 48.00 M 2 2 2
1985 62.00 M 2 2 3
1986 62.00 M 4 2 3
1987 70.00 F 1 2 3
1988 75.00 M 2 2 3
1989 44.00 F 3 2 2
1990 62.00 M 2 2 3
1991 78.00 M 3 2 3
1992 52.00 M 0 2 2
1993 55.00 M 0 2 2
1994 75.00 F 17 2 3
1995 80.00 M 0 2 3
1996 43.00 M 1 2 2
1997 36.00 M 0 2 2
1998 65.00 M 5 2 3
1999 51.00 M 1 2 2
2000 34.00 M 1 2 2
2001 60.00 F 0 2 3
2002 47.00 M 1 2 2
2003 64.00 M 10 2 3
2004 51.00 M 3 2 2
2005 62.00 M 2 2 3
2006 82.00 M 1 2 3
2007 82.00 F 0 2 3
2008 72.00 M 0 2 3
2009 59.00 F 5 2 2
2010 65.00 F 0 2 3
2011 74.00 M 0 2 3
2012 86.00 F 0 2 3
2013 44.00 M 1 2 2
2014 70.00 F 1 2 3
2015 62.00 M 0 2 3
2016 46.00 F 2 2 2
2017 44.00 M 0 2 2
2018 50.00 F 1 2 2
2019 71.00 M 0 2 3
2020 30.00 F 1 2 2
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2022 65.00 F 2 2 3
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2100 55.00 M 1 2 2
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19311 32.00 M 9 1 2
19312 46.00 M 6 1 2
19313 54.00 M 9 1 2
19314 6.00 F 12 1 1
19315 65.00 M 12 1 3
19316 3.00 F 7 1 1
19317 2.00 M 12 1 1
19318 24.00 F 12 1 2
19319 45.00 F 12 1 2
19320 29.00 F 18 1 2
19321 4.00 F 18 1 1
19322 13.00 F 18 1 1
19323 8.00 M 18 1 1
19324 59.00 M 18 1 2
19325 37.00 M 10 1 2
19326 40.00 F 10 1 2
19327 16.00 F 18 1 1
19328 33.00 F 18 1 2
19329 31.00 M 18 1 2
19330 56.00 M 18 1 2
19331 50.00 F 18 1 2
19332 42.00 F 10 1 2
19333 16.00 M 18 1 1
19334 12.00 F 18 1 1
19335 28.00 M 10 1 2
19336 23.00 F 10 1 2
19337 23.00 M 12 1 2
19338 31.00 M 8 1 2
19339 27.00 M 10 1 2
19340 40.00 M 8 1 2
19341 42.00 M 10 1 2
19342 36.00 M 8 1 2
19343 26.00 M 21 1 2
19344 34.00 M 10 1 2
19345 34.00 M 10 1 2
19346 39.00 M 10 1 2
19347 43.00 M 10 1 2
19348 43.00 M 8 1 2
19349 31.00 M 12 1 2
19350 40.00 M 10 1 2
19351 36.00 M 12 1 2
19352 25.00 M 10 1 2
19353 50.00 F 10 1 2
19354 29.00 F 10 1 2
19355 21.00 F 10 1 2
19356 60.00 M 10 1 3
19357 24.00 F 10 1 2
19358 21.00 M 12 1 2
19359 19.00 F 6 1 2
19360 32.00 M 10 1 2
19361 24.00 M 12 1 2
19362 56.00 M 10 1 2
19363 28.00 M 10 1 2
19364 35.00 F 10 1 2
19365 39.00 F 12 1 2
19366 38.00 F 10 1 2
19367 60.00 M 10 1 3
19368 27.00 F 10 1 2
19369 30.00 M 8 1 2
19370 0.30 M 8 1 1
19371 42.00 M 8 1 2
19372 19.00 M 5 1 2
19373 28.00 F 5 1 2
19374 45.00 F 5 1 2
19375 47.00 M 10 1 2
19376 46.00 M 10 1 2
19377 65.00 F 10 1 3
19378 45.00 F 10 1 2
19379 44.00 F 10 1 2
19380 43.00 F 7 1 2
19381 25.00 F 10 1 2
19382 22.00 F 7 1 2
19383 20.00 F 7 1 2
19384 16.00 F 7 1 1
19385 13.00 M 7 1 1
19386 15.00 F 7 1 1
19387 8.00 M 10 1 1
19388 48.00 M 10 1 2
19389 23.00 M 7 1 2
19390 24.00 M 7 1 2
19391 16.00 M 7 1 1
19392 19.00 M 7 1 2
19393 47.00 M 7 1 2
19394 62.00 F 7 1 3
19395 48.00 M 7 1 2
19396 70.00 M 7 1 3
19397 70.00 F 7 1 3
19398 38.00 M 7 1 2
19399 42.00 M 7 1 2
19400 26.00 M 7 1 2
19401 68.00 F 7 1 3
19402 28.00 M 7 1 2
19403 24.00 M 7 1 2
19404 24.00 M 7 1 2
19405 19.00 F 7 1 2
19406 30.00 M 7 1 2
19407 60.00 F 7 1 3
19408 38.00 M 7 1 2
19409 26.00 M 7 1 2
19410 38.00 F 7 1 2
19411 10.00 M 7 1 1
19412 50.00 M 7 1 2
19413 52.00 M 7 1 2
19414 40.00 M 7 1 2
19415 20.00 F 7 1 2
19416 8.00 F 7 1 1
19417 75.00 M 7 1 3
19418 65.00 F 7 1 3
19419 75.00 F 7 1 3
19420 26.00 M 7 1 2
19421 38.00 F 7 1 2
19422 64.00 M 16 1 3
19423 62.00 F 7 1 3
19424 14.00 M 7 1 1
19425 6.00 M 7 1 1
19426 38.00 M 7 1 2
19427 13.00 F 7 1 1
19428 30.00 M 7 1 2
19429 30.00 F 7 1 2
19430 55.00 F 3 1 2
19431 45.00 F 7 1 2
19432 40.00 F 7 1 2
19433 4.00 F 7 1 1
19434 33.00 M 7 1 2
19435 7.00 M 7 1 1
19436 24.00 M 7 1 2
19437 21.00 F 7 1 2
19438 28.00 M 7 1 2
19439 30.00 M 7 1 2
19440 12.00 F 7 1 1
19441 20.00 F 7 1 2
19442 9.00 F 7 1 1
19443 31.00 M 8 1 2
19444 36.00 M 8 1 2
19445 24.00 M 8 1 2
19446 33.00 M 8 1 2
19447 30.00 M 8 1 2
19448 23.00 M 8 1 2
19449 22.00 M 8 1 2
19450 42.00 M 8 1 2
19451 54.00 F 5 1 2
19452 49.00 M 8 1 2
19453 53.00 M 8 1 2
19454 52.00 M 8 1 2
19455 22.00 M 8 1 2
19456 30.00 M 8 1 2
19457 24.00 M 8 1 2
19458 28.00 M 8 1 2
19459 23.00 M 8 1 2
19460 22.00 M 8 1 2
19461 25.00 M 8 1 2
19462 21.00 M 6 1 2
19463 49.00 F 5 1 2
19464 19.00 M 8 1 2
19465 38.00 M 9 1 2
19466 30.00 F 9 1 2
19467 54.00 F 14 1 2
19468 51.00 M 9 1 2
19469 51.00 M 9 1 2
19470 38.00 F 11 1 2
19471 42.00 M 11 1 2
19472 15.00 M 11 1 1
19473 27.00 M 11 1 2
19474 17.00 M 11 1 1
19475 50.00 F 9 1 2
19476 19.00 M 9 1 2
19477 54.00 M 9 1 2
19478 33.00 M 11 1 2
19479 36.00 M 9 1 2
19480 20.00 M 9 1 2
19481 21.00 F 19 1 2
19482 55.00 F 9 1 2
19483 31.00 F 9 1 2
19484 25.00 F 9 1 2
19485 32.00 F 9 1 2
19486 4.00 F 9 1 1
19487 70.00 F 9 1 3
19488 42.00 M 9 1 2
19489 20.00 M 9 1 2
19490 32.00 F 9 1 2
19491 52.00 M 9 1 2
19492 32.00 F 9 1 2
19493 21.00 F 9 1 2
19494 60.00 F 9 1 3
19495 23.00 M 9 1 2
19496 35.00 M 9 1 2
19497 19.00 M 9 1 2
19498 20.00 F 9 1 2
19499 22.00 F 9 1 2
19500 72.00 F 9 1 3
19501 22.00 F 9 1 2
19502 20.00 M 9 1 2
19503 40.00 F 9 1 2
19504 48.00 F 9 1 2
19505 45.00 F 9 1 2
19506 80.00 F 9 1 3
19507 24.00 F 19 1 2
19508 28.00 M 9 1 2
19509 42.00 M 9 1 2
19510 30.00 F 12 1 2
19511 60.00 F 14 1 3
19512 35.00 M 13 1 2
19513 12.00 F 6 1 1
19514 21.00 M 14 1 2
19515 32.00 M 21 1 2
19516 35.00 M 14 1 2
19517 45.00 F 13 1 2
19518 36.00 M 13 1 2
19519 60.00 F 12 1 3
19520 21.00 F 14 1 2
19521 30.00 F 15 1 2
19522 22.00 F 14 1 2
19523 62.00 M 13 1 3
19524 52.00 F 14 1 2
19525 60.00 M 15 1 3
19526 69.00 M 14 1 3
19527 45.00 F 12 1 2
19528 54.00 M 14 1 2
19529 24.00 F 15 1 2
19530 54.00 F 14 1 2
19531 53.00 M 12 1 2
19532 27.00 F 13 1 2
19533 68.00 M 22 1 3
19534 50.00 F 14 1 2
19535 36.00 M 14 1 2
19536 41.00 M 14 1 2
19537 61.00 M 14 1 3
19538 37.00 F 12 1 2
19539 34.00 M 13 1 2
19540 34.00 F 13 1 2
19541 44.00 F 13 1 2
19542 65.00 F 13 1 3
19543 15.00 F 14 1 1
19544 70.00 F 14 1 3
19545 32.00 F 13 1 2
19546 53.00 F 14 1 2
19547 54.00 M 10 1 2
19548 48.00 F 10 1 2
19549 45.00 F 12 1 2
19550 20.00 F 12 1 2
19551 15.00 F 12 1 1
19552 4.00 M 12 1 1
19553 60.00 M 12 1 3
19554 40.00 M 12 1 2
19555 30.00 M 14 1 2
19556 40.00 F 14 1 2
19557 16.00 M 14 1 1
19558 16.00 M 14 1 1
19559 30.00 F 14 1 2
19560 19.00 M 7 1 2
19561 28.00 M 7 1 2
19562 35.00 F 17 1 2
19563 25.00 M 14 1 2
19564 35.00 M 14 1 2
19565 30.00 F 14 1 2
19566 18.00 M 14 1 2
19567 25.00 F 7 1 2
19568 9.00 M 8 1 1
19569 40.00 F 9 1 2
19570 28.00 F 16 1 2
19571 62.00 M 9 1 3
19572 28.00 F 17 1 2
19573 10.00 F 16 1 1
19574 60.00 F 22 1 3
19575 19.00 M 14 1 2
19576 40.00 F 10 1 2
19577 40.00 F 10 1 2
19578 50.00 M 10 1 2
19579 22.00 F 10 1 2
19580 30.00 M 9 1 2
19581 53.00 F 8 1 2
19582 55.00 M 9 1 2
19583 27.00 F 10 1 2
19584 31.00 M 10 1 2
19585 30.00 M 10 1 2
19586 24.00 F 10 1 2
19587 0.75 F 10 1 1
19588 2.00 F 16 1 1
19589 40.00 F 9 1 2
19590 8.00 M 10 1 1
19591 10.00 M 9 1 1
19592 44.00 M 9 1 2
19593 27.00 M 9 1 2
19594 24.00 F 16 1 2
19595 15.00 M 9 1 1
19596 9.00 F 9 1 1
19597 28.00 M 10 1 2
19598 25.00 F 3 1 2
19599 45.00 F 12 1 2
19600 23.00 F 21 1 2
19601 38.00 F 12 1 2
19602 6.00 F 14 1 1
19603 36.00 F 14 1 2
19604 12.00 F 5 1 1
19605 55.00 F 5 1 2
19606 56.00 M 5 1 2
19607 14.00 M 5 1 1
19608 45.00 F 5 1 2
19609 50.00 F 14 1 2
19610 36.00 M 5 1 2
19611 25.00 F 10 1 2
19612 58.00 F 10 1 2
19613 0.41 F 10 1 1
19614 33.00 M 10 1 2
19615 17.00 M 6 1 1
19616 43.00 F 7 1 2
19617 69.00 F 6 1 3
19618 50.00 M 6 1 2
19619 42.00 F 7 1 2
19620 19.00 F 6 1 2
19621 45.00 M 6 1 2
19622 29.00 F 12 1 2
19623 35.00 M 12 1 2
19624 65.00 M 10 1 3
19625 75.00 F 12 1 3
19626 27.00 M 11 1 2
19627 28.00 F 11 1 2
19628 53.00 F 11 1 2
19629 6.00 M 11 1 1
19630 31.00 F 11 1 2
19631 55.00 M 13 1 2
19632 24.00 M 11 1 2
19633 21.00 F 12 1 2
19634 33.00 M 12 1 2
19635 68.00 F 12 1 3
19636 6.00 F 12 1 1
19637 31.00 M 12 1 2
19638 12.00 M 12 1 1
19639 5.00 M 12 1 1
19640 27.00 F 9 1 2
19641 8.00 F 8 1 1
19642 12.00 M 8 1 1
19643 34.00 F 8 1 2
19644 36.00 M 8 1 2
19645 25.00 M 8 1 2
19646 15.00 M 8 1 1
19647 30.00 F 8 1 2
19648 37.00 M 8 1 2
19649 66.00 M 7 1 3
19650 28.00 M 7 1 2
19651 50.00 F 7 1 2
19652 50.00 M 7 1 2
19653 60.00 M 7 1 3
19654 65.00 F 7 1 3
19655 60.00 F 7 1 3
19656 59.00 F 7 1 2
19657 30.00 F 7 1 2
19658 61.00 M 12 1 3
19659 65.00 F 7 1 3
19660 22.00 M 7 1 2
19661 45.00 M 7 1 2
19662 16.00 M 7 1 1
19663 62.00 M 7 1 3
19664 70.00 M 7 1 3
19665 10.00 F 7 1 1
19666 8.00 M 7 1 1
19667 48.00 F 7 1 2
19668 65.00 M 3 1 3
19669 25.00 M 7 1 2
19670 70.00 F 1 1 3
19671 30.00 F 7 1 2
19672 85.00 M 7 1 3
19673 29.00 M 7 1 2
19674 58.00 F 7 1 2
19675 31.00 F 7 1 2
19676 29.00 F 7 1 2
19677 4.00 F 7 1 1
19678 1.00 M 7 1 1
19679 34.00 F 7 1 2
19680 15.00 M 7 1 1
19681 23.00 M 7 1 2
19682 20.00 F 7 1 2
19683 26.00 F 7 1 2
19684 25.00 F 7 1 2
19685 59.00 M 9 1 2
19686 42.00 M 9 1 2
19687 58.00 F 9 1 2
19688 56.00 F 10 1 2
19689 65.00 F 10 1 3
19690 26.00 M 10 1 2
19691 25.00 M 10 1 2
19692 31.00 M 7 1 2
19693 39.00 M 7 1 2
19694 58.00 F 6 1 2
19695 14.00 M 6 1 1
19696 56.00 F 7 1 2
19697 60.00 F 7 1 3
19698 59.00 M 7 1 2
19699 47.00 M 7 1 2
19700 26.00 F 10 1 2
19701 18.00 M 13 1 2
19702 31.00 M 7 1 2
19703 71.00 F 10 1 3
19704 30.00 F 10 1 2
19705 24.00 F 3 1 2
19706 29.00 F 7 1 2
19707 65.00 M 18 1 3
19708 25.00 M 4 1 2
19709 35.00 M 11 1 2
19710 60.00 F 10 1 3
19711 38.00 F 10 1 2
19712 65.00 M 9 1 3
19713 21.00 F 9 1 2
19714 38.00 M 13 1 2
19715 37.00 M 14 1 2
19716 52.00 F 13 1 2
19717 23.00 M 13 1 2
19718 45.00 F 12 1 2
19719 32.00 M 12 1 2
19720 27.00 M 12 1 2
19721 62.00 F 15 1 3
19722 48.00 M 12 1 2
19723 26.00 M 13 1 2
19724 80.00 F 13 1 3
19725 44.00 M 12 1 2
19726 34.00 M 11 1 2
19727 58.00 M 11 1 2
19728 30.00 M 14 1 2
19729 6.00 F 13 1 1
19730 68.00 M 12 1 3
19731 23.00 F 14 1 2
19732 58.00 M 14 1 2
19733 56.00 M 13 1 2
19734 46.00 M 14 1 2
19735 47.00 M 12 1 2
19736 27.00 F 13 1 2
19737 35.00 F 12 1 2
19738 64.00 F 17 1 3
19739 52.00 F 17 1 2
19740 27.00 F 13 1 2
19741 30.00 F 12 1 2
19742 30.00 M 13 1 2
19743 36.00 M 13 1 2
19744 32.00 F 12 1 2
19745 25.00 F 11 1 2
19746 51.00 F 13 1 2
19747 55.00 F 13 1 2
19748 52.00 M 13 1 2
19749 36.00 F 13 1 2
19750 63.00 F 15 1 3
19751 59.00 M 13 1 2
19752 48.00 M 13 1 2
19753 27.00 M 13 1 2
19754 59.00 M 12 1 2
19755 81.00 M 13 1 3
19756 55.00 M 13 1 2
19757 55.00 M 12 1 2
19758 55.00 M 13 1 2
19759 16.00 M 13 1 1
19760 51.00 F 12 1 2
19761 23.00 M 11 1 2
19762 15.00 F 12 1 1
19763 49.00 F 11 1 2
19764 66.00 F 11 1 3
19765 24.00 F 11 1 2
19766 19.00 F 13 1 2
19767 16.00 F 11 1 1
19768 8.00 F 11 1 1
19769 37.00 F 11 1 2
19770 52.00 F 12 1 2
19771 70.00 M 13 1 3
19772 56.00 M 16 1 2
19773 55.00 M 12 1 2
19774 1.00 M 12 1 1
19775 27.00 F 12 1 2
19776 32.00 M 12 1 2
19777 4.00 F 12 1 1
19778 27.00 M 14 1 2
19779 73.00 M 14 1 3
19780 50.00 F 13 1 2
19781 23.00 F 14 1 2
19782 54.00 M 13 1 2
19783 11.00 M 13 1 1
19784 68.00 M 20 1 3
19785 38.00 M 13 1 2
19786 43.00 F 11 1 2
19787 30.00 M 12 1 2
19788 62.00 F 12 1 3
19789 50.00 M 12 1 2
19790 49.00 M 14 1 2
19791 57.00 F 13 1 2
19792 68.00 F 15 1 3
19793 23.00 F 11 1 2
19794 25.00 M 13 1 2
19795 35.00 M 13 1 2
19796 24.00 F 13 1 2
19797 65.00 M 12 1 3
19798 33.00 M 12 1 2
19799 49.00 F 13 1 2
19800 36.00 M 13 1 2
19801 57.00 M 14 1 2
19802 63.00 M 13 1 3
19803 48.00 M 13 1 2
19804 66.00 M 13 1 3
19805 85.00 M 13 1 3
19806 16.00 M 11 1 1
19807 42.00 M 13 1 2
19808 26.00 F 13 1 2
19809 27.00 M 13 1 2
19810 28.00 M 13 1 2
19811 75.00 M 13 1 3
19812 55.00 F 13 1 2
19813 50.00 M 13 1 2
19814 55.00 M 14 1 2
19815 24.00 M 13 1 2
19816 56.00 M 13 1 2
19817 54.00 F 13 1 2
19818 42.00 M 13 1 2
19819 56.00 F 12 1 2
19820 57.00 M 12 1 2
19821 27.00 M 17 1 2
19822 27.00 F 13 1 2
19823 57.00 M 15 1 2
19824 56.00 F 13 1 2
19825 55.00 F 13 1 2
19826 29.00 M 13 1 2
19827 28.00 M 13 1 2
19828 49.00 M 13 1 2
19829 65.00 M 14 1 3
19830 67.00 M 13 1 3
19831 31.00 F 13 1 2
19832 51.00 M 4 1 2
19833 4.00 M 13 1 1
19834 10.00 F 13 1 1
19835 8.00 M 13 1 1
19836 58.00 M 13 1 2
19837 59.00 M 13 1 2
19838 51.00 M 13 1 2
19839 59.00 M 14 1 2
19840 20.00 M 13 1 2
19841 27.00 M 14 1 2
19842 25.00 M 14 1 2
19843 17.00 M 14 1 1
19844 14.00 M 14 1 1
19845 14.00 F 14 1 1
19846 70.00 M 7 1 3
19847 26.00 F 7 1 2
19848 32.00 F 8 1 2
19849 59.00 M 8 1 2
19850 30.00 F 8 1 2
19851 38.00 M 8 1 2
19852 36.00 M 9 1 2
19853 42.00 M 12 1 2
19854 54.00 M 9 1 2
19855 43.00 M 17 1 2
19856 17.00 M 17 1 1
19857 32.00 M 9 1 2
19858 50.00 M 7 1 2
19859 42.00 M 7 1 2
19860 48.00 M 7 1 2
19861 22.00 M 7 1 2
19862 56.00 M 7 1 2
19863 38.00 M 7 1 2
19864 9.00 M 7 1 1
19865 13.00 F 7 1 1
19866 30.00 M 7 1 2
19867 31.00 F 7 1 2
19868 65.00 M 10 1 3
19869 62.00 M 9 1 3
19870 33.00 F 7 1 2
19871 64.00 M 7 1 3
19872 64.00 M 7 1 3
19873 30.00 M 11 1 2
19874 35.00 M 9 1 2
19875 56.00 M 11 1 2
19876 65.00 F 8 1 3
19877 25.00 M 17 1 2
19878 32.00 M 8 1 2
19879 23.00 F 8 1 2
19880 30.00 F 8 1 2
19881 30.00 F 8 1 2
19882 37.00 F 8 1 2
19883 30.00 F 8 1 2
19884 28.00 M 8 1 2
19885 36.00 F 15 1 2
19886 65.00 M 10 1 3
19887 50.00 F 11 1 2
19888 25.00 F 20 1 2
19889 28.00 M 7 1 2
19890 31.00 M 9 1 2
19891 29.00 M 7 1 2
19892 29.00 M 7 1 2
19893 30.00 M 9 1 2
19894 29.00 F 9 1 2
19895 58.00 M 9 1 2
19896 33.00 F 9 1 2
19897 38.00 F 9 1 2
19898 19.00 M 9 1 2
19899 14.00 F 9 1 1
19900 30.00 M 9 1 2
19901 13.00 F 9 1 1
19902 25.00 M 9 1 2
19903 15.00 F 9 1 1
19904 38.00 F 12 1 2
19905 24.00 M 9 1 2
19906 28.00 M 9 1 2
19907 41.00 F 9 1 2
19908 19.00 F 9 1 2
19909 19.00 F 9 1 2
19910 50.00 M 9 1 2
19911 24.00 M 9 1 2
19912 40.00 F 9 1 2
19913 32.00 M 9 1 2
19914 6.00 M 7 1 1
19915 3.00 M 7 1 1
19916 34.00 M 9 1 2
19917 37.00 M 9 1 2
19918 30.00 M 20 1 2
19919 31.00 M 20 1 2
19920 22.00 F 9 1 2
19921 77.00 M 11 1 3
19922 72.00 M 20 1 3
19923 32.00 M 9 1 2
19924 35.00 M 20 1 2
19925 50.00 F 20 1 2
19926 69.00 F 9 1 3
19927 42.00 F 21 1 2
19928 32.00 M 2 1 2
19929 25.00 M 8 1 2
19930 24.00 F 9 1 2
19931 3.00 M 9 1 1
19932 50.00 F 9 1 2
19933 25.00 M 10 1 2
19934 50.00 M 9 1 2
19935 7.00 F 9 1 1
19936 31.00 M 9 1 2
19937 3.00 M 9 1 1
19938 26.00 M 9 1 2
19939 45.00 F 9 1 2
19940 1.00 M 9 1 1
19941 30.00 F 12 1 2
19942 12.00 F 12 1 1
19943 40.00 M 13 1 2
19944 62.00 M 8 1 3
19945 39.00 M 8 1 2
19946 57.00 F 8 1 2
19947 30.00 F 9 1 2
19948 7.00 M 8 1 1
19949 30.00 F 9 1 2
19950 45.00 M 9 1 2
19951 2.00 F 15 1 1
19952 30.00 M 13 1 2
19953 18.00 F 13 1 2
19954 9.00 F 15 1 1
19955 29.00 M 10 1 2
19956 30.00 M 15 1 2
19957 8.00 M 16 1 1
19958 52.00 M 11 1 2
19959 12.00 M 9 1 1
19960 24.00 M 9 1 2
19961 24.00 M 9 1 2
19962 22.00 M 9 1 2
19963 1.00 M 14 1 1
19964 29.00 M 4 1 2
19965 40.00 F 9 1 2
19966 42.00 F 10 1 2
19967 28.00 F 13 1 2
19968 19.00 F 14 1 2
19969 21.00 M 14 1 2
19970 57.00 F 20 1 2
19971 54.00 M 13 1 2
19972 24.00 F 9 1 2
19973 25.00 M 13 1 2
19974 30.00 F 9 1 2
19975 64.00 F 11 1 3
19976 72.00 F 8 1 3
19977 43.00 M 8 1 2
19978 30.00 M 8 1 2
19979 35.00 F 8 1 2
19980 60.00 F 6 1 3
19981 75.00 M 12 1 3
19982 19.00 F 17 1 2
19983 13.00 M 17 1 1
19984 25.00 M 12 1 2
19985 50.00 M 17 1 2
19986 20.00 F 6 1 2
19987 20.00 F 6 1 2
19988 60.00 F 6 1 3
19989 20.00 F 6 1 2
19990 32.00 F 6 1 2
19991 28.00 F 6 1 2
19992 68.00 F 8 1 3
19993 20.00 F 6 1 2
19994 70.00 M 6 1 3
19995 30.00 M 6 1 2
19996 20.00 M 6 1 2
19997 52.00 F 6 1 2
19998 33.00 M 6 1 2
19999 56.00 M 9 1 2
[ reached 'max' / getOption("max.print") -- omitted 6742 rows ]Preparing the data
For the analysis, slight modifications will need to be implemented to
the data. Since visR was
developed with the goal to speed up the clinical development process, it
follows the cdisc ADaM
standards. Additionally, some of the functions are data-type sensitive
and give more informative summaries when the proper data-type is
present.
The following changes are necessary:
As per ADaM guidelines, an event is indicated as
0while censoring is indicated as>0. In the data that we will use,2stands for the event while1stands for censoring. Therefore, the censoring values have to be adjusted.The column
Age_Catwill be parsed as aintegerwhileSexwill becharacter. Converting them tofactorwill result in more informative output. Furthermore,Age_Catis encoded as a numerical value between 1-3. By adding the respective names to these categories, the output will be more readable.
Generation of summary tables
A typical clinical report contains a demography table, detailing the distribution of patients across different arms of the clinical trial. Commonly reported variables are age, sex and baseline disease characteristics. This table is sometimes referred to as Table 1.
visR provides a set of functions to both generate and
render tables. When rendering, visR enforces the
specification of a data source and a title. These functions are
demonstrated demonstrated below. By default, the table is generated for
the whole population but a stratifier can be defined.
# Define meta-data
DATASOURCE = "https://github.com/vntkumar8/covid-survival"
TITLE = "Indian Covid Data"
data %>% tableone(title = TITLE, datasource = DATASOURCE)Exercise 1: Generate the table using
Sex as the stratifier.
Exercise 2: Generate the table using
Sex and Age_Cat as the stratifier.
Exercise 3: Add an additional footnote
to the table.
# Have fun!Note
You will notice that the the above tables will contain
NAs for the stratifier they have been split on. A
workaround for this is to first get the table 1 as a
data.frame, then remove these stratifier, and then render it. This
examples demonstrates the modular built and compatibility of visR. The
tableone command was actually a wrapper around the
functions get_tableone which builds a
data.frame with summary statistics and the
render command which displays it nicely.
data %>%
get_tableone(strata = c("Sex", "Age_Cat")) %>%
filter(variable != "Sex" & variable != "Age_Cat") %>%
render(title = TITLE, datasource = DATASOURCE)(This will be fixed in the upcoming release.)
Fitting survival models
As mentioned, visR has
defaults following the CDISC ADaM standard. If your data set uses these
as well, estimating and plotting survival curves is particularly
straightforward.
# The dataset that comes with visR is cdisc ADaM compliant
mod = visR::adtte %>% estimate_KM()
# When no strata is specified, the overall patient cohort is analysed
mod
mod %>% visr()Survival analysis of the Indian COVID data
Since ADaM standards are not utilized beyond clinical trial analysis
and reporting,, the functions in visR have
parameters allowing for specification of the respective column names
without modifying the actual data.
mod = data %>% estimate_KM(strata = "Sex", CNSR = "Status", AVAL = "Stay")
modOnce the model is estimated, different summary statistics are easy to
get_
mod %>% get_pvalue()Exercise 1: Calculate the quantiles for the model.
# Have fun!Plotting survival models
These survival models can easily be plotted using visR.
The visR function
provides several convenient functions to adjust the aesthetics of the
plot.
mod %>%
visr(legend_position = "top",
x_label = "")
mod %>%
visr(legend_position = "top",
x_units = "years")
mod %>%
visr(legend_position = "top",
fun = "log")Exercise 1: Change the stratifier in the survival
model to Age_Cat.
Exercise 2: Plot the new model.
# Have fun!Improving survival plots
On top this, visR provides
several functions to further improve plots. It is, for example, easy to
add confidence intervals or censoring markers.
Exercise 1: Add markers for censoring. (Hint:
functions adding statistics to a plot in visR usually
start with add_).
# Have fun!Also, a function is provided to further annotate the plot with additional information.
mod %>%
visr() %>%
visR::add_annotation(label = "Hello world", xmin = 0, ymin = 0.5)Exercise 1: Add information about statistical
significance tests to a survival plot. (Hint: Combine
add_annotation() and get_pvalue()).
# Have fun!Styling
Particular attention was paid to stay compatible with the
ggplot2 ecosystem. Therefore, the created objects can for
example be styled like any other ggplot.
gg = mod %>% visr()
gg + ggplot2::theme_dark()Exercise 1: Try to recreate the Figure 5 of the Indian Covid data publication as close as possible using what you learned until now. Alternatively, you can create a more visually pleasing variant.
# Have fun!Combining plots with summary statistics
Another good practice is to provide summary statistics that accompany
the survival plot. For this purpose, visR provides
a handy function that can be appended to the normal piping
framework.
NOTE: This breaks compatibility with the
ggplot2 functions. Therefore, styling has to happen before
adding the risktable.
mod %>%
visr() %>%
add_risktable(statlist = c("n.risk", "n.event", "n.censor"))Analogous to the functions creating and showing a Table 1, risktables
can also be generated independently (using a get_ function)
and can then be rendered.
Exercise 1: Create and render a risktable from the survival model
# Have fun!Advanced usage
Here we demonstrate some advanced options that might be useful for some cases.
Attrition
Often only subgroups of the population are analysed and complete data
sets need to be filtered according to specific criteria. The function
get_attrition allows to list filter statements
explanations, which can be visualized as a table (with
render) or be plotted (with visr). The
function apply_attrition then filters a
data.frame.
data$Patient = 1:nrow(data)
condition = "Age_Cat != '<18yr'"
filters = data %>% get_attrition(subject_column_name = "Patient",
criteria_descriptions = "18 and older",
criteria_conditions = condition)
filters %>% visr()
filters %>% render(title = "Attrition Table", datasource = DATASOURCE)
data %>% apply_attrition(condition)Custom summary functions for the Table 1
The tableone function accepts custom functions to be run on the columns. For this purpose, the different possible data-type have to be considered. For simplicity, only the behaviour for the numeric columns will be changed in the example below.
# This is basically the default function but doesn't return the missing values.
my_func = function(x) {
if (is.numeric(x)) {
dat = list(
`Mean (SD)` = paste0(format(mean(x, na.rm = TRUE), digits = 3),
" (",
format(sd(x, na.rm = TRUE), digits = 3),
")"),
`Median (IQR)` = paste0(format(median(x, na.rm = TRUE), digits = 3),
" (",
format(quantile(x, probs=0.25, na.rm = TRUE), digits = 3),
"-",
format(quantile(x, probs=0.75, na.rm = TRUE), digits = 3),
")"),
`Min-max` = paste0(format(min(x, na.rm = TRUE), digits = 3),
"-", format(max(x, na.rm = TRUE), digits = 3))
)
list(dat)
} else {
visR::summarize_short(x)
}
}
data %>%
get_tableone(strata = c("Sex", "Age_Cat"), summary_function = my_func) %>%
filter(variable != "Sex" & variable != "Age_Cat") %>%
render(title = TITLE, datasource = DATASOURCE)Different rendering engines for table output
As default, the tables will be rendered with gt.
However, other options like for example kable or
dt are also implemented.
mod %>%
get_pvalue() %>%
render(title = TITLE,
datasource = DATASOURCE,
engine = "kable") # dt launches a child process which crashes on shinyapps.io? Possible solution to the exercise for recreating figure 5
m = data %>% estimate_KM(strata = c("Sex", "Age_Cat"),
CNSR = "Status",
AVAL = "Stay")
half_surv_df = m %>%
get_quantile(probs = 0.5) %>%
dplyr::filter(quantity == "quantile") %>%
dplyr::mutate(pos1 = `50`, pos2 = 0.5) %>%
dplyr::select(-c(quantity, `50`))
half_surv_df_horizontal = half_surv_df %>% dplyr::mutate(pos1 = 0)
half_surv_df_vertical = half_surv_df %>% dplyr::mutate(pos2 = 0)
half_surv_df = rbind(half_surv_df, half_surv_df_horizontal, half_surv_df_vertical)
m %>%
visr() %>%
add_CI() %>%
add_CNSR() +
ggplot2::geom_line(data = half_surv_df, ggplot2::aes(pos1, pos2, group = strata), linetype = "dashed")