artcog: Arthritis and cognition in the elderly.
In sensitivitymult: Sensitivity Analysis for Observational Studies with Multiple Outcomes
Description
Usage
Format
Details
Source
References
Examples
Description
The R package contains a simulated data set similar to actual
data from 2009-2011 Irish Longitudinal Study of Aging (TILDA) used
in Rosenbaum (2017). Additionally, in the documentation
below, instructions are given for constructing the actual
data set after downloading a file from ICPSR. The simulated
data may be used to try the methods in this package. The
actual data may be used to replicate the calculations in
Rosenbaum (2017). Please be careful to distinguish
the simulated data (with continuous outcomes) and the
actual data (with integer outcomes), as scientific
conclusions should not be based on the simulated data.
Instructions for creating the actual data are in
the example section, but are not executed because you
must obtain a file from ICPSR.
The simulated data were built from the actual data by
calculating the trivariate within group means and the
pooled within group covariance matrix. Then a data
set of the same size was sampled from the trivariate
Normal distribution, using the actual means and
covariance matrix as the population parameters for
the simulation. The simulation used the mvtnorm
package. Although the data set consists of matched
triples, in the simulated version, the matched
sets are independent of the outcomes.
There are 219 matched triples containing
one individual with arthritis (arthritis=1) and two without
(arthritis=0). There are three measures of cognitive
performance, words, wordsdelay and animals.
Usage
1
data("artcog")
Format
A data frame with 657 observations on the following 5 variables.
arthritis1 if osteoarthritis, 0 if no arthritis
wordsIndividuals are read a list of words and are
immediately asked to recall as many as they can.
wordsdelayAfter a delay and another task, individuals
are again asked to recall as many of the words as they can.
animalsIndividuals are asked to name all of the
animals they can think of.
msetIndicator of the matched set: 1, 2, ..., 219.
Details
A theory that NSAIDs reduce the risk of Alzheimer's disease has often been
examined by comparing elderly people with and without arthritis, reasoning
that many people with arthritis have consumed NSAIDs in quantity for a
long period; see McGeer et al. (1996). This comparison does not
ask a person with Alzheimer's disease to recall past use of NSAIDs.
Triples were matched for age, sex, education and mother's education.
Everyone is 75 years old or older.
Source
Simulated data with a script for obtaining the actual data
from the Irish Longitudinal Study of Aging 2009-2011.
References
Irish Longitudinal Study of Aging. http://tilda.tcd.ie/
McGeer, P. L., Schulzer, M., and McGeer, E. G. (1996). Arthritis and anti-
inflammatory agents as possible protective factors for alzheimer's disease.
Neurology, 47, 425-432.
Rosenbaum, P. R. (2017) Combining planned and discovered comparisons
in observational studies. Manuscript. (The artcog example is discussed
in this manuscript.)
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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47
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49
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52
53
54
55
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57
58
59
60
61
62
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101
102
103
104
105
106
107
108
109
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118
119
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129
130
131
# data(artcog) returns the simulated example.
data(artcog)
# Three correlated outcomes.
cor(artcog[,2:4])
# See documentation for principal() for use of this example.
# The code below constructs the actual data, as distinct
# from the simulated example. The lengthy list of numbers
# assembles the 219 matched triples, or 657 = 3*219 rows,
# from the larger TILDA data set.
## Not run:
# Obtain from ICPSR the R data file
# ICPSR_34315-1IrishAging/34315-0001-Data.rda
# http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315?q=34315
# The data should have 8504 rows and 1992 columns
d<-da34315.0001
attach(d)
wordsC<-PH118
wordsI<-PH119
wordsC[wordsC<0]<-0
wordsI[wordsI<0]<-0
words<-wordsC+wordsI
wordsdelayC<-PH712
wordsdelayC[is.na(wordsdelayC)]<-0
wordsdelayC[wordsdelayC<=-1]<-0
wordsdelayI<-PH713
wordsdelayI[is.na(wordsdelayI)]<-0
wordsdelayI[wordsdelayI<=-1]<-0
wordsdelay<-wordsdelayC+wordsdelayI
animals<-PH125
arthritis<-PH301_03
osteoA<-PH304_1
z<-rep(NA,dim(d)[1])
z[arthritis==0]<-0
z[(arthritis==1)&(osteoA==1)]<-1
detach(d)
artcog<-data.frame(z,words,wordsdelay,animals)
who <- c(91, 4408, 7754, 129, 4716, 8383, 135, 6066,
8028, 280, 894, 5300, 288, 151, 667, 298, 4889, 5977,
333, 1100, 3707, 480, 696, 8148, 568, 372,
7578, 584, 1852, 8057, 589, 3799, 6567, 590, 7422,
8419, 609, 2825, 8272, 669, 1197, 8471, 684, 141,
1847, 687, 2416, 7591, 771, 5239, 6986, 782,
4857, 7654, 850, 885, 2239, 892, 2717, 7788, 929, 248,
4740, 975, 1965, 8242, 1036, 6459, 7973, 1059, 1541,
5901, 1103, 6518, 8264, 1160, 4798, 7330,
1168, 4678, 7319, 1180, 152, 2735, 1191, 3740,
7260, 1199, 26, 5209, 1252, 2615, 3251, 1444, 4790,
7298, 1549, 898, 7630, 1587, 4418, 7122, 1596, 5875,
8489, 1604, 3594, 7246, 1614, 3189, 7052, 1646,
5415, 6828, 1708, 1634, 7029, 1760, 1950, 7815, 1840,
5860, 8334, 1843, 6054, 7331, 1849, 5617, 8046, 1854,
2890, 7703, 1885, 5846, 7247, 1896, 4365, 7803,
1898, 3952, 4187, 1977, 544, 940, 1987, 768, 960,
2029, 5363, 6293, 2161, 10, 4432, 2270, 5620, 7132,
2330, 445, 1301, 2372, 1014, 1138, 2379, 3906,
6183, 2386, 6226, 7203, 2417, 2458, 6616, 2437, 6262,
7178, 2442, 3840, 8024, 2443, 4955, 5834, 2455, 1969,
5967, 2457, 6962, 7560, 2466, 986, 2895, 2498, 2461,
5876, 2522, 1837, 4803, 2618, 7279, 7764, 2734, 4005,
4477, 2747, 221, 3837, 2763, 4440, 7863, 2765,
6173, 7377, 2799, 7711, 7822, 2820, 2676, 7288, 2853,
3035, 7518, 2914, 3142, 6891, 2952, 3081, 4908, 2969,
3077, 6837, 3013, 747, 7614, 3107, 1754, 6564,
3178, 2242, 4377, 3192, 260, 4530, 3246, 3019, 6478,
3313, 4710, 7271, 3389, 356, 1796, 3481, 99, 491,
3571, 658, 1410, 3693, 4341, 7624, 3694, 522,
7702, 3704, 6532, 7171, 3705, 4973, 7131, 3806, 2163,
5400, 3848, 4811, 7097, 3850, 2154, 5773, 3851, 3547,
7613, 3862, 3357, 3370, 3877, 6186, 7990, 3913,
455, 2883, 3931, 3548, 3699, 3933, 3210, 6164, 3935,
4712, 7813, 3940, 5598, 7826, 3964, 2129, 8005, 3997,
49, 1537, 4000, 3915, 5392, 4044, 3014, 6130,
4052, 5208, 7213, 4186, 1586, 4249, 4264, 7058, 7182,
4324, 3950, 7507, 4343, 3701, 6359, 4358, 567, 1020,
4387, 2919, 4011, 4389, 5851, 7125, 4409, 3310,
8100, 4427, 767, 2108, 4439, 1263, 6024, 4447, 3814,
8373, 4478, 3493, 6743, 4479, 939, 2621, 4537, 1264,
7942, 4608, 1797, 2987, 4633, 976, 1814, 4641,
274, 1116, 4697, 4718, 7008, 4750, 2842, 5787, 4791,
4386, 6966, 4812, 2817, 5640, 4815, 845, 5430, 4856,
2288, 2289, 4887, 2182, 4874, 4942, 460, 4300,
4945, 565, 3644, 4946, 487, 3369, 4953, 4352, 6709,
4956, 2731, 3387, 4958, 4436, 6460, 4964, 3388, 6692,
5078, 278, 963, 5110, 842, 4842, 5166, 600,
1530, 5199, 1775, 6210, 5204, 4993, 8477, 5210, 2646,
5563, 5291, 2957, 7777, 5325, 4881, 7053, 5342, 4385,
6444, 5377, 3957, 4319, 5384, 3144, 7757, 5385,
2813, 3054, 5386, 3636, 6185, 5474, 2507, 5085, 5488,
4278, 5675, 5584, 2606, 5359, 5599, 3180, 7037, 5605,
2459, 5304, 5637, 2581, 3621, 5641, 2781, 4302,
5805, 6424, 7227, 5870, 492, 5847, 5909, 1750, 5158,
5923, 3199, 6492, 6039, 4347, 4762, 6048, 5332, 7320,
6080, 1992, 2830, 6091, 5213, 7045, 6099, 5167,
6511, 6135, 5177, 6944, 6172, 2983, 6455, 6176, 2319,
3737, 6189, 5525, 7257, 6196, 4423, 6893, 6256, 2639,
5740, 6322, 1427, 2435, 6370, 7321, 7385, 6371,
1212, 2423, 6417, 205, 1674, 6462, 2393, 2882, 6463,
2170, 4765, 6496, 1630, 5048, 6519, 3058, 7498, 6901,
5237, 7508, 6984, 3819, 6548, 7042, 2961, 6445,
7057, 5457, 7984, 7061, 5401, 6049, 7093, 502, 3847,
7094, 4717, 6348, 7096, 825, 7844, 7099, 2188, 8337,
7251, 7423, 7576, 7269, 2616, 6401, 7270, 2394,
5039, 7273, 2337, 4941, 7300, 2241, 4934, 7316, 2604,
6369, 7355, 2113, 3880, 7402, 1456, 2378, 7473, 6368,
7243, 7592, 5583, 7892, 7615, 855, 6924, 7684,
6412, 6822, 7852, 405, 7077, 7862, 3623, 3990, 7879,
2447, 6334, 7913, 3927, 5299, 7930, 5289, 5844, 7983,
297, 7772, 8006, 3869, 6930, 8009, 2729, 6480,
8081, 4700, 6560, 8109, 62, 8061, 8130, 3351, 4381,
8149, 2854, 6513, 8157, 5220, 7559, 8184, 1523, 4195,
8185, 1459, 3820, 8188, 117, 1050, 8206, 2513,
3954, 8335, 2352, 4435, 8346, 5109, 8207)
artcog<-artcog[who,]
mset<-as.numeric(gl(219,3))
artcog<-cbind(artcog,mset)
rm(z,words,wordsdelay,animals,mset)
## End(Not run)
sensitivitymult documentation built on May 2, 2019, 3:52 a.m.
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Description Usage Format Details Source References Examples
Description
The R package contains a simulated data set similar to actual data from 2009-2011 Irish Longitudinal Study of Aging (TILDA) used in Rosenbaum (2017). Additionally, in the documentation below, instructions are given for constructing the actual data set after downloading a file from ICPSR. The simulated data may be used to try the methods in this package. The actual data may be used to replicate the calculations in Rosenbaum (2017). Please be careful to distinguish the simulated data (with continuous outcomes) and the actual data (with integer outcomes), as scientific conclusions should not be based on the simulated data.
Instructions for creating the actual data are in the example section, but are not executed because you must obtain a file from ICPSR.
The simulated data were built from the actual data by calculating the trivariate within group means and the pooled within group covariance matrix. Then a data set of the same size was sampled from the trivariate Normal distribution, using the actual means and covariance matrix as the population parameters for the simulation. The simulation used the mvtnorm package. Although the data set consists of matched triples, in the simulated version, the matched sets are independent of the outcomes.
There are 219 matched triples containing one individual with arthritis (arthritis=1) and two without (arthritis=0). There are three measures of cognitive performance, words, wordsdelay and animals.
Usage
1 | data("artcog")
|
Format
A data frame with 657 observations on the following 5 variables.
arthritis1 if osteoarthritis, 0 if no arthritis
wordsIndividuals are read a list of words and are immediately asked to recall as many as they can.
wordsdelayAfter a delay and another task, individuals are again asked to recall as many of the words as they can.
animalsIndividuals are asked to name all of the animals they can think of.
msetIndicator of the matched set: 1, 2, ..., 219.
Details
A theory that NSAIDs reduce the risk of Alzheimer's disease has often been examined by comparing elderly people with and without arthritis, reasoning that many people with arthritis have consumed NSAIDs in quantity for a long period; see McGeer et al. (1996). This comparison does not ask a person with Alzheimer's disease to recall past use of NSAIDs.
Triples were matched for age, sex, education and mother's education.
Everyone is 75 years old or older.
Source
Simulated data with a script for obtaining the actual data from the Irish Longitudinal Study of Aging 2009-2011.
References
Irish Longitudinal Study of Aging. http://tilda.tcd.ie/
McGeer, P. L., Schulzer, M., and McGeer, E. G. (1996). Arthritis and anti- inflammatory agents as possible protective factors for alzheimer's disease. Neurology, 47, 425-432.
Rosenbaum, P. R. (2017) Combining planned and discovered comparisons in observational studies. Manuscript. (The artcog example is discussed in this manuscript.)
Examples
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 | # data(artcog) returns the simulated example.
data(artcog)
# Three correlated outcomes.
cor(artcog[,2:4])
# See documentation for principal() for use of this example.
# The code below constructs the actual data, as distinct
# from the simulated example. The lengthy list of numbers
# assembles the 219 matched triples, or 657 = 3*219 rows,
# from the larger TILDA data set.
## Not run:
# Obtain from ICPSR the R data file
# ICPSR_34315-1IrishAging/34315-0001-Data.rda
# http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315?q=34315
# The data should have 8504 rows and 1992 columns
d<-da34315.0001
attach(d)
wordsC<-PH118
wordsI<-PH119
wordsC[wordsC<0]<-0
wordsI[wordsI<0]<-0
words<-wordsC+wordsI
wordsdelayC<-PH712
wordsdelayC[is.na(wordsdelayC)]<-0
wordsdelayC[wordsdelayC<=-1]<-0
wordsdelayI<-PH713
wordsdelayI[is.na(wordsdelayI)]<-0
wordsdelayI[wordsdelayI<=-1]<-0
wordsdelay<-wordsdelayC+wordsdelayI
animals<-PH125
arthritis<-PH301_03
osteoA<-PH304_1
z<-rep(NA,dim(d)[1])
z[arthritis==0]<-0
z[(arthritis==1)&(osteoA==1)]<-1
detach(d)
artcog<-data.frame(z,words,wordsdelay,animals)
who <- c(91, 4408, 7754, 129, 4716, 8383, 135, 6066,
8028, 280, 894, 5300, 288, 151, 667, 298, 4889, 5977,
333, 1100, 3707, 480, 696, 8148, 568, 372,
7578, 584, 1852, 8057, 589, 3799, 6567, 590, 7422,
8419, 609, 2825, 8272, 669, 1197, 8471, 684, 141,
1847, 687, 2416, 7591, 771, 5239, 6986, 782,
4857, 7654, 850, 885, 2239, 892, 2717, 7788, 929, 248,
4740, 975, 1965, 8242, 1036, 6459, 7973, 1059, 1541,
5901, 1103, 6518, 8264, 1160, 4798, 7330,
1168, 4678, 7319, 1180, 152, 2735, 1191, 3740,
7260, 1199, 26, 5209, 1252, 2615, 3251, 1444, 4790,
7298, 1549, 898, 7630, 1587, 4418, 7122, 1596, 5875,
8489, 1604, 3594, 7246, 1614, 3189, 7052, 1646,
5415, 6828, 1708, 1634, 7029, 1760, 1950, 7815, 1840,
5860, 8334, 1843, 6054, 7331, 1849, 5617, 8046, 1854,
2890, 7703, 1885, 5846, 7247, 1896, 4365, 7803,
1898, 3952, 4187, 1977, 544, 940, 1987, 768, 960,
2029, 5363, 6293, 2161, 10, 4432, 2270, 5620, 7132,
2330, 445, 1301, 2372, 1014, 1138, 2379, 3906,
6183, 2386, 6226, 7203, 2417, 2458, 6616, 2437, 6262,
7178, 2442, 3840, 8024, 2443, 4955, 5834, 2455, 1969,
5967, 2457, 6962, 7560, 2466, 986, 2895, 2498, 2461,
5876, 2522, 1837, 4803, 2618, 7279, 7764, 2734, 4005,
4477, 2747, 221, 3837, 2763, 4440, 7863, 2765,
6173, 7377, 2799, 7711, 7822, 2820, 2676, 7288, 2853,
3035, 7518, 2914, 3142, 6891, 2952, 3081, 4908, 2969,
3077, 6837, 3013, 747, 7614, 3107, 1754, 6564,
3178, 2242, 4377, 3192, 260, 4530, 3246, 3019, 6478,
3313, 4710, 7271, 3389, 356, 1796, 3481, 99, 491,
3571, 658, 1410, 3693, 4341, 7624, 3694, 522,
7702, 3704, 6532, 7171, 3705, 4973, 7131, 3806, 2163,
5400, 3848, 4811, 7097, 3850, 2154, 5773, 3851, 3547,
7613, 3862, 3357, 3370, 3877, 6186, 7990, 3913,
455, 2883, 3931, 3548, 3699, 3933, 3210, 6164, 3935,
4712, 7813, 3940, 5598, 7826, 3964, 2129, 8005, 3997,
49, 1537, 4000, 3915, 5392, 4044, 3014, 6130,
4052, 5208, 7213, 4186, 1586, 4249, 4264, 7058, 7182,
4324, 3950, 7507, 4343, 3701, 6359, 4358, 567, 1020,
4387, 2919, 4011, 4389, 5851, 7125, 4409, 3310,
8100, 4427, 767, 2108, 4439, 1263, 6024, 4447, 3814,
8373, 4478, 3493, 6743, 4479, 939, 2621, 4537, 1264,
7942, 4608, 1797, 2987, 4633, 976, 1814, 4641,
274, 1116, 4697, 4718, 7008, 4750, 2842, 5787, 4791,
4386, 6966, 4812, 2817, 5640, 4815, 845, 5430, 4856,
2288, 2289, 4887, 2182, 4874, 4942, 460, 4300,
4945, 565, 3644, 4946, 487, 3369, 4953, 4352, 6709,
4956, 2731, 3387, 4958, 4436, 6460, 4964, 3388, 6692,
5078, 278, 963, 5110, 842, 4842, 5166, 600,
1530, 5199, 1775, 6210, 5204, 4993, 8477, 5210, 2646,
5563, 5291, 2957, 7777, 5325, 4881, 7053, 5342, 4385,
6444, 5377, 3957, 4319, 5384, 3144, 7757, 5385,
2813, 3054, 5386, 3636, 6185, 5474, 2507, 5085, 5488,
4278, 5675, 5584, 2606, 5359, 5599, 3180, 7037, 5605,
2459, 5304, 5637, 2581, 3621, 5641, 2781, 4302,
5805, 6424, 7227, 5870, 492, 5847, 5909, 1750, 5158,
5923, 3199, 6492, 6039, 4347, 4762, 6048, 5332, 7320,
6080, 1992, 2830, 6091, 5213, 7045, 6099, 5167,
6511, 6135, 5177, 6944, 6172, 2983, 6455, 6176, 2319,
3737, 6189, 5525, 7257, 6196, 4423, 6893, 6256, 2639,
5740, 6322, 1427, 2435, 6370, 7321, 7385, 6371,
1212, 2423, 6417, 205, 1674, 6462, 2393, 2882, 6463,
2170, 4765, 6496, 1630, 5048, 6519, 3058, 7498, 6901,
5237, 7508, 6984, 3819, 6548, 7042, 2961, 6445,
7057, 5457, 7984, 7061, 5401, 6049, 7093, 502, 3847,
7094, 4717, 6348, 7096, 825, 7844, 7099, 2188, 8337,
7251, 7423, 7576, 7269, 2616, 6401, 7270, 2394,
5039, 7273, 2337, 4941, 7300, 2241, 4934, 7316, 2604,
6369, 7355, 2113, 3880, 7402, 1456, 2378, 7473, 6368,
7243, 7592, 5583, 7892, 7615, 855, 6924, 7684,
6412, 6822, 7852, 405, 7077, 7862, 3623, 3990, 7879,
2447, 6334, 7913, 3927, 5299, 7930, 5289, 5844, 7983,
297, 7772, 8006, 3869, 6930, 8009, 2729, 6480,
8081, 4700, 6560, 8109, 62, 8061, 8130, 3351, 4381,
8149, 2854, 6513, 8157, 5220, 7559, 8184, 1523, 4195,
8185, 1459, 3820, 8188, 117, 1050, 8206, 2513,
3954, 8335, 2352, 4435, 8346, 5109, 8207)
artcog<-artcog[who,]
mset<-as.numeric(gl(219,3))
artcog<-cbind(artcog,mset)
rm(z,words,wordsdelay,animals,mset)
## End(Not run)
|
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