relapse: Drug-relapse of patients with time-varying covariates
In OIsurv: Survival analysis supplement to OpenIntro guide
Description
Usage
Format
Details
Source
References
Examples
Description
Drug-relapse of patients with time-varying covariates. This data set is simulated.
Usage
1
Format
A data frame with 300 observations on the following 4 variables.
eventa numeric vector
deltaa logical vector
gendera numeric vector
intera numeric vector
Details
This data is simulated under the following pretense. Patient records were obtained for 150 days after they joined a rehabilitation program. The event of interest was drug-relapse and two covariates were recorded. The event variable describes the observed or censored time; the delta variable describes whether the time denotes an observed relapse (TRUE) or a censored time; the gender variable is a time-independent covariate; and inter is a time-dependent covariate indicating whether the patient had was (randomly) assigned a second intervention: working 10 hours a week for a nonprofit. Each of these special interventions were assigned after the patients entered the clinic, meaning the intervention covariate changes for those patients who had an intervention before relapse.
Source
Simulated (David M Diez)
References
Fox J (2002). "Cox Proportional-Hazards Regression for Survival Data. Appendix to An R and S-PLUS Companion to Applied Regression." Comprehensive R Archive Network.
http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf
Examples
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#=====> 10. Cox PH model, time-dependent covariates <=====#
data(relapse)
relapse
attach(relapse)
N <- dim(relapse)[1]
t1 <- rep(0, N+sum(!is.na(inter))) # Initialize start times at 0
t2 <- rep(NA, length(t1)) # The end times for each record
e <- rep(NA, length(t1)) # Was the event censored?
g <- rep(NA, length(t1)) # Gender
PI <- rep(FALSE, length(t1)) # Initialize intervention at FALSE
R <- 1 # Row of new record
for(ii in 1:dim(relapse)[1]){
if(is.na(inter[ii])){ # no intervention, copy survival record
t2[R] <- event[ii]
e[R] <- delta[ii]
g[R] <- gender[ii]
R <- R+1
} else { # intervention, split records
g[R+0:1] <- gender[ii] # gender is same for each time
e[R] <- 0 # no relapse observed pre-intervention
e[R+1] <- delta[ii] # relapse occur post-intervention?
PI[R+1] <- TRUE # Intervention covariate, post-intervention
t2[R] <- inter[ii]-1 # End of pre-intervention
t1[R+1] <- inter[ii]-1 # Start of post-intervention
t2[R+1] <- event[ii] # End of post-intervention
R <- R+2 # Two records added
}
}
mySurv <- Surv(t1, t2, e)
coxphFit <- coxph(mySurv ~ g + PI)
detach(relapse)
Example output
Loading required package: survival
Loading required package: KMsurv
event delta gender inter
1 150 FALSE 0 84
2 53 TRUE 1 50
3 12 TRUE 1 NA
4 150 FALSE 0 89
5 150 FALSE 1 77
6 135 TRUE 1 7
7 150 FALSE 0 21
8 57 TRUE 1 NA
9 65 TRUE 0 NA
10 92 TRUE 1 6
11 16 TRUE 1 10
12 42 TRUE 0 NA
13 76 TRUE 1 NA
14 123 TRUE 0 15
15 150 FALSE 0 69
16 150 FALSE 1 6
17 120 TRUE 1 61
18 111 TRUE 0 46
19 58 TRUE 0 NA
20 150 FALSE 0 19
21 121 TRUE 1 66
22 150 FALSE 0 18
23 150 FALSE 1 27
24 62 TRUE 0 11
25 150 FALSE 0 41
26 150 FALSE 0 23
27 76 TRUE 0 65
28 2 TRUE 0 NA
29 141 TRUE 1 11
30 150 FALSE 0 8
31 150 FALSE 0 20
32 30 TRUE 0 NA
33 15 TRUE 1 NA
34 103 TRUE 1 40
35 37 TRUE 1 3
36 150 FALSE 1 6
37 30 TRUE 1 12
38 33 TRUE 0 NA
39 150 FALSE 1 85
40 21 TRUE 0 NA
41 150 FALSE 1 87
42 77 TRUE 0 NA
43 63 TRUE 0 NA
44 133 TRUE 0 3
45 66 TRUE 1 53
46 150 FALSE 1 37
47 139 TRUE 1 64
48 75 TRUE 0 NA
49 37 TRUE 1 20
50 19 TRUE 1 NA
51 5 TRUE 0 NA
52 88 TRUE 0 60
53 150 FALSE 1 41
54 150 FALSE 1 88
55 75 TRUE 0 19
56 33 TRUE 1 NA
57 103 TRUE 1 80
58 150 FALSE 1 61
59 150 FALSE 1 7
60 150 FALSE 1 70
61 19 TRUE 1 16
62 57 TRUE 1 21
63 90 TRUE 1 12
64 111 TRUE 0 3
65 30 TRUE 1 NA
66 150 FALSE 0 25
67 150 FALSE 0 14
68 150 FALSE 0 7
69 137 TRUE 0 71
70 150 FALSE 0 5
71 150 FALSE 0 89
72 150 FALSE 0 3
73 150 FALSE 0 52
74 150 FALSE 0 65
75 21 TRUE 0 NA
76 150 FALSE 1 67
77 150 FALSE 0 27
78 150 FALSE 1 100
79 150 FALSE 0 70
80 147 TRUE 1 7
81 33 TRUE 0 12
82 150 FALSE 1 49
83 150 FALSE 0 48
84 150 FALSE 0 28
85 65 TRUE 0 10
86 37 TRUE 1 NA
87 5 TRUE 1 NA
88 99 TRUE 0 55
89 150 FALSE 1 87
90 75 TRUE 0 NA
91 21 TRUE 1 NA
92 97 TRUE 0 93
93 126 TRUE 0 34
94 150 FALSE 1 17
95 38 TRUE 0 23
96 38 TRUE 0 13
97 9 TRUE 0 NA
98 150 FALSE 0 86
99 150 FALSE 0 24
100 27 TRUE 0 NA
101 73 TRUE 0 63
102 39 TRUE 0 NA
103 150 FALSE 1 17
104 68 TRUE 1 5
105 49 TRUE 0 NA
106 28 TRUE 1 NA
107 150 FALSE 1 60
108 1 TRUE 0 NA
109 90 TRUE 0 18
110 150 FALSE 0 81
111 90 TRUE 0 66
112 58 TRUE 1 NA
113 116 TRUE 0 46
114 150 FALSE 1 9
115 150 FALSE 1 37
116 12 TRUE 1 NA
117 28 TRUE 1 NA
118 24 TRUE 0 NA
119 21 TRUE 1 NA
120 150 FALSE 1 80
121 53 TRUE 0 NA
122 150 FALSE 0 2
123 15 TRUE 1 NA
124 66 TRUE 1 NA
125 20 TRUE 0 NA
126 150 FALSE 1 14
127 107 TRUE 1 9
128 79 TRUE 0 58
129 32 TRUE 1 NA
130 76 TRUE 0 8
131 37 TRUE 0 NA
132 51 TRUE 1 NA
133 27 TRUE 0 NA
134 19 TRUE 1 NA
135 60 TRUE 0 11
136 121 TRUE 1 84
137 21 TRUE 1 14
138 39 TRUE 1 NA
139 48 TRUE 0 NA
140 6 TRUE 0 NA
141 150 FALSE 0 66
142 111 TRUE 0 21
143 26 TRUE 1 NA
144 56 TRUE 0 56
145 3 TRUE 0 NA
146 21 TRUE 1 NA
147 150 FALSE 1 98
148 150 FALSE 0 57
149 120 TRUE 0 54
150 9 TRUE 0 NA
151 150 FALSE 0 69
152 150 FALSE 0 75
153 4 TRUE 1 NA
154 150 FALSE 0 15
155 150 FALSE 1 82
156 80 TRUE 0 NA
157 15 TRUE 1 NA
158 67 TRUE 0 NA
159 16 TRUE 0 NA
160 52 TRUE 0 NA
161 150 FALSE 1 95
162 150 FALSE 1 3
163 66 TRUE 0 4
164 150 FALSE 1 50
165 150 FALSE 0 28
166 126 TRUE 1 77
167 28 TRUE 0 NA
168 150 FALSE 1 75
169 50 TRUE 0 6
170 41 TRUE 1 NA
171 60 TRUE 1 16
172 150 FALSE 0 45
173 12 TRUE 0 NA
174 2 TRUE 1 NA
175 18 TRUE 0 NA
176 150 FALSE 1 80
177 56 TRUE 0 55
178 8 TRUE 0 4
179 34 TRUE 1 29
180 74 TRUE 1 NA
181 150 FALSE 1 48
182 88 TRUE 1 77
183 28 TRUE 1 NA
184 15 TRUE 1 NA
185 150 FALSE 0 89
186 1 TRUE 1 NA
187 150 FALSE 0 72
188 20 TRUE 1 NA
189 9 TRUE 0 NA
190 79 TRUE 0 NA
191 102 TRUE 0 30
192 49 TRUE 0 NA
193 20 TRUE 1 NA
194 75 TRUE 0 NA
195 23 TRUE 1 NA
196 150 FALSE 0 22
197 150 FALSE 0 33
198 56 TRUE 0 NA
199 150 FALSE 0 34
200 150 FALSE 0 57
201 9 TRUE 1 NA
202 107 TRUE 1 57
203 89 TRUE 1 35
204 150 FALSE 1 25
205 37 TRUE 0 NA
206 92 TRUE 1 74
207 34 TRUE 0 NA
208 150 FALSE 0 50
209 150 FALSE 0 81
210 150 FALSE 0 69
211 51 TRUE 1 NA
212 61 TRUE 1 11
213 27 TRUE 1 NA
214 150 FALSE 1 42
215 5 TRUE 1 NA
216 150 FALSE 0 60
217 1 TRUE 0 NA
218 58 TRUE 0 53
219 25 TRUE 0 NA
220 40 TRUE 0 NA
221 78 TRUE 0 NA
222 60 TRUE 1 7
223 136 TRUE 0 47
224 2 TRUE 0 NA
225 25 TRUE 1 NA
226 3 TRUE 1 NA
227 128 TRUE 1 4
228 133 TRUE 0 71
229 150 FALSE 1 27
230 108 TRUE 1 90
231 150 FALSE 1 10
232 40 TRUE 0 NA
233 6 TRUE 1 NA
234 62 TRUE 1 15
235 150 FALSE 1 69
236 9 TRUE 0 NA
237 13 TRUE 1 NA
238 150 FALSE 0 28
239 42 TRUE 0 NA
240 48 TRUE 0 NA
241 134 TRUE 1 2
242 28 TRUE 0 NA
243 150 TRUE 1 49
244 35 TRUE 1 NA
245 3 TRUE 1 NA
246 47 TRUE 1 4
247 4 TRUE 0 NA
248 29 TRUE 1 NA
249 22 TRUE 0 NA
250 55 TRUE 0 NA
251 7 TRUE 1 NA
252 39 TRUE 0 37
253 150 FALSE 0 50
254 40 TRUE 0 NA
255 93 TRUE 1 85
256 77 TRUE 0 51
257 18 TRUE 0 NA
258 150 FALSE 0 59
259 1 TRUE 0 NA
260 39 TRUE 1 NA
261 150 FALSE 1 98
262 50 TRUE 0 3
263 150 FALSE 1 75
264 19 TRUE 0 NA
265 31 TRUE 1 NA
266 150 FALSE 1 32
267 145 TRUE 0 28
268 150 FALSE 1 56
269 150 FALSE 1 92
270 150 FALSE 1 12
271 49 TRUE 1 NA
272 150 FALSE 1 59
273 150 FALSE 1 89
274 3 TRUE 0 NA
275 66 TRUE 1 51
276 37 TRUE 0 NA
277 150 FALSE 1 91
278 7 TRUE 0 2
279 41 TRUE 0 NA
280 150 FALSE 0 21
281 26 TRUE 0 14
282 63 TRUE 1 15
283 84 TRUE 1 73
284 150 FALSE 1 60
285 43 TRUE 0 NA
286 150 FALSE 1 76
287 38 TRUE 1 NA
288 53 TRUE 1 10
289 93 TRUE 1 NA
290 3 TRUE 0 NA
291 91 TRUE 0 37
292 25 TRUE 0 NA
293 150 FALSE 1 67
294 14 TRUE 0 5
295 150 FALSE 1 3
296 144 TRUE 0 75
297 150 FALSE 0 10
298 62 TRUE 0 24
299 10 TRUE 0 NA
300 94 TRUE 1 4
OIsurv documentation built on May 29, 2017, 11:03 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format Details Source References Examples
Description
Drug-relapse of patients with time-varying covariates. This data set is simulated.
Usage
1 |
Format
A data frame with 300 observations on the following 4 variables.
eventa numeric vector
deltaa logical vector
gendera numeric vector
intera numeric vector
Details
This data is simulated under the following pretense. Patient records were obtained for 150 days after they joined a rehabilitation program. The event of interest was drug-relapse and two covariates were recorded. The event variable describes the observed or censored time; the delta variable describes whether the time denotes an observed relapse (TRUE) or a censored time; the gender variable is a time-independent covariate; and inter is a time-dependent covariate indicating whether the patient had was (randomly) assigned a second intervention: working 10 hours a week for a nonprofit. Each of these special interventions were assigned after the patients entered the clinic, meaning the intervention covariate changes for those patients who had an intervention before relapse.
Source
Simulated (David M Diez)
References
Fox J (2002). "Cox Proportional-Hazards Regression for Survival Data. Appendix to An R and S-PLUS Companion to Applied Regression." Comprehensive R Archive Network. http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf
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 | #=====> 10. Cox PH model, time-dependent covariates <=====#
data(relapse)
relapse
attach(relapse)
N <- dim(relapse)[1]
t1 <- rep(0, N+sum(!is.na(inter))) # Initialize start times at 0
t2 <- rep(NA, length(t1)) # The end times for each record
e <- rep(NA, length(t1)) # Was the event censored?
g <- rep(NA, length(t1)) # Gender
PI <- rep(FALSE, length(t1)) # Initialize intervention at FALSE
R <- 1 # Row of new record
for(ii in 1:dim(relapse)[1]){
if(is.na(inter[ii])){ # no intervention, copy survival record
t2[R] <- event[ii]
e[R] <- delta[ii]
g[R] <- gender[ii]
R <- R+1
} else { # intervention, split records
g[R+0:1] <- gender[ii] # gender is same for each time
e[R] <- 0 # no relapse observed pre-intervention
e[R+1] <- delta[ii] # relapse occur post-intervention?
PI[R+1] <- TRUE # Intervention covariate, post-intervention
t2[R] <- inter[ii]-1 # End of pre-intervention
t1[R+1] <- inter[ii]-1 # Start of post-intervention
t2[R+1] <- event[ii] # End of post-intervention
R <- R+2 # Two records added
}
}
mySurv <- Surv(t1, t2, e)
coxphFit <- coxph(mySurv ~ g + PI)
detach(relapse)
|
Example output
Loading required package: survival
Loading required package: KMsurv
event delta gender inter
1 150 FALSE 0 84
2 53 TRUE 1 50
3 12 TRUE 1 NA
4 150 FALSE 0 89
5 150 FALSE 1 77
6 135 TRUE 1 7
7 150 FALSE 0 21
8 57 TRUE 1 NA
9 65 TRUE 0 NA
10 92 TRUE 1 6
11 16 TRUE 1 10
12 42 TRUE 0 NA
13 76 TRUE 1 NA
14 123 TRUE 0 15
15 150 FALSE 0 69
16 150 FALSE 1 6
17 120 TRUE 1 61
18 111 TRUE 0 46
19 58 TRUE 0 NA
20 150 FALSE 0 19
21 121 TRUE 1 66
22 150 FALSE 0 18
23 150 FALSE 1 27
24 62 TRUE 0 11
25 150 FALSE 0 41
26 150 FALSE 0 23
27 76 TRUE 0 65
28 2 TRUE 0 NA
29 141 TRUE 1 11
30 150 FALSE 0 8
31 150 FALSE 0 20
32 30 TRUE 0 NA
33 15 TRUE 1 NA
34 103 TRUE 1 40
35 37 TRUE 1 3
36 150 FALSE 1 6
37 30 TRUE 1 12
38 33 TRUE 0 NA
39 150 FALSE 1 85
40 21 TRUE 0 NA
41 150 FALSE 1 87
42 77 TRUE 0 NA
43 63 TRUE 0 NA
44 133 TRUE 0 3
45 66 TRUE 1 53
46 150 FALSE 1 37
47 139 TRUE 1 64
48 75 TRUE 0 NA
49 37 TRUE 1 20
50 19 TRUE 1 NA
51 5 TRUE 0 NA
52 88 TRUE 0 60
53 150 FALSE 1 41
54 150 FALSE 1 88
55 75 TRUE 0 19
56 33 TRUE 1 NA
57 103 TRUE 1 80
58 150 FALSE 1 61
59 150 FALSE 1 7
60 150 FALSE 1 70
61 19 TRUE 1 16
62 57 TRUE 1 21
63 90 TRUE 1 12
64 111 TRUE 0 3
65 30 TRUE 1 NA
66 150 FALSE 0 25
67 150 FALSE 0 14
68 150 FALSE 0 7
69 137 TRUE 0 71
70 150 FALSE 0 5
71 150 FALSE 0 89
72 150 FALSE 0 3
73 150 FALSE 0 52
74 150 FALSE 0 65
75 21 TRUE 0 NA
76 150 FALSE 1 67
77 150 FALSE 0 27
78 150 FALSE 1 100
79 150 FALSE 0 70
80 147 TRUE 1 7
81 33 TRUE 0 12
82 150 FALSE 1 49
83 150 FALSE 0 48
84 150 FALSE 0 28
85 65 TRUE 0 10
86 37 TRUE 1 NA
87 5 TRUE 1 NA
88 99 TRUE 0 55
89 150 FALSE 1 87
90 75 TRUE 0 NA
91 21 TRUE 1 NA
92 97 TRUE 0 93
93 126 TRUE 0 34
94 150 FALSE 1 17
95 38 TRUE 0 23
96 38 TRUE 0 13
97 9 TRUE 0 NA
98 150 FALSE 0 86
99 150 FALSE 0 24
100 27 TRUE 0 NA
101 73 TRUE 0 63
102 39 TRUE 0 NA
103 150 FALSE 1 17
104 68 TRUE 1 5
105 49 TRUE 0 NA
106 28 TRUE 1 NA
107 150 FALSE 1 60
108 1 TRUE 0 NA
109 90 TRUE 0 18
110 150 FALSE 0 81
111 90 TRUE 0 66
112 58 TRUE 1 NA
113 116 TRUE 0 46
114 150 FALSE 1 9
115 150 FALSE 1 37
116 12 TRUE 1 NA
117 28 TRUE 1 NA
118 24 TRUE 0 NA
119 21 TRUE 1 NA
120 150 FALSE 1 80
121 53 TRUE 0 NA
122 150 FALSE 0 2
123 15 TRUE 1 NA
124 66 TRUE 1 NA
125 20 TRUE 0 NA
126 150 FALSE 1 14
127 107 TRUE 1 9
128 79 TRUE 0 58
129 32 TRUE 1 NA
130 76 TRUE 0 8
131 37 TRUE 0 NA
132 51 TRUE 1 NA
133 27 TRUE 0 NA
134 19 TRUE 1 NA
135 60 TRUE 0 11
136 121 TRUE 1 84
137 21 TRUE 1 14
138 39 TRUE 1 NA
139 48 TRUE 0 NA
140 6 TRUE 0 NA
141 150 FALSE 0 66
142 111 TRUE 0 21
143 26 TRUE 1 NA
144 56 TRUE 0 56
145 3 TRUE 0 NA
146 21 TRUE 1 NA
147 150 FALSE 1 98
148 150 FALSE 0 57
149 120 TRUE 0 54
150 9 TRUE 0 NA
151 150 FALSE 0 69
152 150 FALSE 0 75
153 4 TRUE 1 NA
154 150 FALSE 0 15
155 150 FALSE 1 82
156 80 TRUE 0 NA
157 15 TRUE 1 NA
158 67 TRUE 0 NA
159 16 TRUE 0 NA
160 52 TRUE 0 NA
161 150 FALSE 1 95
162 150 FALSE 1 3
163 66 TRUE 0 4
164 150 FALSE 1 50
165 150 FALSE 0 28
166 126 TRUE 1 77
167 28 TRUE 0 NA
168 150 FALSE 1 75
169 50 TRUE 0 6
170 41 TRUE 1 NA
171 60 TRUE 1 16
172 150 FALSE 0 45
173 12 TRUE 0 NA
174 2 TRUE 1 NA
175 18 TRUE 0 NA
176 150 FALSE 1 80
177 56 TRUE 0 55
178 8 TRUE 0 4
179 34 TRUE 1 29
180 74 TRUE 1 NA
181 150 FALSE 1 48
182 88 TRUE 1 77
183 28 TRUE 1 NA
184 15 TRUE 1 NA
185 150 FALSE 0 89
186 1 TRUE 1 NA
187 150 FALSE 0 72
188 20 TRUE 1 NA
189 9 TRUE 0 NA
190 79 TRUE 0 NA
191 102 TRUE 0 30
192 49 TRUE 0 NA
193 20 TRUE 1 NA
194 75 TRUE 0 NA
195 23 TRUE 1 NA
196 150 FALSE 0 22
197 150 FALSE 0 33
198 56 TRUE 0 NA
199 150 FALSE 0 34
200 150 FALSE 0 57
201 9 TRUE 1 NA
202 107 TRUE 1 57
203 89 TRUE 1 35
204 150 FALSE 1 25
205 37 TRUE 0 NA
206 92 TRUE 1 74
207 34 TRUE 0 NA
208 150 FALSE 0 50
209 150 FALSE 0 81
210 150 FALSE 0 69
211 51 TRUE 1 NA
212 61 TRUE 1 11
213 27 TRUE 1 NA
214 150 FALSE 1 42
215 5 TRUE 1 NA
216 150 FALSE 0 60
217 1 TRUE 0 NA
218 58 TRUE 0 53
219 25 TRUE 0 NA
220 40 TRUE 0 NA
221 78 TRUE 0 NA
222 60 TRUE 1 7
223 136 TRUE 0 47
224 2 TRUE 0 NA
225 25 TRUE 1 NA
226 3 TRUE 1 NA
227 128 TRUE 1 4
228 133 TRUE 0 71
229 150 FALSE 1 27
230 108 TRUE 1 90
231 150 FALSE 1 10
232 40 TRUE 0 NA
233 6 TRUE 1 NA
234 62 TRUE 1 15
235 150 FALSE 1 69
236 9 TRUE 0 NA
237 13 TRUE 1 NA
238 150 FALSE 0 28
239 42 TRUE 0 NA
240 48 TRUE 0 NA
241 134 TRUE 1 2
242 28 TRUE 0 NA
243 150 TRUE 1 49
244 35 TRUE 1 NA
245 3 TRUE 1 NA
246 47 TRUE 1 4
247 4 TRUE 0 NA
248 29 TRUE 1 NA
249 22 TRUE 0 NA
250 55 TRUE 0 NA
251 7 TRUE 1 NA
252 39 TRUE 0 37
253 150 FALSE 0 50
254 40 TRUE 0 NA
255 93 TRUE 1 85
256 77 TRUE 0 51
257 18 TRUE 0 NA
258 150 FALSE 0 59
259 1 TRUE 0 NA
260 39 TRUE 1 NA
261 150 FALSE 1 98
262 50 TRUE 0 3
263 150 FALSE 1 75
264 19 TRUE 0 NA
265 31 TRUE 1 NA
266 150 FALSE 1 32
267 145 TRUE 0 28
268 150 FALSE 1 56
269 150 FALSE 1 92
270 150 FALSE 1 12
271 49 TRUE 1 NA
272 150 FALSE 1 59
273 150 FALSE 1 89
274 3 TRUE 0 NA
275 66 TRUE 1 51
276 37 TRUE 0 NA
277 150 FALSE 1 91
278 7 TRUE 0 2
279 41 TRUE 0 NA
280 150 FALSE 0 21
281 26 TRUE 0 14
282 63 TRUE 1 15
283 84 TRUE 1 73
284 150 FALSE 1 60
285 43 TRUE 0 NA
286 150 FALSE 1 76
287 38 TRUE 1 NA
288 53 TRUE 1 10
289 93 TRUE 1 NA
290 3 TRUE 0 NA
291 91 TRUE 0 37
292 25 TRUE 0 NA
293 150 FALSE 1 67
294 14 TRUE 0 5
295 150 FALSE 1 3
296 144 TRUE 0 75
297 150 FALSE 0 10
298 62 TRUE 0 24
299 10 TRUE 0 NA
300 94 TRUE 1 4
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