Unanchored MAIC for binary and time-to-event endpoint
Source:R/maic_unanchored.R
maic_unanchored.RdThis is a wrapper function to provide adjusted effect estimates and relevant statistics in unanchored case (i.e. there is no common comparator arm in the internal and external trial).
Usage
maic_unanchored(
weights_object,
ipd,
pseudo_ipd,
trt_ipd,
trt_agd,
trt_var_ipd = "ARM",
trt_var_agd = "ARM",
normalize_weights = FALSE,
endpoint_type = "tte",
endpoint_name = "Time to Event Endpoint",
eff_measure = c("HR", "OR", "RR", "RD"),
boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
time_scale = "months",
km_conf_type = "log-log",
binary_robust_cov_type = "HC3"
)Arguments
- weights_object
an object returned by
estimate_weight- ipd
a data frame that meet format requirements in 'Details', individual patient data (IPD) of internal trial
- pseudo_ipd
a data frame, pseudo IPD from digitized KM curve of external trial (for time-to-event endpoint) or from contingency table (for binary endpoint)
- trt_ipd
a string, name of the interested investigation arm in internal trial
dat_igd(real IPD)- trt_agd
a string, name of the interested investigation arm in external trial
pseudo_ipd(pseudo IPD)- trt_var_ipd
a string, column name in
ipdthat contains the treatment assignment- trt_var_agd
a string, column name in
ipdthat contains the treatment assignment- normalize_weights
logical, default is
FALSE. IfTRUE,scaled_weights(normalized weights) inweights_object$datawill be used.- endpoint_type
a string, one out of the following "binary", "tte" (time to event)
- endpoint_name
a string, name of time to event endpoint, to be show in the last line of title
- eff_measure
a string, "RD" (risk difference), "OR" (odds ratio), "RR" (relative risk) for a binary endpoint; "HR" for a time-to-event endpoint. By default is
NULL, "OR" is used for binary case, otherwise "HR" is used.- boot_ci_type
a string, one of
c("norm","basic", "stud", "perc", "bca")to select the type of bootstrap confidence interval. See boot::boot.ci for more details.- time_scale
a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'. NOTE: it is assumed that values in TIME column of
ipdandpseudo_ipdis in the unit of days- km_conf_type
a string, pass to
conf.typeofsurvfit- binary_robust_cov_type
a string to pass to argument
typeof sandwich::vcovHC, see possible options in the documentation of that function. Default is"HC3"
Details
For time-to-event analysis, it is required that input ipd and pseudo_ipd to have the following
columns. This function is not sensitive to upper or lower case of letters in column names.
USUBJID - character, unique subject ID
ARM - character or factor, treatment indicator, column name does not have to be 'ARM'. User specify in
trt_var_ipdandtrt_var_agdEVENT - numeric, 1 for censored/death, 0 for otherwise
TIME - numeric column, observation time of the
EVENT; unit in days
Examples
#
# unanchored example using maic_unanchored for time-to-event data
#
data(centered_ipd_sat)
data(adtte_sat)
data(pseudo_ipd_sat)
#### derive weights
weighted_data <- estimate_weights(
data = centered_ipd_sat,
centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
start_val = 0,
method = "BFGS"
)
weighted_data2 <- estimate_weights(
data = centered_ipd_sat,
centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
start_val = 0,
method = "BFGS",
n_boot_iteration = 100,
set_seed_boot = 1234
)
# inferential result
result <- maic_unanchored(
weights_object = weighted_data,
ipd = adtte_sat,
pseudo_ipd = pseudo_ipd_sat,
trt_var_ipd = "ARM",
trt_var_agd = "ARM",
trt_ipd = "A",
trt_agd = "B",
endpoint_name = "Overall Survival",
endpoint_type = "tte",
eff_measure = "HR",
time_scale = "month",
km_conf_type = "log-log"
)
result$descriptive$summary
#> trt_ind treatment type records n.max n.start events
#> 1 B B Before matching 300 300.0000 300.0000 178.00000
#> 2 A A Before matching 500 500.0000 500.0000 190.00000
#> 3 B B After matching 300 300.0000 300.0000 178.00000
#> 4 A A After matching 500 173.3137 173.3137 55.37392
#> rmean se(rmean) median 0.95LCL 0.95UCL
#> 1 4.303551 0.3367260 2.746131 2.261125 3.320857
#> 2 8.709690 0.3551477 7.587627 6.278691 10.288538
#> 3 4.303551 0.3367260 2.746131 2.261125 3.320857
#> 4 10.584605 0.5739799 12.166430 10.244293 NA
result$inferential$summary
#> case HR LCL UCL pval
#> 1 AB 0.3748981 0.3039010 0.4624815 5.245204e-20
#> 2 adjusted_AB 0.2622843 0.1815542 0.3789120 1.001235e-12
result_boot <- maic_unanchored(
weights_object = weighted_data2,
ipd = adtte_sat,
pseudo_ipd = pseudo_ipd_sat,
trt_var_ipd = "ARM",
trt_var_agd = "ARM",
trt_ipd = "A",
trt_agd = "B",
endpoint_name = "Overall Survival",
endpoint_type = "tte",
eff_measure = "HR",
time_scale = "month",
km_conf_type = "log-log"
)
result$descriptive$summary
#> trt_ind treatment type records n.max n.start events
#> 1 B B Before matching 300 300.0000 300.0000 178.00000
#> 2 A A Before matching 500 500.0000 500.0000 190.00000
#> 3 B B After matching 300 300.0000 300.0000 178.00000
#> 4 A A After matching 500 173.3137 173.3137 55.37392
#> rmean se(rmean) median 0.95LCL 0.95UCL
#> 1 4.303551 0.3367260 2.746131 2.261125 3.320857
#> 2 8.709690 0.3551477 7.587627 6.278691 10.288538
#> 3 4.303551 0.3367260 2.746131 2.261125 3.320857
#> 4 10.584605 0.5739799 12.166430 10.244293 NA
result$inferential$summary
#> case HR LCL UCL pval
#> 1 AB 0.3748981 0.3039010 0.4624815 5.245204e-20
#> 2 adjusted_AB 0.2622843 0.1815542 0.3789120 1.001235e-12
#
# unanchored example using maic_unanchored for binary outcome
#
data(centered_ipd_sat)
data(adrs_sat)
centered_ipd_sat
#> USUBJID ARM AGE SEX SMOKE ECOG0 N_PR_THER SEX_MALE AGE_CENTERED
#> 1 1 A 45 Male 0 0 4 1 -6
#> 2 2 A 71 Male 0 0 3 1 20
#> 3 3 A 58 Male 1 1 2 1 7
#> 4 4 A 48 Female 0 1 4 0 -3
#> 5 5 A 69 Male 0 1 4 1 18
#> 6 6 A 48 Female 0 1 4 0 -3
#> 7 7 A 47 Male 1 0 3 1 -4
#> 8 8 A 61 Male 1 0 1 1 10
#> 9 9 A 54 Female 1 1 1 0 3
#> 10 10 A 56 Female 1 0 3 0 5
#> 11 11 A 63 Female 0 0 4 0 12
#> 12 12 A 50 Female 0 0 1 0 -1
#> 13 13 A 57 Male 0 1 3 1 6
#> 14 14 A 62 Female 1 1 1 0 11
#> 15 15 A 57 Female 0 1 3 0 6
#> 16 16 A 66 Male 0 0 2 1 15
#> 17 17 A 75 Male 1 1 3 1 24
#> 18 18 A 47 Female 0 0 4 0 -4
#> 19 19 A 57 Male 0 0 3 1 6
#> 20 20 A 54 Male 0 0 3 1 3
#> 21 21 A 55 Male 1 0 3 1 4
#> 22 22 A 64 Male 0 1 3 1 13
#> 23 23 A 53 Female 1 0 3 0 2
#> 24 24 A 58 Male 1 1 2 1 7
#> 25 25 A 47 Male 0 0 1 1 -4
#> 26 26 A 60 Female 1 0 1 0 9
#> 27 27 A 49 Female 0 1 3 0 -2
#> 28 28 A 55 Female 0 0 1 0 4
#> 29 29 A 66 Female 0 1 2 0 15
#> 30 30 A 58 Male 0 1 4 1 7
#> 31 31 A 49 Male 0 1 4 1 -2
#> 32 32 A 61 Male 0 0 4 1 10
#> 33 33 A 66 Male 1 0 3 1 15
#> 34 34 A 45 Male 0 0 1 1 -6
#> 35 35 A 59 Female 1 1 2 0 8
#> 36 36 A 74 Female 1 0 4 0 23
#> 37 37 A 73 Female 0 0 3 0 22
#> 38 38 A 74 Male 0 1 4 1 23
#> 39 39 A 54 Male 0 0 1 1 3
#> 40 40 A 58 Female 1 1 1 0 7
#> 41 41 A 61 Female 0 1 3 0 10
#> 42 42 A 47 Female 1 1 2 0 -4
#> 43 43 A 73 Female 1 1 2 0 22
#> 44 44 A 68 Male 0 0 1 1 17
#> 45 45 A 49 Female 0 0 3 0 -2
#> 46 46 A 71 Female 0 0 2 0 20
#> 47 47 A 70 Male 0 1 4 1 19
#> 48 48 A 62 Female 1 0 1 0 11
#> 49 49 A 49 Male 0 0 1 1 -2
#> 50 50 A 74 Female 0 0 1 0 23
#> 51 51 A 46 Female 0 1 3 0 -5
#> 52 52 A 68 Female 1 0 3 0 17
#> 53 53 A 46 Male 1 0 2 1 -5
#> 54 54 A 75 Female 1 1 3 0 24
#> 55 55 A 47 Female 0 0 3 0 -4
#> 56 56 A 56 Male 0 1 3 1 5
#> 57 57 A 72 Female 0 0 3 0 21
#> 58 58 A 57 Male 1 1 4 1 6
#> 59 59 A 46 Male 0 0 1 1 -5
#> 60 60 A 56 Female 1 1 1 0 5
#> 61 61 A 73 Male 0 1 2 1 22
#> 62 62 A 60 Female 1 1 3 0 9
#> 63 63 A 75 Male 0 0 2 1 24
#> 64 64 A 69 Female 1 1 2 0 18
#> 65 65 A 47 Female 0 1 1 0 -4
#> 66 66 A 74 Male 0 0 4 1 23
#> 67 67 A 71 Female 0 1 1 0 20
#> 68 68 A 49 Female 1 1 1 0 -2
#> 69 69 A 68 Male 0 0 3 1 17
#> 70 70 A 49 Male 0 1 1 1 -2
#> 71 71 A 70 Male 0 1 1 1 19
#> 72 72 A 45 Female 0 0 2 0 -6
#> 73 73 A 47 Female 0 1 3 0 -4
#> 74 74 A 58 Male 0 1 3 1 7
#> 75 75 A 49 Female 0 1 4 0 -2
#> 76 76 A 68 Female 0 0 1 0 17
#> 77 77 A 60 Male 0 0 4 1 9
#> 78 78 A 45 Female 1 0 1 0 -6
#> 79 79 A 57 Female 0 0 1 0 6
#> 80 80 A 50 Female 0 1 1 0 -1
#> 81 81 A 63 Male 0 1 3 1 12
#> 82 82 A 47 Female 0 0 2 0 -4
#> 83 83 A 68 Female 0 1 4 0 17
#> 84 84 A 51 Male 0 0 4 1 0
#> 85 85 A 60 Male 0 0 1 1 9
#> 86 86 A 52 Female 1 0 4 0 1
#> 87 87 A 69 Male 1 1 1 1 18
#> 88 88 A 70 Female 0 1 4 0 19
#> 89 89 A 72 Male 0 0 2 1 21
#> 90 90 A 46 Female 0 1 1 0 -5
#> 91 91 A 51 Male 1 0 4 1 0
#> 92 92 A 69 Female 0 1 1 0 18
#> 93 93 A 66 Male 0 0 1 1 15
#> 94 94 A 73 Male 0 1 1 1 22
#> 95 95 A 73 Female 1 0 3 0 22
#> 96 96 A 62 Female 0 1 2 0 11
#> 97 97 A 55 Female 0 0 4 0 4
#> 98 98 A 67 Male 1 0 3 1 16
#> 99 99 A 54 Female 1 0 3 0 3
#> 100 100 A 52 Female 1 1 4 0 1
#> 101 101 A 57 Male 0 0 2 1 6
#> 102 102 A 57 Female 1 1 3 0 6
#> 103 103 A 57 Male 0 0 3 1 6
#> 104 104 A 67 Female 1 1 2 0 16
#> 105 105 A 67 Female 1 1 2 0 16
#> 106 106 A 74 Female 1 1 2 0 23
#> 107 107 A 72 Female 1 0 2 0 21
#> 108 108 A 73 Female 0 0 3 0 22
#> 109 109 A 57 Female 0 0 4 0 6
#> 110 110 A 69 Female 1 0 1 0 18
#> 111 111 A 55 Male 0 0 1 1 4
#> 112 112 A 74 Female 0 0 4 0 23
#> 113 113 A 68 Female 0 0 4 0 17
#> 114 114 A 53 Male 0 0 2 1 2
#> 115 115 A 69 Male 0 0 2 1 18
#> 116 116 A 68 Male 0 1 2 1 17
#> 117 117 A 58 Male 0 0 1 1 7
#> 118 118 A 64 Female 0 0 3 0 13
#> 119 119 A 71 Male 0 0 1 1 20
#> 120 120 A 69 Female 0 1 2 0 18
#> 121 121 A 64 Female 1 0 4 0 13
#> 122 122 A 64 Male 1 0 1 1 13
#> 123 123 A 55 Male 0 1 3 1 4
#> 124 124 A 74 Male 0 0 3 1 23
#> 125 125 A 50 Male 0 1 3 1 -1
#> 126 126 A 68 Male 0 1 1 1 17
#> 127 127 A 60 Male 0 0 2 1 9
#> 128 128 A 59 Female 0 0 3 0 8
#> 129 129 A 71 Female 1 0 3 0 20
#> 130 130 A 69 Male 0 1 4 1 18
#> 131 131 A 56 Female 0 1 1 0 5
#> 132 132 A 51 Male 0 1 3 1 0
#> 133 133 A 65 Male 0 1 2 1 14
#> 134 134 A 45 Male 1 1 3 1 -6
#> 135 135 A 49 Female 0 0 2 0 -2
#> 136 136 A 74 Female 1 0 4 0 23
#> 137 137 A 71 Female 1 1 3 0 20
#> 138 138 A 65 Male 0 0 1 1 14
#> 139 139 A 67 Female 0 0 2 0 16
#> 140 140 A 48 Female 0 0 3 0 -3
#> 141 141 A 70 Female 0 1 4 0 19
#> 142 142 A 72 Female 0 0 1 0 21
#> 143 143 A 53 Male 1 1 4 1 2
#> 144 144 A 68 Female 1 0 3 0 17
#> 145 145 A 65 Male 0 1 1 1 14
#> 146 146 A 70 Female 1 1 4 0 19
#> 147 147 A 58 Male 0 0 2 1 7
#> 148 148 A 68 Female 0 1 4 0 17
#> 149 149 A 54 Female 0 0 4 0 3
#> 150 150 A 75 Female 1 1 4 0 24
#> 151 151 A 49 Female 1 1 3 0 -2
#> 152 152 A 60 Male 0 0 4 1 9
#> 153 153 A 45 Female 1 0 4 0 -6
#> 154 154 A 73 Female 1 0 3 0 22
#> 155 155 A 71 Female 0 1 3 0 20
#> 156 156 A 73 Female 0 0 3 0 22
#> 157 157 A 58 Female 0 0 4 0 7
#> 158 158 A 59 Female 0 1 2 0 8
#> 159 159 A 75 Female 0 1 2 0 24
#> 160 160 A 65 Male 0 0 2 1 14
#> 161 161 A 48 Male 1 0 2 1 -3
#> 162 162 A 63 Female 0 1 2 0 12
#> 163 163 A 74 Female 0 0 3 0 23
#> 164 164 A 64 Female 0 0 3 0 13
#> 165 165 A 49 Female 0 0 1 0 -2
#> 166 166 A 56 Female 0 1 4 0 5
#> 167 167 A 56 Female 0 1 2 0 5
#> 168 168 A 48 Female 0 0 1 0 -3
#> 169 169 A 65 Male 0 1 3 1 14
#> 170 170 A 53 Female 0 0 3 0 2
#> 171 171 A 72 Male 0 0 2 1 21
#> 172 172 A 75 Female 1 0 4 0 24
#> 173 173 A 70 Female 0 1 2 0 19
#> 174 174 A 53 Female 1 0 2 0 2
#> 175 175 A 45 Female 1 1 3 0 -6
#> 176 176 A 53 Female 1 1 3 0 2
#> 177 177 A 52 Female 0 1 2 0 1
#> 178 178 A 61 Female 1 0 4 0 10
#> 179 179 A 70 Male 0 0 3 1 19
#> 180 180 A 58 Female 0 0 3 0 7
#> 181 181 A 54 Male 0 0 1 1 3
#> 182 182 A 53 Male 0 0 2 1 2
#> 183 183 A 74 Male 0 0 2 1 23
#> 184 184 A 64 Male 0 1 3 1 13
#> 185 185 A 52 Male 0 0 1 1 1
#> 186 186 A 73 Female 0 0 3 0 22
#> 187 187 A 55 Female 1 0 4 0 4
#> 188 188 A 71 Female 0 0 3 0 20
#> 189 189 A 57 Female 1 0 3 0 6
#> 190 190 A 49 Female 1 0 2 0 -2
#> 191 191 A 69 Male 0 0 4 1 18
#> 192 192 A 74 Female 1 0 2 0 23
#> 193 193 A 59 Female 0 0 3 0 8
#> 194 194 A 53 Male 0 0 4 1 2
#> 195 195 A 52 Female 0 1 3 0 1
#> 196 196 A 47 Female 1 1 1 0 -4
#> 197 197 A 61 Female 0 0 4 0 10
#> 198 198 A 51 Female 0 0 2 0 0
#> 199 199 A 62 Female 1 0 1 0 11
#> 200 200 A 59 Female 1 0 2 0 8
#> 201 201 A 58 Male 0 0 2 1 7
#> 202 202 A 61 Female 0 1 4 0 10
#> 203 203 A 45 Female 0 0 1 0 -6
#> 204 204 A 59 Male 1 1 2 1 8
#> 205 205 A 58 Female 1 0 2 0 7
#> 206 206 A 67 Male 0 0 1 1 16
#> 207 207 A 51 Female 1 0 2 0 0
#> 208 208 A 68 Male 1 1 3 1 17
#> 209 209 A 53 Female 1 0 2 0 2
#> 210 210 A 64 Male 1 0 3 1 13
#> 211 211 A 61 Male 0 1 1 1 10
#> 212 212 A 52 Male 1 0 2 1 1
#> 213 213 A 57 Female 0 1 4 0 6
#> 214 214 A 57 Female 1 0 2 0 6
#> 215 215 A 49 Female 0 0 4 0 -2
#> 216 216 A 51 Male 1 1 3 1 0
#> 217 217 A 60 Female 0 0 2 0 9
#> 218 218 A 60 Female 1 0 1 0 9
#> 219 219 A 71 Female 0 0 3 0 20
#> 220 220 A 54 Female 0 0 1 0 3
#> 221 221 A 51 Male 1 0 1 1 0
#> 222 222 A 71 Male 0 1 3 1 20
#> 223 223 A 47 Male 0 0 2 1 -4
#> 224 224 A 65 Male 0 0 1 1 14
#> 225 225 A 53 Female 0 0 1 0 2
#> 226 226 A 56 Female 0 0 1 0 5
#> 227 227 A 51 Male 1 0 4 1 0
#> 228 228 A 68 Female 0 0 2 0 17
#> 229 229 A 75 Female 0 1 1 0 24
#> 230 230 A 49 Female 0 0 2 0 -2
#> 231 231 A 74 Male 1 0 1 1 23
#> 232 232 A 66 Female 0 0 1 0 15
#> 233 233 A 74 Female 0 0 1 0 23
#> 234 234 A 62 Female 0 1 2 0 11
#> 235 235 A 53 Female 0 0 1 0 2
#> 236 236 A 62 Female 0 1 1 0 11
#> 237 237 A 70 Female 1 0 3 0 19
#> 238 238 A 60 Female 1 0 2 0 9
#> 239 239 A 72 Male 0 0 1 1 21
#> 240 240 A 74 Female 1 0 3 0 23
#> 241 241 A 47 Female 1 0 3 0 -4
#> 242 242 A 54 Female 0 1 3 0 3
#> 243 243 A 65 Female 0 1 2 0 14
#> 244 244 A 74 Male 0 1 1 1 23
#> 245 245 A 61 Male 0 1 2 1 10
#> 246 246 A 54 Female 1 0 3 0 3
#> 247 247 A 65 Male 0 0 3 1 14
#> 248 248 A 72 Female 1 0 3 0 21
#> 249 249 A 71 Female 1 1 2 0 20
#> 250 250 A 65 Male 0 0 3 1 14
#> 251 251 A 58 Male 0 1 1 1 7
#> 252 252 A 46 Male 0 0 1 1 -5
#> 253 253 A 53 Female 1 0 4 0 2
#> 254 254 A 71 Female 1 0 4 0 20
#> 255 255 A 47 Male 0 1 3 1 -4
#> 256 256 A 45 Male 0 0 1 1 -6
#> 257 257 A 50 Male 0 0 3 1 -1
#> 258 258 A 67 Female 1 0 3 0 16
#> 259 259 A 72 Male 1 0 2 1 21
#> 260 260 A 45 Female 0 1 2 0 -6
#> 261 261 A 75 Female 1 0 1 0 24
#> 262 262 A 65 Male 0 0 3 1 14
#> 263 263 A 60 Female 0 1 3 0 9
#> 264 264 A 75 Female 0 1 4 0 24
#> 265 265 A 60 Female 1 0 4 0 9
#> 266 266 A 49 Female 1 0 2 0 -2
#> 267 267 A 58 Female 0 1 1 0 7
#> 268 268 A 57 Male 1 1 3 1 6
#> 269 269 A 69 Male 1 0 2 1 18
#> 270 270 A 51 Male 0 1 1 1 0
#> 271 271 A 54 Female 1 0 4 0 3
#> 272 272 A 55 Male 0 1 3 1 4
#> 273 273 A 49 Female 0 0 4 0 -2
#> 274 274 A 74 Female 1 0 1 0 23
#> 275 275 A 55 Male 1 0 1 1 4
#> 276 276 A 52 Female 1 0 1 0 1
#> 277 277 A 65 Male 0 0 2 1 14
#> 278 278 A 70 Female 1 0 1 0 19
#> 279 279 A 66 Female 1 1 2 0 15
#> 280 280 A 63 Female 0 1 4 0 12
#> 281 281 A 61 Female 0 1 3 0 10
#> 282 282 A 65 Male 0 1 2 1 14
#> 283 283 A 73 Male 0 0 2 1 22
#> 284 284 A 55 Female 1 1 4 0 4
#> 285 285 A 56 Female 1 1 4 0 5
#> 286 286 A 68 Female 0 1 1 0 17
#> 287 287 A 74 Female 1 0 4 0 23
#> 288 288 A 67 Female 0 0 2 0 16
#> 289 289 A 66 Male 0 1 3 1 15
#> 290 290 A 48 Female 0 0 3 0 -3
#> 291 291 A 49 Female 1 1 3 0 -2
#> 292 292 A 60 Female 1 1 1 0 9
#> 293 293 A 69 Female 0 1 4 0 18
#> 294 294 A 58 Female 0 1 3 0 7
#> 295 295 A 45 Female 1 1 4 0 -6
#> 296 296 A 49 Female 0 1 1 0 -2
#> 297 297 A 67 Female 1 0 4 0 16
#> 298 298 A 63 Male 0 0 4 1 12
#> 299 299 A 50 Female 0 0 2 0 -1
#> 300 300 A 68 Female 0 1 3 0 17
#> 301 301 A 53 Male 0 1 2 1 2
#> 302 302 A 63 Male 0 1 2 1 12
#> 303 303 A 58 Male 0 0 4 1 7
#> 304 304 A 70 Female 0 1 4 0 19
#> 305 305 A 56 Female 0 0 1 0 5
#> 306 306 A 56 Male 0 1 3 1 5
#> 307 307 A 61 Female 0 0 3 0 10
#> 308 308 A 72 Male 0 0 2 1 21
#> 309 309 A 51 Male 0 1 1 1 0
#> 310 310 A 72 Male 0 0 4 1 21
#> 311 311 A 64 Female 0 0 3 0 13
#> 312 312 A 59 Male 0 0 2 1 8
#> 313 313 A 75 Female 0 0 3 0 24
#> 314 314 A 75 Female 0 0 1 0 24
#> 315 315 A 74 Male 0 0 2 1 23
#> 316 316 A 54 Male 0 1 4 1 3
#> 317 317 A 55 Female 0 0 3 0 4
#> 318 318 A 52 Female 1 0 1 0 1
#> 319 319 A 46 Female 0 1 1 0 -5
#> 320 320 A 53 Male 0 0 1 1 2
#> 321 321 A 54 Female 0 1 1 0 3
#> 322 322 A 62 Female 0 0 2 0 11
#> 323 323 A 54 Male 1 0 4 1 3
#> 324 324 A 56 Female 0 0 4 0 5
#> 325 325 A 48 Female 0 0 1 0 -3
#> 326 326 A 52 Female 0 1 1 0 1
#> 327 327 A 55 Female 0 1 3 0 4
#> 328 328 A 69 Female 0 0 2 0 18
#> 329 329 A 48 Female 0 0 1 0 -3
#> 330 330 A 48 Female 1 1 3 0 -3
#> 331 331 A 60 Male 0 0 3 1 9
#> 332 332 A 74 Female 0 1 2 0 23
#> 333 333 A 45 Female 0 0 1 0 -6
#> 334 334 A 64 Male 1 0 1 1 13
#> 335 335 A 75 Female 1 1 3 0 24
#> 336 336 A 62 Female 0 0 3 0 11
#> 337 337 A 71 Male 0 0 4 1 20
#> 338 338 A 48 Female 1 1 3 0 -3
#> 339 339 A 53 Female 0 0 2 0 2
#> 340 340 A 62 Male 0 0 3 1 11
#> 341 341 A 69 Female 0 0 2 0 18
#> 342 342 A 72 Female 0 0 1 0 21
#> 343 343 A 61 Female 0 0 2 0 10
#> 344 344 A 47 Male 0 0 2 1 -4
#> 345 345 A 58 Male 0 1 1 1 7
#> 346 346 A 52 Female 0 0 4 0 1
#> 347 347 A 49 Female 0 0 4 0 -2
#> 348 348 A 51 Female 0 0 1 0 0
#> 349 349 A 51 Female 1 1 2 0 0
#> 350 350 A 72 Male 0 0 3 1 21
#> 351 351 A 68 Male 0 0 3 1 17
#> 352 352 A 49 Female 0 0 4 0 -2
#> 353 353 A 45 Female 0 0 3 0 -6
#> 354 354 A 49 Female 0 0 3 0 -2
#> 355 355 A 65 Male 0 1 2 1 14
#> 356 356 A 56 Male 0 0 2 1 5
#> 357 357 A 45 Female 1 1 1 0 -6
#> 358 358 A 57 Male 1 0 2 1 6
#> 359 359 A 53 Male 1 1 2 1 2
#> 360 360 A 65 Female 0 0 2 0 14
#> 361 361 A 57 Male 0 1 4 1 6
#> 362 362 A 55 Female 0 1 4 0 4
#> 363 363 A 57 Male 0 1 2 1 6
#> 364 364 A 46 Female 0 1 4 0 -5
#> 365 365 A 69 Female 0 1 1 0 18
#> 366 366 A 67 Female 0 1 3 0 16
#> 367 367 A 55 Male 0 0 1 1 4
#> 368 368 A 53 Female 0 1 3 0 2
#> 369 369 A 46 Female 0 0 3 0 -5
#> 370 370 A 71 Male 0 0 4 1 20
#> 371 371 A 68 Male 0 1 1 1 17
#> 372 372 A 49 Female 0 1 3 0 -2
#> 373 373 A 51 Female 0 0 3 0 0
#> 374 374 A 65 Female 1 1 3 0 14
#> 375 375 A 55 Female 0 0 4 0 4
#> 376 376 A 53 Male 0 0 4 1 2
#> 377 377 A 65 Female 0 1 4 0 14
#> 378 378 A 72 Female 1 1 4 0 21
#> 379 379 A 61 Male 0 1 1 1 10
#> 380 380 A 73 Female 0 0 1 0 22
#> 381 381 A 62 Female 1 1 2 0 11
#> 382 382 A 46 Male 0 0 2 1 -5
#> 383 383 A 51 Male 0 1 4 1 0
#> 384 384 A 60 Male 1 1 4 1 9
#> 385 385 A 56 Female 0 1 3 0 5
#> 386 386 A 69 Female 0 1 1 0 18
#> 387 387 A 58 Female 1 1 2 0 7
#> 388 388 A 58 Female 1 1 3 0 7
#> 389 389 A 53 Female 0 1 2 0 2
#> 390 390 A 47 Female 0 0 2 0 -4
#> 391 391 A 59 Male 0 1 2 1 8
#> 392 392 A 47 Female 0 0 4 0 -4
#> 393 393 A 60 Female 1 0 4 0 9
#> 394 394 A 73 Female 0 0 4 0 22
#> 395 395 A 60 Male 0 0 1 1 9
#> 396 396 A 75 Male 0 0 4 1 24
#> 397 397 A 65 Female 0 0 3 0 14
#> 398 398 A 68 Male 0 0 1 1 17
#> 399 399 A 55 Female 0 0 4 0 4
#> 400 400 A 46 Female 1 0 4 0 -5
#> 401 401 A 45 Female 0 1 1 0 -6
#> 402 402 A 70 Male 1 1 3 1 19
#> 403 403 A 56 Female 0 1 4 0 5
#> 404 404 A 62 Female 0 0 3 0 11
#> 405 405 A 49 Male 0 0 4 1 -2
#> 406 406 A 52 Female 0 0 4 0 1
#> 407 407 A 67 Female 0 1 4 0 16
#> 408 408 A 50 Female 1 1 1 0 -1
#> 409 409 A 68 Female 1 1 2 0 17
#> 410 410 A 54 Female 0 0 4 0 3
#> 411 411 A 65 Male 0 0 4 1 14
#> 412 412 A 55 Female 0 1 2 0 4
#> 413 413 A 53 Female 1 1 4 0 2
#> 414 414 A 71 Female 0 0 2 0 20
#> 415 415 A 48 Male 0 0 1 1 -3
#> 416 416 A 54 Female 0 1 3 0 3
#> 417 417 A 75 Male 1 0 3 1 24
#> 418 418 A 53 Female 0 0 2 0 2
#> 419 419 A 50 Female 1 0 4 0 -1
#> 420 420 A 64 Female 1 0 1 0 13
#> 421 421 A 65 Female 0 0 2 0 14
#> 422 422 A 65 Female 0 1 4 0 14
#> 423 423 A 60 Female 1 0 2 0 9
#> 424 424 A 70 Female 1 0 3 0 19
#> 425 425 A 51 Female 0 0 2 0 0
#> 426 426 A 45 Female 0 1 1 0 -6
#> 427 427 A 75 Female 1 0 2 0 24
#> 428 428 A 52 Female 1 0 1 0 1
#> 429 429 A 70 Male 0 0 4 1 19
#> 430 430 A 69 Female 1 1 3 0 18
#> 431 431 A 64 Female 0 0 2 0 13
#> 432 432 A 68 Female 1 0 1 0 17
#> 433 433 A 51 Male 1 0 1 1 0
#> 434 434 A 59 Female 0 1 2 0 8
#> 435 435 A 57 Female 0 0 1 0 6
#> 436 436 A 47 Male 0 0 2 1 -4
#> 437 437 A 65 Male 0 1 1 1 14
#> 438 438 A 65 Male 0 1 1 1 14
#> 439 439 A 65 Male 0 0 2 1 14
#> 440 440 A 46 Male 0 0 2 1 -5
#> 441 441 A 64 Female 0 0 3 0 13
#> 442 442 A 57 Female 0 1 4 0 6
#> 443 443 A 67 Female 0 1 3 0 16
#> 444 444 A 61 Female 1 0 3 0 10
#> 445 445 A 56 Male 0 0 4 1 5
#> 446 446 A 52 Male 0 0 3 1 1
#> 447 447 A 74 Female 1 1 3 0 23
#> 448 448 A 75 Male 0 1 3 1 24
#> 449 449 A 58 Male 0 1 3 1 7
#> 450 450 A 57 Female 0 1 4 0 6
#> 451 451 A 55 Female 0 0 1 0 4
#> 452 452 A 53 Female 1 0 1 0 2
#> 453 453 A 75 Male 1 0 4 1 24
#> 454 454 A 65 Female 0 1 3 0 14
#> 455 455 A 65 Female 0 0 4 0 14
#> 456 456 A 58 Male 0 0 4 1 7
#> 457 457 A 71 Female 0 0 3 0 20
#> 458 458 A 71 Male 0 0 4 1 20
#> 459 459 A 59 Male 1 0 4 1 8
#> 460 460 A 46 Male 1 0 4 1 -5
#> 461 461 A 51 Female 0 0 4 0 0
#> 462 462 A 56 Female 0 1 4 0 5
#> 463 463 A 66 Female 0 0 1 0 15
#> 464 464 A 59 Female 1 0 2 0 8
#> 465 465 A 48 Female 1 0 2 0 -3
#> 466 466 A 68 Female 0 0 4 0 17
#> 467 467 A 57 Female 0 0 4 0 6
#> 468 468 A 63 Male 0 0 4 1 12
#> 469 469 A 62 Male 0 1 3 1 11
#> 470 470 A 70 Female 0 0 3 0 19
#> 471 471 A 55 Female 0 1 2 0 4
#> 472 472 A 56 Male 1 0 3 1 5
#> 473 473 A 51 Male 1 0 1 1 0
#> 474 474 A 51 Female 0 0 4 0 0
#> 475 475 A 46 Female 1 1 3 0 -5
#> 476 476 A 52 Male 0 0 4 1 1
#> 477 477 A 71 Male 0 1 4 1 20
#> 478 478 A 54 Male 1 0 3 1 3
#> 479 479 A 55 Male 0 1 2 1 4
#> 480 480 A 46 Female 0 0 3 0 -5
#> 481 481 A 70 Female 0 0 1 0 19
#> 482 482 A 68 Female 0 1 3 0 17
#> 483 483 A 50 Male 0 1 1 1 -1
#> 484 484 A 45 Female 0 0 2 0 -6
#> 485 485 A 68 Male 0 0 1 1 17
#> 486 486 A 56 Male 0 1 2 1 5
#> 487 487 A 59 Male 0 1 3 1 8
#> 488 488 A 51 Male 1 1 1 1 0
#> 489 489 A 61 Female 1 0 3 0 10
#> 490 490 A 60 Female 0 1 3 0 9
#> 491 491 A 68 Female 1 0 2 0 17
#> 492 492 A 67 Male 1 1 4 1 16
#> 493 493 A 45 Male 1 1 2 1 -6
#> 494 494 A 71 Female 0 1 2 0 20
#> 495 495 A 55 Male 0 1 3 1 4
#> 496 496 A 72 Female 0 0 4 0 21
#> 497 497 A 48 Female 1 0 3 0 -3
#> 498 498 A 68 Female 1 0 1 0 17
#> 499 499 A 45 Female 0 0 1 0 -6
#> 500 500 A 58 Female 1 1 3 0 7
#> AGE_MEDIAN_CENTERED AGE_SQUARED_CENTERED SEX_MALE_CENTERED ECOG0_CENTERED
#> 1 -0.5 -586.5625 0.51 -0.35
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#> 134 -0.5 -586.5625 0.51 0.65
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#> 144 0.5 2012.4375 -0.49 -0.35
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#> 146 0.5 2288.4375 -0.49 0.65
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#> 348 0.5 -10.5625 -0.49 -0.35
#> 349 0.5 -10.5625 -0.49 0.65
#> 350 0.5 2572.4375 0.51 -0.35
#> 351 0.5 2012.4375 0.51 -0.35
#> 352 -0.5 -210.5625 -0.49 -0.35
#> 353 -0.5 -586.5625 -0.49 -0.35
#> 354 -0.5 -210.5625 -0.49 -0.35
#> 355 0.5 1613.4375 0.51 0.65
#> 356 0.5 524.4375 0.51 -0.35
#> 357 -0.5 -586.5625 -0.49 0.65
#> 358 0.5 637.4375 0.51 -0.35
#> 359 0.5 197.4375 0.51 0.65
#> 360 0.5 1613.4375 -0.49 -0.35
#> 361 0.5 637.4375 0.51 0.65
#> 362 0.5 413.4375 -0.49 0.65
#> 363 0.5 637.4375 0.51 0.65
#> 364 -0.5 -495.5625 -0.49 0.65
#> 365 0.5 2149.4375 -0.49 0.65
#> 366 0.5 1877.4375 -0.49 0.65
#> 367 0.5 413.4375 0.51 -0.35
#> 368 0.5 197.4375 -0.49 0.65
#> 369 -0.5 -495.5625 -0.49 -0.35
#> 370 0.5 2429.4375 0.51 -0.35
#> 371 0.5 2012.4375 0.51 0.65
#> 372 -0.5 -210.5625 -0.49 0.65
#> 373 0.5 -10.5625 -0.49 -0.35
#> 374 0.5 1613.4375 -0.49 0.65
#> 375 0.5 413.4375 -0.49 -0.35
#> 376 0.5 197.4375 0.51 -0.35
#> 377 0.5 1613.4375 -0.49 0.65
#> 378 0.5 2572.4375 -0.49 0.65
#> 379 0.5 1109.4375 0.51 0.65
#> 380 0.5 2717.4375 -0.49 -0.35
#> 381 0.5 1232.4375 -0.49 0.65
#> 382 -0.5 -495.5625 0.51 -0.35
#> 383 0.5 -10.5625 0.51 0.65
#> 384 0.5 988.4375 0.51 0.65
#> 385 0.5 524.4375 -0.49 0.65
#> 386 0.5 2149.4375 -0.49 0.65
#> 387 0.5 752.4375 -0.49 0.65
#> 388 0.5 752.4375 -0.49 0.65
#> 389 0.5 197.4375 -0.49 0.65
#> 390 -0.5 -402.5625 -0.49 -0.35
#> 391 0.5 869.4375 0.51 0.65
#> 392 -0.5 -402.5625 -0.49 -0.35
#> 393 0.5 988.4375 -0.49 -0.35
#> 394 0.5 2717.4375 -0.49 -0.35
#> 395 0.5 988.4375 0.51 -0.35
#> 396 0.5 3013.4375 0.51 -0.35
#> 397 0.5 1613.4375 -0.49 -0.35
#> 398 0.5 2012.4375 0.51 -0.35
#> 399 0.5 413.4375 -0.49 -0.35
#> 400 -0.5 -495.5625 -0.49 -0.35
#> 401 -0.5 -586.5625 -0.49 0.65
#> 402 0.5 2288.4375 0.51 0.65
#> 403 0.5 524.4375 -0.49 0.65
#> 404 0.5 1232.4375 -0.49 -0.35
#> 405 -0.5 -210.5625 0.51 -0.35
#> 406 0.5 92.4375 -0.49 -0.35
#> 407 0.5 1877.4375 -0.49 0.65
#> 408 0.5 -111.5625 -0.49 0.65
#> 409 0.5 2012.4375 -0.49 0.65
#> 410 0.5 304.4375 -0.49 -0.35
#> 411 0.5 1613.4375 0.51 -0.35
#> 412 0.5 413.4375 -0.49 0.65
#> 413 0.5 197.4375 -0.49 0.65
#> 414 0.5 2429.4375 -0.49 -0.35
#> 415 -0.5 -307.5625 0.51 -0.35
#> 416 0.5 304.4375 -0.49 0.65
#> 417 0.5 3013.4375 0.51 -0.35
#> 418 0.5 197.4375 -0.49 -0.35
#> 419 0.5 -111.5625 -0.49 -0.35
#> 420 0.5 1484.4375 -0.49 -0.35
#> 421 0.5 1613.4375 -0.49 -0.35
#> 422 0.5 1613.4375 -0.49 0.65
#> 423 0.5 988.4375 -0.49 -0.35
#> 424 0.5 2288.4375 -0.49 -0.35
#> 425 0.5 -10.5625 -0.49 -0.35
#> 426 -0.5 -586.5625 -0.49 0.65
#> 427 0.5 3013.4375 -0.49 -0.35
#> 428 0.5 92.4375 -0.49 -0.35
#> 429 0.5 2288.4375 0.51 -0.35
#> 430 0.5 2149.4375 -0.49 0.65
#> 431 0.5 1484.4375 -0.49 -0.35
#> 432 0.5 2012.4375 -0.49 -0.35
#> 433 0.5 -10.5625 0.51 -0.35
#> 434 0.5 869.4375 -0.49 0.65
#> 435 0.5 637.4375 -0.49 -0.35
#> 436 -0.5 -402.5625 0.51 -0.35
#> 437 0.5 1613.4375 0.51 0.65
#> 438 0.5 1613.4375 0.51 0.65
#> 439 0.5 1613.4375 0.51 -0.35
#> 440 -0.5 -495.5625 0.51 -0.35
#> 441 0.5 1484.4375 -0.49 -0.35
#> 442 0.5 637.4375 -0.49 0.65
#> 443 0.5 1877.4375 -0.49 0.65
#> 444 0.5 1109.4375 -0.49 -0.35
#> 445 0.5 524.4375 0.51 -0.35
#> 446 0.5 92.4375 0.51 -0.35
#> 447 0.5 2864.4375 -0.49 0.65
#> 448 0.5 3013.4375 0.51 0.65
#> 449 0.5 752.4375 0.51 0.65
#> 450 0.5 637.4375 -0.49 0.65
#> 451 0.5 413.4375 -0.49 -0.35
#> 452 0.5 197.4375 -0.49 -0.35
#> 453 0.5 3013.4375 0.51 -0.35
#> 454 0.5 1613.4375 -0.49 0.65
#> 455 0.5 1613.4375 -0.49 -0.35
#> 456 0.5 752.4375 0.51 -0.35
#> 457 0.5 2429.4375 -0.49 -0.35
#> 458 0.5 2429.4375 0.51 -0.35
#> 459 0.5 869.4375 0.51 -0.35
#> 460 -0.5 -495.5625 0.51 -0.35
#> 461 0.5 -10.5625 -0.49 -0.35
#> 462 0.5 524.4375 -0.49 0.65
#> 463 0.5 1744.4375 -0.49 -0.35
#> 464 0.5 869.4375 -0.49 -0.35
#> 465 -0.5 -307.5625 -0.49 -0.35
#> 466 0.5 2012.4375 -0.49 -0.35
#> 467 0.5 637.4375 -0.49 -0.35
#> 468 0.5 1357.4375 0.51 -0.35
#> 469 0.5 1232.4375 0.51 0.65
#> 470 0.5 2288.4375 -0.49 -0.35
#> 471 0.5 413.4375 -0.49 0.65
#> 472 0.5 524.4375 0.51 -0.35
#> 473 0.5 -10.5625 0.51 -0.35
#> 474 0.5 -10.5625 -0.49 -0.35
#> 475 -0.5 -495.5625 -0.49 0.65
#> 476 0.5 92.4375 0.51 -0.35
#> 477 0.5 2429.4375 0.51 0.65
#> 478 0.5 304.4375 0.51 -0.35
#> 479 0.5 413.4375 0.51 0.65
#> 480 -0.5 -495.5625 -0.49 -0.35
#> 481 0.5 2288.4375 -0.49 -0.35
#> 482 0.5 2012.4375 -0.49 0.65
#> 483 0.5 -111.5625 0.51 0.65
#> 484 -0.5 -586.5625 -0.49 -0.35
#> 485 0.5 2012.4375 0.51 -0.35
#> 486 0.5 524.4375 0.51 0.65
#> 487 0.5 869.4375 0.51 0.65
#> 488 0.5 -10.5625 0.51 0.65
#> 489 0.5 1109.4375 -0.49 -0.35
#> 490 0.5 988.4375 -0.49 0.65
#> 491 0.5 2012.4375 -0.49 -0.35
#> 492 0.5 1877.4375 0.51 0.65
#> 493 -0.5 -586.5625 0.51 0.65
#> 494 0.5 2429.4375 -0.49 0.65
#> 495 0.5 413.4375 0.51 0.65
#> 496 0.5 2572.4375 -0.49 -0.35
#> 497 -0.5 -307.5625 -0.49 -0.35
#> 498 0.5 2012.4375 -0.49 -0.35
#> 499 -0.5 -586.5625 -0.49 -0.35
#> 500 0.5 752.4375 -0.49 0.65
#> SMOKE_CENTERED N_PR_THER_MEDIAN_CENTERED
#> 1 -0.1933333 0.5
#> 2 -0.1933333 0.5
#> 3 0.8066667 -0.5
#> 4 -0.1933333 0.5
#> 5 -0.1933333 0.5
#> 6 -0.1933333 0.5
#> 7 0.8066667 0.5
#> 8 0.8066667 -0.5
#> 9 0.8066667 -0.5
#> 10 0.8066667 0.5
#> 11 -0.1933333 0.5
#> 12 -0.1933333 -0.5
#> 13 -0.1933333 0.5
#> 14 0.8066667 -0.5
#> 15 -0.1933333 0.5
#> 16 -0.1933333 -0.5
#> 17 0.8066667 0.5
#> 18 -0.1933333 0.5
#> 19 -0.1933333 0.5
#> 20 -0.1933333 0.5
#> 21 0.8066667 0.5
#> 22 -0.1933333 0.5
#> 23 0.8066667 0.5
#> 24 0.8066667 -0.5
#> 25 -0.1933333 -0.5
#> 26 0.8066667 -0.5
#> 27 -0.1933333 0.5
#> 28 -0.1933333 -0.5
#> 29 -0.1933333 -0.5
#> 30 -0.1933333 0.5
#> 31 -0.1933333 0.5
#> 32 -0.1933333 0.5
#> 33 0.8066667 0.5
#> 34 -0.1933333 -0.5
#> 35 0.8066667 -0.5
#> 36 0.8066667 0.5
#> 37 -0.1933333 0.5
#> 38 -0.1933333 0.5
#> 39 -0.1933333 -0.5
#> 40 0.8066667 -0.5
#> 41 -0.1933333 0.5
#> 42 0.8066667 -0.5
#> 43 0.8066667 -0.5
#> 44 -0.1933333 -0.5
#> 45 -0.1933333 0.5
#> 46 -0.1933333 -0.5
#> 47 -0.1933333 0.5
#> 48 0.8066667 -0.5
#> 49 -0.1933333 -0.5
#> 50 -0.1933333 -0.5
#> 51 -0.1933333 0.5
#> 52 0.8066667 0.5
#> 53 0.8066667 -0.5
#> 54 0.8066667 0.5
#> 55 -0.1933333 0.5
#> 56 -0.1933333 0.5
#> 57 -0.1933333 0.5
#> 58 0.8066667 0.5
#> 59 -0.1933333 -0.5
#> 60 0.8066667 -0.5
#> 61 -0.1933333 -0.5
#> 62 0.8066667 0.5
#> 63 -0.1933333 -0.5
#> 64 0.8066667 -0.5
#> 65 -0.1933333 -0.5
#> 66 -0.1933333 0.5
#> 67 -0.1933333 -0.5
#> 68 0.8066667 -0.5
#> 69 -0.1933333 0.5
#> 70 -0.1933333 -0.5
#> 71 -0.1933333 -0.5
#> 72 -0.1933333 -0.5
#> 73 -0.1933333 0.5
#> 74 -0.1933333 0.5
#> 75 -0.1933333 0.5
#> 76 -0.1933333 -0.5
#> 77 -0.1933333 0.5
#> 78 0.8066667 -0.5
#> 79 -0.1933333 -0.5
#> 80 -0.1933333 -0.5
#> 81 -0.1933333 0.5
#> 82 -0.1933333 -0.5
#> 83 -0.1933333 0.5
#> 84 -0.1933333 0.5
#> 85 -0.1933333 -0.5
#> 86 0.8066667 0.5
#> 87 0.8066667 -0.5
#> 88 -0.1933333 0.5
#> 89 -0.1933333 -0.5
#> 90 -0.1933333 -0.5
#> 91 0.8066667 0.5
#> 92 -0.1933333 -0.5
#> 93 -0.1933333 -0.5
#> 94 -0.1933333 -0.5
#> 95 0.8066667 0.5
#> 96 -0.1933333 -0.5
#> 97 -0.1933333 0.5
#> 98 0.8066667 0.5
#> 99 0.8066667 0.5
#> 100 0.8066667 0.5
#> 101 -0.1933333 -0.5
#> 102 0.8066667 0.5
#> 103 -0.1933333 0.5
#> 104 0.8066667 -0.5
#> 105 0.8066667 -0.5
#> 106 0.8066667 -0.5
#> 107 0.8066667 -0.5
#> 108 -0.1933333 0.5
#> 109 -0.1933333 0.5
#> 110 0.8066667 -0.5
#> 111 -0.1933333 -0.5
#> 112 -0.1933333 0.5
#> 113 -0.1933333 0.5
#> 114 -0.1933333 -0.5
#> 115 -0.1933333 -0.5
#> 116 -0.1933333 -0.5
#> 117 -0.1933333 -0.5
#> 118 -0.1933333 0.5
#> 119 -0.1933333 -0.5
#> 120 -0.1933333 -0.5
#> 121 0.8066667 0.5
#> 122 0.8066667 -0.5
#> 123 -0.1933333 0.5
#> 124 -0.1933333 0.5
#> 125 -0.1933333 0.5
#> 126 -0.1933333 -0.5
#> 127 -0.1933333 -0.5
#> 128 -0.1933333 0.5
#> 129 0.8066667 0.5
#> 130 -0.1933333 0.5
#> 131 -0.1933333 -0.5
#> 132 -0.1933333 0.5
#> 133 -0.1933333 -0.5
#> 134 0.8066667 0.5
#> 135 -0.1933333 -0.5
#> 136 0.8066667 0.5
#> 137 0.8066667 0.5
#> 138 -0.1933333 -0.5
#> 139 -0.1933333 -0.5
#> 140 -0.1933333 0.5
#> 141 -0.1933333 0.5
#> 142 -0.1933333 -0.5
#> 143 0.8066667 0.5
#> 144 0.8066667 0.5
#> 145 -0.1933333 -0.5
#> 146 0.8066667 0.5
#> 147 -0.1933333 -0.5
#> 148 -0.1933333 0.5
#> 149 -0.1933333 0.5
#> 150 0.8066667 0.5
#> 151 0.8066667 0.5
#> 152 -0.1933333 0.5
#> 153 0.8066667 0.5
#> 154 0.8066667 0.5
#> 155 -0.1933333 0.5
#> 156 -0.1933333 0.5
#> 157 -0.1933333 0.5
#> 158 -0.1933333 -0.5
#> 159 -0.1933333 -0.5
#> 160 -0.1933333 -0.5
#> 161 0.8066667 -0.5
#> 162 -0.1933333 -0.5
#> 163 -0.1933333 0.5
#> 164 -0.1933333 0.5
#> 165 -0.1933333 -0.5
#> 166 -0.1933333 0.5
#> 167 -0.1933333 -0.5
#> 168 -0.1933333 -0.5
#> 169 -0.1933333 0.5
#> 170 -0.1933333 0.5
#> 171 -0.1933333 -0.5
#> 172 0.8066667 0.5
#> 173 -0.1933333 -0.5
#> 174 0.8066667 -0.5
#> 175 0.8066667 0.5
#> 176 0.8066667 0.5
#> 177 -0.1933333 -0.5
#> 178 0.8066667 0.5
#> 179 -0.1933333 0.5
#> 180 -0.1933333 0.5
#> 181 -0.1933333 -0.5
#> 182 -0.1933333 -0.5
#> 183 -0.1933333 -0.5
#> 184 -0.1933333 0.5
#> 185 -0.1933333 -0.5
#> 186 -0.1933333 0.5
#> 187 0.8066667 0.5
#> 188 -0.1933333 0.5
#> 189 0.8066667 0.5
#> 190 0.8066667 -0.5
#> 191 -0.1933333 0.5
#> 192 0.8066667 -0.5
#> 193 -0.1933333 0.5
#> 194 -0.1933333 0.5
#> 195 -0.1933333 0.5
#> 196 0.8066667 -0.5
#> 197 -0.1933333 0.5
#> 198 -0.1933333 -0.5
#> 199 0.8066667 -0.5
#> 200 0.8066667 -0.5
#> 201 -0.1933333 -0.5
#> 202 -0.1933333 0.5
#> 203 -0.1933333 -0.5
#> 204 0.8066667 -0.5
#> 205 0.8066667 -0.5
#> 206 -0.1933333 -0.5
#> 207 0.8066667 -0.5
#> 208 0.8066667 0.5
#> 209 0.8066667 -0.5
#> 210 0.8066667 0.5
#> 211 -0.1933333 -0.5
#> 212 0.8066667 -0.5
#> 213 -0.1933333 0.5
#> 214 0.8066667 -0.5
#> 215 -0.1933333 0.5
#> 216 0.8066667 0.5
#> 217 -0.1933333 -0.5
#> 218 0.8066667 -0.5
#> 219 -0.1933333 0.5
#> 220 -0.1933333 -0.5
#> 221 0.8066667 -0.5
#> 222 -0.1933333 0.5
#> 223 -0.1933333 -0.5
#> 224 -0.1933333 -0.5
#> 225 -0.1933333 -0.5
#> 226 -0.1933333 -0.5
#> 227 0.8066667 0.5
#> 228 -0.1933333 -0.5
#> 229 -0.1933333 -0.5
#> 230 -0.1933333 -0.5
#> 231 0.8066667 -0.5
#> 232 -0.1933333 -0.5
#> 233 -0.1933333 -0.5
#> 234 -0.1933333 -0.5
#> 235 -0.1933333 -0.5
#> 236 -0.1933333 -0.5
#> 237 0.8066667 0.5
#> 238 0.8066667 -0.5
#> 239 -0.1933333 -0.5
#> 240 0.8066667 0.5
#> 241 0.8066667 0.5
#> 242 -0.1933333 0.5
#> 243 -0.1933333 -0.5
#> 244 -0.1933333 -0.5
#> 245 -0.1933333 -0.5
#> 246 0.8066667 0.5
#> 247 -0.1933333 0.5
#> 248 0.8066667 0.5
#> 249 0.8066667 -0.5
#> 250 -0.1933333 0.5
#> 251 -0.1933333 -0.5
#> 252 -0.1933333 -0.5
#> 253 0.8066667 0.5
#> 254 0.8066667 0.5
#> 255 -0.1933333 0.5
#> 256 -0.1933333 -0.5
#> 257 -0.1933333 0.5
#> 258 0.8066667 0.5
#> 259 0.8066667 -0.5
#> 260 -0.1933333 -0.5
#> 261 0.8066667 -0.5
#> 262 -0.1933333 0.5
#> 263 -0.1933333 0.5
#> 264 -0.1933333 0.5
#> 265 0.8066667 0.5
#> 266 0.8066667 -0.5
#> 267 -0.1933333 -0.5
#> 268 0.8066667 0.5
#> 269 0.8066667 -0.5
#> 270 -0.1933333 -0.5
#> 271 0.8066667 0.5
#> 272 -0.1933333 0.5
#> 273 -0.1933333 0.5
#> 274 0.8066667 -0.5
#> 275 0.8066667 -0.5
#> 276 0.8066667 -0.5
#> 277 -0.1933333 -0.5
#> 278 0.8066667 -0.5
#> 279 0.8066667 -0.5
#> 280 -0.1933333 0.5
#> 281 -0.1933333 0.5
#> 282 -0.1933333 -0.5
#> 283 -0.1933333 -0.5
#> 284 0.8066667 0.5
#> 285 0.8066667 0.5
#> 286 -0.1933333 -0.5
#> 287 0.8066667 0.5
#> 288 -0.1933333 -0.5
#> 289 -0.1933333 0.5
#> 290 -0.1933333 0.5
#> 291 0.8066667 0.5
#> 292 0.8066667 -0.5
#> 293 -0.1933333 0.5
#> 294 -0.1933333 0.5
#> 295 0.8066667 0.5
#> 296 -0.1933333 -0.5
#> 297 0.8066667 0.5
#> 298 -0.1933333 0.5
#> 299 -0.1933333 -0.5
#> 300 -0.1933333 0.5
#> 301 -0.1933333 -0.5
#> 302 -0.1933333 -0.5
#> 303 -0.1933333 0.5
#> 304 -0.1933333 0.5
#> 305 -0.1933333 -0.5
#> 306 -0.1933333 0.5
#> 307 -0.1933333 0.5
#> 308 -0.1933333 -0.5
#> 309 -0.1933333 -0.5
#> 310 -0.1933333 0.5
#> 311 -0.1933333 0.5
#> 312 -0.1933333 -0.5
#> 313 -0.1933333 0.5
#> 314 -0.1933333 -0.5
#> 315 -0.1933333 -0.5
#> 316 -0.1933333 0.5
#> 317 -0.1933333 0.5
#> 318 0.8066667 -0.5
#> 319 -0.1933333 -0.5
#> 320 -0.1933333 -0.5
#> 321 -0.1933333 -0.5
#> 322 -0.1933333 -0.5
#> 323 0.8066667 0.5
#> 324 -0.1933333 0.5
#> 325 -0.1933333 -0.5
#> 326 -0.1933333 -0.5
#> 327 -0.1933333 0.5
#> 328 -0.1933333 -0.5
#> 329 -0.1933333 -0.5
#> 330 0.8066667 0.5
#> 331 -0.1933333 0.5
#> 332 -0.1933333 -0.5
#> 333 -0.1933333 -0.5
#> 334 0.8066667 -0.5
#> 335 0.8066667 0.5
#> 336 -0.1933333 0.5
#> 337 -0.1933333 0.5
#> 338 0.8066667 0.5
#> 339 -0.1933333 -0.5
#> 340 -0.1933333 0.5
#> 341 -0.1933333 -0.5
#> 342 -0.1933333 -0.5
#> 343 -0.1933333 -0.5
#> 344 -0.1933333 -0.5
#> 345 -0.1933333 -0.5
#> 346 -0.1933333 0.5
#> 347 -0.1933333 0.5
#> 348 -0.1933333 -0.5
#> 349 0.8066667 -0.5
#> 350 -0.1933333 0.5
#> 351 -0.1933333 0.5
#> 352 -0.1933333 0.5
#> 353 -0.1933333 0.5
#> 354 -0.1933333 0.5
#> 355 -0.1933333 -0.5
#> 356 -0.1933333 -0.5
#> 357 0.8066667 -0.5
#> 358 0.8066667 -0.5
#> 359 0.8066667 -0.5
#> 360 -0.1933333 -0.5
#> 361 -0.1933333 0.5
#> 362 -0.1933333 0.5
#> 363 -0.1933333 -0.5
#> 364 -0.1933333 0.5
#> 365 -0.1933333 -0.5
#> 366 -0.1933333 0.5
#> 367 -0.1933333 -0.5
#> 368 -0.1933333 0.5
#> 369 -0.1933333 0.5
#> 370 -0.1933333 0.5
#> 371 -0.1933333 -0.5
#> 372 -0.1933333 0.5
#> 373 -0.1933333 0.5
#> 374 0.8066667 0.5
#> 375 -0.1933333 0.5
#> 376 -0.1933333 0.5
#> 377 -0.1933333 0.5
#> 378 0.8066667 0.5
#> 379 -0.1933333 -0.5
#> 380 -0.1933333 -0.5
#> 381 0.8066667 -0.5
#> 382 -0.1933333 -0.5
#> 383 -0.1933333 0.5
#> 384 0.8066667 0.5
#> 385 -0.1933333 0.5
#> 386 -0.1933333 -0.5
#> 387 0.8066667 -0.5
#> 388 0.8066667 0.5
#> 389 -0.1933333 -0.5
#> 390 -0.1933333 -0.5
#> 391 -0.1933333 -0.5
#> 392 -0.1933333 0.5
#> 393 0.8066667 0.5
#> 394 -0.1933333 0.5
#> 395 -0.1933333 -0.5
#> 396 -0.1933333 0.5
#> 397 -0.1933333 0.5
#> 398 -0.1933333 -0.5
#> 399 -0.1933333 0.5
#> 400 0.8066667 0.5
#> 401 -0.1933333 -0.5
#> 402 0.8066667 0.5
#> 403 -0.1933333 0.5
#> 404 -0.1933333 0.5
#> 405 -0.1933333 0.5
#> 406 -0.1933333 0.5
#> 407 -0.1933333 0.5
#> 408 0.8066667 -0.5
#> 409 0.8066667 -0.5
#> 410 -0.1933333 0.5
#> 411 -0.1933333 0.5
#> 412 -0.1933333 -0.5
#> 413 0.8066667 0.5
#> 414 -0.1933333 -0.5
#> 415 -0.1933333 -0.5
#> 416 -0.1933333 0.5
#> 417 0.8066667 0.5
#> 418 -0.1933333 -0.5
#> 419 0.8066667 0.5
#> 420 0.8066667 -0.5
#> 421 -0.1933333 -0.5
#> 422 -0.1933333 0.5
#> 423 0.8066667 -0.5
#> 424 0.8066667 0.5
#> 425 -0.1933333 -0.5
#> 426 -0.1933333 -0.5
#> 427 0.8066667 -0.5
#> 428 0.8066667 -0.5
#> 429 -0.1933333 0.5
#> 430 0.8066667 0.5
#> 431 -0.1933333 -0.5
#> 432 0.8066667 -0.5
#> 433 0.8066667 -0.5
#> 434 -0.1933333 -0.5
#> 435 -0.1933333 -0.5
#> 436 -0.1933333 -0.5
#> 437 -0.1933333 -0.5
#> 438 -0.1933333 -0.5
#> 439 -0.1933333 -0.5
#> 440 -0.1933333 -0.5
#> 441 -0.1933333 0.5
#> 442 -0.1933333 0.5
#> 443 -0.1933333 0.5
#> 444 0.8066667 0.5
#> 445 -0.1933333 0.5
#> 446 -0.1933333 0.5
#> 447 0.8066667 0.5
#> 448 -0.1933333 0.5
#> 449 -0.1933333 0.5
#> 450 -0.1933333 0.5
#> 451 -0.1933333 -0.5
#> 452 0.8066667 -0.5
#> 453 0.8066667 0.5
#> 454 -0.1933333 0.5
#> 455 -0.1933333 0.5
#> 456 -0.1933333 0.5
#> 457 -0.1933333 0.5
#> 458 -0.1933333 0.5
#> 459 0.8066667 0.5
#> 460 0.8066667 0.5
#> 461 -0.1933333 0.5
#> 462 -0.1933333 0.5
#> 463 -0.1933333 -0.5
#> 464 0.8066667 -0.5
#> 465 0.8066667 -0.5
#> 466 -0.1933333 0.5
#> 467 -0.1933333 0.5
#> 468 -0.1933333 0.5
#> 469 -0.1933333 0.5
#> 470 -0.1933333 0.5
#> 471 -0.1933333 -0.5
#> 472 0.8066667 0.5
#> 473 0.8066667 -0.5
#> 474 -0.1933333 0.5
#> 475 0.8066667 0.5
#> 476 -0.1933333 0.5
#> 477 -0.1933333 0.5
#> 478 0.8066667 0.5
#> 479 -0.1933333 -0.5
#> 480 -0.1933333 0.5
#> 481 -0.1933333 -0.5
#> 482 -0.1933333 0.5
#> 483 -0.1933333 -0.5
#> 484 -0.1933333 -0.5
#> 485 -0.1933333 -0.5
#> 486 -0.1933333 -0.5
#> 487 -0.1933333 0.5
#> 488 0.8066667 -0.5
#> 489 0.8066667 0.5
#> 490 -0.1933333 0.5
#> 491 0.8066667 -0.5
#> 492 0.8066667 0.5
#> 493 0.8066667 -0.5
#> 494 -0.1933333 -0.5
#> 495 -0.1933333 0.5
#> 496 -0.1933333 0.5
#> 497 0.8066667 0.5
#> 498 0.8066667 -0.5
#> 499 -0.1933333 -0.5
#> 500 0.8066667 0.5
centered_colnames <- grep("_CENTERED$", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)
weighted_data2 <- estimate_weights(
data = centered_ipd_sat, centered_colnames = centered_colnames,
n_boot_iteration = 100
)
# get dummy binary IPD
pseudo_adrs <- get_pseudo_ipd_binary(
binary_agd = data.frame(
ARM = rep("B", 2),
RESPONSE = c("YES", "NO"),
COUNT = c(280, 120)
),
format = "stacked"
)
# unanchored binary MAIC, with CI based on sandwich estimator
maic_unanchored(
weights_object = weighted_data,
ipd = adrs_sat,
pseudo_ipd = pseudo_adrs,
trt_ipd = "A",
trt_agd = "B",
trt_var_ipd = "ARM",
trt_var_agd = "ARM",
endpoint_type = "binary",
endpoint_name = "Binary Endpoint",
eff_measure = "RR",
# binary specific args
binary_robust_cov_type = "HC3"
)
#> $descriptive
#> $descriptive$summary
#> trt_ind treatment type n events events_pct
#> 1 B B Before matching 400 280.000 70.0000
#> 2 A A Before matching 500 390.000 78.0000
#> 3 B B After matching 400 280.000 70.0000
#> 4 A A After matching 500 128.322 25.6644
#>
#>
#> $inferential
#> $inferential$fit
#> $inferential$fit$model_before
#>
#> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat)
#>
#> Coefficients:
#> (Intercept) ARMA
#> -0.3567 0.1082
#>
#> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual
#> Null Deviance: 395.5
#> Residual Deviance: 393.5 AIC: 1738
#>
#> $inferential$fit$model_after
#>
#> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat,
#> weights = weights)
#>
#> Coefficients:
#> (Intercept) ARMA
#> -0.35667 0.05611
#>
#> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual
#> Null Deviance: 277.2
#> Residual Deviance: 276.9 AIC: 1098
#>
#> $inferential$fit$res_AB
#> $inferential$fit$res_AB$est
#> [1] 1.057718
#>
#> $inferential$fit$res_AB$se
#> [1] 0.06603162
#>
#> $inferential$fit$res_AB$ci_l
#> [1] 0.9362356
#>
#> $inferential$fit$res_AB$ci_u
#> [1] 1.194964
#>
#> $inferential$fit$res_AB$pval
#> [1] 0.3673375
#>
#>
#> $inferential$fit$res_AB_unadj
#> $inferential$fit$res_AB_unadj$est
#> [1] 1.114286
#>
#> $inferential$fit$res_AB_unadj$se
#> [1] 0.08768422
#>
#> $inferential$fit$res_AB_unadj$ci_l
#> [1] 0.9557015
#>
#> $inferential$fit$res_AB_unadj$ci_u
#> [1] 1.299185
#>
#> $inferential$fit$res_AB_unadj$pval
#> [1] 0.1671206
#>
#>
#> $inferential$fit$boot_res
#> NULL
#>
#> $inferential$fit$boot_res_AB
#> NULL
#>
#>
#> $inferential$summary
#> case RR LCL UCL pval
#> 1 AB 1.114286 0.9557015 1.299185 0.1671206
#> 2 adjusted_AB 1.057718 0.9362356 1.194964 0.3673375
#>
#>
# unanchored binary MAIC, with bootstrapped CI
maic_unanchored(
weights_object = weighted_data2,
ipd = adrs_sat,
pseudo_ipd = pseudo_adrs,
trt_ipd = "A",
trt_agd = "B",
trt_var_ipd = "ARM",
trt_var_agd = "ARM",
endpoint_type = "binary",
endpoint_name = "Binary Endpoint",
eff_measure = "RR",
# binary specific args
binary_robust_cov_type = "HC3"
)
#> $descriptive
#> $descriptive$summary
#> trt_ind treatment type n events events_pct
#> 1 B B Before matching 400 280.000 70.0000
#> 2 A A Before matching 500 390.000 78.0000
#> 3 B B After matching 400 280.000 70.0000
#> 4 A A After matching 500 128.322 25.6644
#>
#>
#> $inferential
#> $inferential$fit
#> $inferential$fit$model_before
#>
#> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat)
#>
#> Coefficients:
#> (Intercept) ARMA
#> -0.3567 0.1082
#>
#> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual
#> Null Deviance: 395.5
#> Residual Deviance: 393.5 AIC: 1738
#>
#> $inferential$fit$model_after
#>
#> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat,
#> weights = weights)
#>
#> Coefficients:
#> (Intercept) ARMA
#> -0.35667 0.05611
#>
#> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual
#> Null Deviance: 277.2
#> Residual Deviance: 276.9 AIC: 1098
#>
#> $inferential$fit$res_AB
#> $inferential$fit$res_AB$est
#> [1] 1.057718
#>
#> $inferential$fit$res_AB$se
#> [1] 0.06603162
#>
#> $inferential$fit$res_AB$ci_l
#> [1] 0.9362356
#>
#> $inferential$fit$res_AB$ci_u
#> [1] 1.194964
#>
#> $inferential$fit$res_AB$pval
#> [1] 0.3673375
#>
#>
#> $inferential$fit$res_AB_unadj
#> $inferential$fit$res_AB_unadj$est
#> [1] 1.114286
#>
#> $inferential$fit$res_AB_unadj$se
#> [1] 0.08768422
#>
#> $inferential$fit$res_AB_unadj$ci_l
#> [1] 0.9557015
#>
#> $inferential$fit$res_AB_unadj$ci_u
#> [1] 1.299185
#>
#> $inferential$fit$res_AB_unadj$pval
#> [1] 0.1671206
#>
#>
#> $inferential$fit$boot_res
#>
#> STRATIFIED BOOTSTRAP
#>
#>
#> Call:
#> boot(data = boot_ipd, statistic = stat_fun, R = R, strata = weights_object$boot_strata,
#> w_obj = weights_object, pseudo_ipd = pseudo_ipd, normalize = normalize_weights)
#>
#>
#> Bootstrap Statistics :
#> original bias std. error
#> t1* 0.05611408 0.0092659306 0.0517384223
#> t2* 0.01136433 0.0002515784 0.0008517504
#>
#> $inferential$fit$boot_res_AB
#> $inferential$fit$boot_res_AB$est
#> [1] 1.057718
#>
#> $inferential$fit$boot_res_AB$se
#> [1] NA
#>
#> $inferential$fit$boot_res_AB$ci_l
#> [1] 0.9469043
#>
#> $inferential$fit$boot_res_AB$ci_u
#> [1] 1.159807
#>
#> $inferential$fit$boot_res_AB$pval
#> [1] NA
#>
#>
#>
#> $inferential$summary
#> case RR LCL UCL pval
#> 1 AB 1.114286 0.9557015 1.299185 0.1671206
#> 2 adjusted_AB 1.057718 0.9362356 1.194964 0.3673375
#>
#>
#---------------------------------