weight_func works as expected
Code
result <- weight_func(sw_data = data, switch_n_cov = ~1, switch_d_cov = ~ X1 +
X2, use_switch_weights = TRUE, use_censor_weights = TRUE, cense = "C",
pool_cense_d = FALSE, pool_cense_n = FALSE, cense_d_cov = ~ X1 + X2 + X3 + X4 +
age_s, cense_n_cov = ~ X3 + X4, save_weight_models = FALSE, data_dir = save_dir,
glm_function = "parglm", control = parglm.control(nthreads = 2, method = "FAST"))
Message
P(treatment = 1 | previous treatment = 0) for denominator
Output
Call:
glm(formula = treatment ~ X1 + X2, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.52633 0.05629 -9.351 < 2e-16 ***
X1 0.35856 0.07807 4.593 4.37e-06 ***
X2 0.42935 0.04032 10.648 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3870 on 2848 degrees of freedom
Residual deviance: 3731 on 2846 degrees of freedom
AIC: 3737
Number of Fisher Scoring iterations: 4
Message
P(treatment = 1 | previous treatment = 0) for numerator
Output
Call:
glm(formula = treatment ~ 1, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.3366 0.0380 -8.857 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3870 on 2848 degrees of freedom
Residual deviance: 3870 on 2848 degrees of freedom
AIC: 3872
Number of Fisher Scoring iterations: 4
Message
P(treatment = 1 | previous treatment = 1) for denominator
Output
Call:
glm(formula = treatment ~ X1 + X2, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.89795 0.05905 15.208 < 2e-16 ***
X1 0.34311 0.11198 3.064 0.00218 **
X2 0.44843 0.04888 9.174 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2649.6 on 2153 degrees of freedom
Residual deviance: 2549.5 on 2151 degrees of freedom
AIC: 2555.5
Number of Fisher Scoring iterations: 4
Message
P(treatment = 1 | previous treatment = 1) for numerator
Output
Call:
glm(formula = treatment ~ 1, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.8235 0.0468 17.6 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2649.6 on 2153 degrees of freedom
Residual deviance: 2649.6 on 2153 degrees of freedom
AIC: 2651.6
Number of Fisher Scoring iterations: 4
Message
Model for P(cense = 0 | X, previous treatment = 0) for denominator
Output
Call:
glm(formula = 1 - C ~ X1 + X2 + X3 + X4 + age_s, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.90004 0.09634 9.342 < 2e-16 ***
X1 0.58887 0.11288 5.217 1.82e-07 ***
X2 -0.46469 0.05660 -8.210 < 2e-16 ***
X3 0.32342 0.11178 2.893 0.00381 **
X4 -0.25226 0.05627 -4.483 7.35e-06 ***
age_s 0.97304 0.06783 14.346 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2465.6 on 2848 degrees of freedom
Residual deviance: 2105.0 on 2843 degrees of freedom
AIC: 2117
Number of Fisher Scoring iterations: 5
Message
Model for P(cense = 0 | X, previous treatment = 1) for denominator
Output
Call:
glm(formula = 1 - C ~ X1 + X2 + X3 + X4 + age_s, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.78647 0.13213 13.521 < 2e-16 ***
X1 0.33495 0.20307 1.649 0.0991 .
X2 -0.59679 0.08589 -6.948 3.7e-12 ***
X3 0.36771 0.16983 2.165 0.0304 *
X4 -0.22223 0.09511 -2.336 0.0195 *
age_s 1.14576 0.11569 9.904 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1179.7 on 2153 degrees of freedom
Residual deviance: 1011.3 on 2148 degrees of freedom
AIC: 1023.3
Number of Fisher Scoring iterations: 6
Message
Model for P(cense = 0 | X, previous treatment = 0) for numerator
Output
Call:
glm(formula = 1 - C ~ X3 + X4, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.51989 0.07547 20.139 < 2e-16 ***
X3 0.20212 0.10398 1.944 0.0519 .
X4 -0.24079 0.05398 -4.461 8.17e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2465.6 on 2848 degrees of freedom
Residual deviance: 2442.8 on 2846 degrees of freedom
AIC: 2448.8
Number of Fisher Scoring iterations: 4
Message
Model for P(cense = 0 | X, previous treatment = 1) for numerator
Output
Call:
glm(formula = 1 - C ~ X3 + X4, family = binomial(link = "logit"),
data = data, control = list(epsilon = 1e-08, maxit = 25,
trace = FALSE, nthreads = 2, block_size = NULL, method = "FAST"),
method = parglm::parglm.fit, singular.ok = FALSE)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.35075 0.11680 20.126 <2e-16 ***
X3 0.30635 0.16109 1.902 0.0572 .
X4 -0.10257 0.09121 -1.125 0.2608
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1179.7 on 2153 degrees of freedom
Residual deviance: 1175.0 on 2151 degrees of freedom
AIC: 1181
Number of Fisher Scoring iterations: 5
weight_func works with user specified time on regime
Code
for (i in result$switch_models) print(i, digits = 4)
Output
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.1657 0.06642 -2.495 1.261e-02
X1 0.3719 0.07957 4.674 2.960e-06
X2 0.4548 0.04131 11.008 3.505e-28
time_on_regime -0.2686 0.02750 -9.768 1.538e-22
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3870 2848 -1810 3628 3652 3620 2845 2849
P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) 0.005973 0.05142 0.1162 9.075e-01
time_on_regime -0.250221 0.02676 -9.3493 8.819e-21
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3870 2848 -1885 3773 3785 3769 2847 2849
P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.6281 0.08932 7.032 2.036e-12
X1 0.3559 0.11244 3.165 1.550e-03
X2 0.4496 0.04903 9.170 4.731e-20
time_on_regime 0.1130 0.02898 3.901 9.591e-05
null.deviance df.null logLik AIC BIC deviance df.residual nobs
2650 2153 -1267 2541 2564 2533 2150 2154
P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 0.5673 0.08024 7.070 1.551e-12
time_on_regime 0.1086 0.02845 3.818 1.343e-04
null.deviance df.null logLik AIC BIC deviance df.residual nobs
2650 2153 -1317 2638 2650 2634 2152 2154
Code
for (i in result$censor_models) print(i, digits = 4)
weight_func works with pool_cense = TRUE
Code
lapply(result$switch_models, print, digits = 4)
Output
P(treatment = 1 | previous treatment = 0) for denominator
term estimate std.error statistic p.value
(Intercept) -0.5263 0.05629 -9.351 8.713e-21
X1 0.3586 0.07807 4.593 4.369e-06
X2 0.4294 0.04032 10.648 1.787e-26
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3870 2848 -1865 3737 3755 3731 2846 2849
P(treatment = 1 | previous treatment = 0) for numerator
term estimate std.error statistic p.value
(Intercept) -0.3366 0.038 -8.857 8.201e-19
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3870 2848 -1935 3872 3878 3870 2848 2849
P(treatment = 1 | previous treatment = 1) for denominator
term estimate std.error statistic p.value
(Intercept) 0.8979 0.05905 15.208 3.149e-52
X1 0.3431 0.11198 3.064 2.183e-03
X2 0.4484 0.04888 9.174 4.562e-20
null.deviance df.null logLik AIC BIC deviance df.residual nobs
2650 2153 -1275 2556 2573 2550 2151 2154
P(treatment = 1 | previous treatment = 1) for numerator
term estimate std.error statistic p.value
(Intercept) 0.8235 0.0468 17.6 2.571e-69
null.deviance df.null logLik AIC BIC deviance df.residual nobs
2650 2153 -1325 2652 2657 2650 2153 2154
$switch_d0
NULL
$switch_n0
NULL
$switch_d1
NULL
$switch_n1
NULL
Code
lapply(result$censor_models, print, digits = 4)
Output
Model for P(cense = 0 | X) for denominator
term estimate std.error statistic p.value
(Intercept) 1.3102 0.07502 17.465 2.640e-68
X1 0.3612 0.09673 3.734 1.886e-04
X2 -0.5369 0.04672 -11.492 1.446e-30
X3 0.3147 0.09232 3.409 6.530e-04
X4 -0.1317 0.04615 -2.854 4.320e-03
age_s 1.0445 0.05846 17.866 2.177e-71
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3718 5002 -1594 3200 3239 3188 4997 5003
Model for P(cense = 0 | X) for numerator
term estimate std.error statistic p.value
(Intercept) 1.85647 0.06132 30.277 2.290e-201
X3 0.21535 0.08648 2.490 1.277e-02
X4 -0.05953 0.04371 -1.362 1.732e-01
null.deviance df.null logLik AIC BIC deviance df.residual nobs
3718 5002 -1855 3716 3735 3710 5000 5003
$cens_pool_d
NULL
$cens_pool_n
NULL
CAM-Roche/RandomisedTrialsEmulation documentation built on April 14, 2025, 7:44 a.m.
