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Regression Tables from ‘GLM’, ‘GEE’, ‘GLMM’, ‘Cox’ and ‘survey’ Results for Publication.
## Gaussian
glm_gaussian <- glm(mpg ~ cyl + disp, data = mtcars)
glmshow.display(glm_gaussian, decimal = 2)
#> $first.line
#> [1] "Linear regression predicting mpg\n"
#>
#> $table
#> crude coeff.(95%CI) crude P value adj. coeff.(95%CI) adj. P value
#> cyl "-2.88 (-3.51,-2.24)" "< 0.001" "-1.59 (-2.98,-0.19)" "0.034"
#> disp "-0.04 (-0.05,-0.03)" "< 0.001" "-0.02 (-0.04,0)" "0.054"
#>
#> $last.lines
#> [1] "No. of observations = 32\nR-squared = 0.7596\nAIC value = 167.1456\n\n"
#>
#> attr(,"class")
#> [1] "display" "list"
## Binomial
glm_binomial <- glm(vs ~ cyl + disp, data = mtcars, family = binomial)
glmshow.display(glm_binomial, decimal = 2)
#> $first.line
#> [1] "Logistic regression predicting vs\n"
#>
#> $table
#> crude OR.(95%CI) crude P value adj. OR.(95%CI) adj. P value
#> cyl "0.2 (0.08,0.56)" "0.002" "0.15 (0.02,1.02)" "0.053"
#> disp "0.98 (0.97,0.99)" "0.002" "1 (0.98,1.03)" "0.715"
#>
#> $last.lines
#> [1] "No. of observations = 32\nAIC value = 23.8304\n\n"
#>
#> attr(,"class")
#> [1] "display" "list"geeglm object from
geepack packagelibrary(geepack) ## for dietox data
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
dietox$ddn <- as.numeric(rnorm(nrow(dietox)) > 0)
gee01 <- geeglm(Weight ~ Time + Cu, id = Pig, data = dietox, family = gaussian, corstr = "ex")
geeglm.display(gee01)
#> $caption
#> [1] "GEE(gaussian) predicting Weight by Time, Cu - Group Pig"
#>
#> $table
#> crude coeff(95%CI) crude P value adj. coeff(95%CI)
#> Time "6.94 (6.79,7.1)" "< 0.001" "6.94 (6.79,7.1)"
#> Cu: ref.=Cu000 NA NA NA
#> 035 "-0.59 (-3.73,2.54)" "0.711" "-0.84 (-3.9,2.23)"
#> 175 "1.9 (-1.87,5.66)" "0.324" "1.77 (-1.9,5.45)"
#> adj. P value
#> Time "< 0.001"
#> Cu: ref.=Cu000 NA
#> 035 "0.593"
#> 175 "0.345"
#>
#> $metric
#> crude coeff(95%CI) crude P value
#> NA NA
#> Estimated correlation parameters "0.775" NA
#> No. of clusters "72" NA
#> No. of observations "861" NA
#> adj. coeff(95%CI) adj. P value
#> NA NA
#> Estimated correlation parameters NA NA
#> No. of clusters NA NA
#> No. of observations NA NA
gee02 <- geeglm(ddn ~ Time + Cu, id = Pig, data = dietox, family = binomial, corstr = "ex")
geeglm.display(gee02)
#> $caption
#> [1] "GEE(binomial) predicting ddn by Time, Cu - Group Pig"
#>
#> $table
#> crude OR(95%CI) crude P value adj. OR(95%CI) adj. P value
#> Time "1 (0.96,1.04)" "0.987" "1 (0.96,1.04)" "0.982"
#> Cu: ref.=Cu000 NA NA NA NA
#> 035 "0.8 (0.58,1.1)" "0.17" "0.8 (0.59,1.1)" "0.17"
#> 175 "0.79 (0.58,1.09)" "0.148" "0.79 (0.58,1.09)" "0.148"
#>
#> $metric
#> crude OR(95%CI) crude P value adj. OR(95%CI)
#> NA NA NA
#> Estimated correlation parameters "-0.008" NA NA
#> No. of clusters "72" NA NA
#> No. of observations "861" NA NA
#> adj. P value
#> NA
#> Estimated correlation parameters NA
#> No. of clusters NA
#> No. of observations NAlmerMod or glmerMod
object from lme4 packagelibrary(lme4)
l1 <- lmer(Weight ~ Time + Cu + (1 | Pig), data = dietox)
lmer.display(l1, ci.ranef = T)
#> $table
#> crude coeff(95%CI) crude P value adj. coeff(95%CI)
#> Time 6.94 (6.88,7.01) 0.0000000 6.94 (6.88,7.01)
#> Cu: ref.=Cu000 <NA> NA <NA>
#> 035 -0.58 (-4.67,3.51) 0.7811327 -0.84 (-4.47,2.8)
#> 175 1.9 (-2.23,6.04) 0.3670740 1.77 (-1.9,5.45)
#> Random effects <NA> NA <NA>
#> Pig 40.34 (28.08,54.95) NA <NA>
#> Residual 11.37 (10.3,12.55) NA <NA>
#> Metrics <NA> NA <NA>
#> No. of groups (Pig) 72 NA <NA>
#> No. of observations 861 NA <NA>
#> Log-likelihood -2400.8 NA <NA>
#> AIC value 4801.6 NA <NA>
#> adj. P value
#> Time 0.0000000
#> Cu: ref.=Cu000 NA
#> 035 0.6527264
#> 175 0.3442309
#> Random effects NA
#> Pig NA
#> Residual NA
#> Metrics NA
#> No. of groups (Pig) NA
#> No. of observations NA
#> Log-likelihood NA
#> AIC value NA
#>
#> $caption
#> [1] "Linear mixed model fit by REML : Weight ~ Time + Cu + (1 | Pig)"
l2 <- glmer(ddn ~ Time + Cu + (1 | Pig), data = dietox, family = "binomial")
lmer.display(l2)
#> $table
#> crude OR(95%CI) crude P value adj. OR(95%CI) adj. P value
#> Time 1 (0.96,1.04) 0.9846359 1 (0.96,1.04) 0.9795894
#> Cu: ref.=Cu000 <NA> NA <NA> NA
#> 035 0.8 (0.58,1.11) 0.1863867 0.8 (0.58,1.11) 0.1863524
#> 175 0.79 (0.57,1.1) 0.1662915 0.79 (0.57,1.1) 0.1662740
#> Random effects <NA> NA <NA> NA
#> Pig 0 NA <NA> NA
#> Metrics <NA> NA <NA> NA
#> No. of groups (Pig) 72 NA <NA> NA
#> No. of observations 861 NA <NA> NA
#> Log-likelihood -595.1 NA <NA> NA
#> AIC value 1200.2 NA <NA> NA
#>
#> $caption
#> [1] "Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) : ddn ~ Time + Cu + (1 | Pig)"frailty or cluster
optionslibrary(survival)
fit1 <- coxph(Surv(time, status) ~ ph.ecog + age, cluster = inst, lung, model = T) ## model = T: to extract original data
fit2 <- coxph(Surv(time, status) ~ ph.ecog + age + frailty(inst), lung, model = T)
cox2.display(fit1)
#> $table
#> crude HR(95%CI) crude P value adj. HR(95%CI) adj. P value
#> ph.ecog "1.61 (1.25,2.08)" "< 0.001" "1.56 (1.22,2)" "< 0.001"
#> age "1.02 (1.01,1.03)" "0.007" "1.01 (1,1.02)" "0.085"
#>
#> $ranef
#> [,1] [,2] [,3] [,4]
#> cluster NA NA NA NA
#> inst NA NA NA NA
#>
#> $metric
#> [,1] [,2] [,3] [,4]
#> <NA> NA NA NA NA
#> No. of observations 226.000 NA NA NA
#> No. of events 163.000 NA NA NA
#> AIC 1463.797 NA NA NA
#>
#> $caption
#> [1] "Marginal Cox model on time ('time') to event ('status') - Group inst"
cox2.display(fit2)
#> $table
#> crude HR(95%CI) crude P value adj. HR(95%CI) adj. P value
#> ph.ecog "1.64 (1.31,2.05)" "< 0.001" "1.58 (1.26,1.99)" "< 0.001"
#> age "1.02 (1,1.04)" "0.041" "1.01 (0.99,1.03)" "0.225"
#>
#> $ranef
#> [,1] [,2] [,3] [,4]
#> frailty NA NA NA NA
#> inst NA NA NA NA
#>
#> $metric
#> [,1] [,2] [,3] [,4]
#> <NA> NA NA NA NA
#> No. of observations 226.000 NA NA NA
#> No. of events 163.000 NA NA NA
#> AIC 1463.223 NA NA NA
#>
#> $caption
#> [1] "Frailty Cox model on time ('time') to event ('status') - Group inst"coxme object from
coxme packagelibrary(coxme)
fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1 | inst), lung)
coxme.display(fit)
#> $table
#> crude HR(95%CI) crude P value adj. HR(95%CI) adj. P value
#> ph.ecog "1.66 (1.32,2.09)" "< 0.001" "1.61 (1.27,2.03)" "< 0.001"
#> age "1.02 (1,1.04)" "0.043" "1.01 (0.99,1.03)" "0.227"
#>
#> $ranef
#> [,1] [,2] [,3] [,4]
#> Random effect NA NA NA NA
#> inst(Intercept) 0.02 NA NA NA
#>
#> $metric
#> [,1] [,2] [,3] [,4]
#> <NA> NA NA NA NA
#> No. of groups(inst) 18 NA NA NA
#> No. of observations 226 NA NA NA
#> No. of events 163 NA NA NA
#>
#> $caption
#> [1] "Mixed effects Cox model on time ('time') to event ('status') - Group inst"svyglm object from
survey packagelibrary(survey)
data(api)
apistrat$tt <- c(rep(1, 20), rep(0, nrow(apistrat) - 20))
apistrat$tt2 <- factor(c(rep(0, 40), rep(1, nrow(apistrat) - 40)))
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc)
ds <- svyglm(api00 ~ ell + meals + tt2, design = dstrat)
ds2 <- svyglm(tt ~ ell + meals + tt2, design = dstrat, family = quasibinomial())
svyregress.display(ds)
#> $first.line
#> [1] "Linear regression predicting api00- weighted data\n"
#>
#> $table
#> crude coeff.(95%CI) crude P value adj. coeff.(95%CI)
#> ell "-3.73 (-4.36,-3.1)" "< 0.001" "-0.51 (-1.27,0.26)"
#> meals "-3.38 (-3.71,-3.05)" "< 0.001" "-3.11 (-3.65,-2.57)"
#> tt2: 1 vs 0 "10.98 (-34.44,56.39)" "0.634" "6.24 (-17.83,30.32)"
#> adj. P value
#> ell "0.195"
#> meals "< 0.001"
#> tt2: 1 vs 0 "0.61"
#>
#> $last.lines
#> [1] "No. of observations = 200\nAIC value = 2308.0628\n\n"
#>
#> attr(,"class")
#> [1] "display" "list"
svyregress.display(ds2)
#> $first.line
#> [1] "Logistic regression predicting tt- weighted data\n"
#>
#> $table
#> crude OR.(95%CI) crude P value adj. OR.(95%CI) adj. P value
#> ell "1.02 (1,1.05)" "0.047" "1.11 (1.02,1.21)" "0.02"
#> meals "1.01 (0.99,1.03)" "0.255" "0.97 (0.93,1.01)" "0.151"
#> tt2: 1 vs 0 "0 (0,0)" "< 0.001" "0 (0,0)" "< 0.001"
#>
#> $last.lines
#> [1] "No. of observations = 200\n\n"
#>
#> attr(,"class")
#> [1] "display" "list"svycoxph object from
survey packagedata(pbc, package = "survival")
pbc$sex <- factor(pbc$sex)
pbc$stage <- factor(pbc$stage)
pbc$randomized <- with(pbc, !is.na(trt) & trt > 0)
biasmodel <- glm(randomized ~ age * edema, data = pbc, family = binomial)
pbc$randprob <- fitted(biasmodel)
if (is.null(pbc$albumin)) pbc$albumin <- pbc$alb ## pre2.9.0
dpbc <- svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized))
model <- svycoxph(Surv(time, status > 0) ~ sex + protime + albumin + stage, design = dpbc)
svycox.display(model)
#> Stratified Independent Sampling design (with replacement)
#> svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc,
#> randomized))
#> Stratified Independent Sampling design (with replacement)
#> svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc,
#> randomized))
#> Stratified Independent Sampling design (with replacement)
#> svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc,
#> randomized))
#> Stratified Independent Sampling design (with replacement)
#> svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc,
#> randomized))
#> Stratified Independent Sampling design (with replacement)
#> svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc,
#> randomized))
#> $table
#> crude HR(95%CI) crude P value adj. HR(95%CI)
#> sex: f vs m "0.62 (0.4,0.97)" "0.038" "0.55 (0.33,0.9)"
#> protime "1.37 (1.09,1.72)" "0.006" "1.52 (1.2,1.91)"
#> albumin "0.2 (0.14,0.29)" "< 0.001" "0.31 (0.2,0.47)"
#> stage: ref.=1 NA NA NA
#> 2 "5.67 (0.77,41.78)" "0.089" "10.94 (1.01,118.54)"
#> 3 "9.78 (1.37,69.94)" "0.023" "17.03 (1.69,171.6)"
#> 4 "22.89 (3.2,163.47)" "0.002" "22.56 (2.25,226.41)"
#> adj. P value
#> sex: f vs m "0.017"
#> protime "< 0.001"
#> albumin "< 0.001"
#> stage: ref.=1 NA
#> 2 "0.049"
#> 3 "0.016"
#> 4 "0.008"
#>
#> $metric
#> [,1] [,2] [,3] [,4]
#> <NA> NA NA NA NA
#> No. of observations 312.00 NA NA NA
#> No. of events 144.00 NA NA NA
#> AIC 1483.12 NA NA NA
#>
#> $caption
#> [1] "Survey cox model on time ('time') to event ('status > 0')"library(dplyr)
lung %>%
mutate(
status = as.integer(status == 1),
sex = factor(sex),
kk = factor(as.integer(pat.karno >= 70)),
kk1 = factor(as.integer(pat.karno >= 60))
) -> lung
# TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data=lung, line = T)
## Survey data
library(survey)
data.design <- svydesign(id = ~1, data = lung, weights = ~1)
# TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = data.design, line = F)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.