Regression Tables from ‘GLM’, ‘GEE’, ‘GLMM’, ‘Cox’ and ‘survey’ Results for Publication.
remotes::install_github('jinseob2kim/jstable')
library(jstable)## 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)
#> cyl "-2.88 (-3.51,-2.24)" "< 0.001" "-1.59 (-2.98,-0.19)"
#> disp "-0.04 (-0.05,-0.03)" "< 0.001" "-0.02 (-0.04,0)"
#> adj. P value
#> cyl "0.034"
#> disp "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.=1 NA NA NA
#> 2 "-0.59 (-3.73,2.54)" "0.711" "-0.84 (-3.9,2.23)"
#> 3 "1.9 (-1.87,5.66)" "0.324" "1.77 (-1.9,5.45)"
#> adj. P value
#> Time "< 0.001"
#> Cu: ref.=1 NA
#> 2 "0.593"
#> 3 "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)
#> Time "0.99 (0.95,1.03)" "0.59" "0.99 (0.95,1.03)"
#> Cu: ref.=1 NA NA NA
#> 2 "0.69 (0.53,0.89)" "0.005" "0.69 (0.53,0.89)"
#> 3 "0.8 (0.59,1.07)" "0.127" "0.8 (0.59,1.07)"
#> adj. P value
#> Time "0.598"
#> Cu: ref.=1 NA
#> 2 "0.005"
#> 3 "0.128"
#>
#> $metric
#> crude OR(95%CI) crude P value
#> NA NA
#> Estimated correlation parameters "-0.024" NA
#> No. of clusters "72" NA
#> No. of observations "861" NA
#> adj. OR(95%CI) adj. P value
#> NA NA
#> Estimated correlation parameters NA NA
#> No. of clusters NA NA
#> No. of observations NA NAlmerMod or glmerMod object from lme4 packagelibrary(lme4)
l1 = lmer(Weight ~ Time + Cu + (1|Pig) + (1|Evit), 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.=1 <NA> NA <NA>
#> 2 -0.57 (-4.66,3.52) 0.7837028 -0.81 (-4.42,2.8)
#> 3 1.9 (-2.23,6.04) 0.3666829 1.78 (-1.87,5.43)
#> Random effects <NA> NA <NA>
#> Pig 39.71 (27.82,54.93) NA <NA>
#> Evit 0.9 (0,13.45) NA <NA>
#> Residual 11.37 (10.3,12.55) NA <NA>
#> Metrics <NA> NA <NA>
#> No. of groups (Pig) 72 NA <NA>
#> No. of groups (Evit) 3 NA <NA>
#> No. of observations 861 NA <NA>
#> Log-likelihood -2400.69 NA <NA>
#> AIC value 4801.38 NA <NA>
#> adj. P value
#> Time 0.0000000
#> Cu: ref.=1 NA
#> 2 0.6598522
#> 3 0.3393579
#> Random effects NA
#> Pig NA
#> Evit NA
#> Residual NA
#> Metrics NA
#> No. of groups (Pig) NA
#> No. of groups (Evit) NA
#> No. of observations NA
#> Log-likelihood NA
#> AIC value NA
#>
#> $caption
#> [1] "Linear mixed model fit by REML : Weight ~ Time + Cu + (1 | Pig) + (1 | Evit)"
l2 = glmer(ddn ~ Weight + Time + (1|Pig), data= dietox, family= "binomial")
lmer.display(l2)
#> $table
#> crude OR(95%CI) crude P value adj. OR(95%CI)
#> Weight 1 (0.99,1) 0.6400477 1 (0.98,1.02)
#> Time 0.99 (0.95,1.03) 0.5851545 0.98 (0.85,1.12)
#> Random effects <NA> NA <NA>
#> Pig 0 NA <NA>
#> Metrics <NA> NA <NA>
#> No. of groups (Pig) 72 NA <NA>
#> No. of observations 861 NA <NA>
#> Log-likelihood -596.42 NA <NA>
#> AIC value 1200.84 NA <NA>
#> adj. P value
#> Weight 0.8469379
#> Time 0.7326996
#> Random effects NA
#> Pig NA
#> Metrics NA
#> No. of groups (Pig) NA
#> No. of observations NA
#> Log-likelihood NA
#> AIC value NA
#>
#> $caption
#> [1] "Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) : ddn ~ Weight + Time + (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 NA NA NA
#> No. of events 163 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 NA NA NA
#> No. of events 163 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+cname+mobility + tt2, design=dstrat)
ds2 <- svyglm(tt~ell+meals+cname+mobility + 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
#> ell "-3.73 (-4.35,-3.11)" "< 0.001"
#> meals "-3.38 (-3.71,-3.05)" "< 0.001"
#> cname: ref.=Alameda NA NA
#> Amador "47.84 (-52.72,148.4)" "0.353"
#> Butte "-68.16 (-168.72,32.4)" "0.186"
#> Colusa "-143.16 (-243.72,-42.6)" "0.006"
#> Contra Costa "83.73 (-38.42,205.89)" "0.181"
#> El Dorado "28.65 (-82.39,139.68)" "0.614"
#> Fresno "-141.53 (-263.93,-19.12)" "0.025"
#> Humboldt "16.84 (-83.72,117.4)" "0.743"
#> Inyo "-15.72 (-119.64,88.19)" "0.767"
#> Kern "-16.93 (-162.52,128.67)" "0.82"
#> Kings "-166.16 (-266.72,-65.6)" "0.001"
#> Los Angeles "-61.65 (-170.91,47.61)" "0.27"
#> Marin "136.85 (14.32,259.38)" "0.03"
#> Mariposa "10.84 (-89.72,111.4)" "0.833"
#> Mendocino "-63.14 (-163.71,37.43)" "0.22"
#> Merced "-124.3 (-226.55,-22.05)" "0.018"
#> Monterey "-32.33 (-181.51,116.86)" "0.672"
#> Napa "-35.16 (-135.72,65.4)" "0.494"
#> Orange "15.8 (-107.96,139.56)" "0.803"
#> Placer "111.2 (-24.09,246.48)" "0.109"
#> Riverside "-104.26 (-208.46,-0.06)" "0.052"
#> Sacramento "-3.86 (-146.36,138.64)" "0.958"
#> San Bernardino "-127.61 (-246.83,-8.38)" "0.038"
#> San Diego "8.96 (-110.28,128.2)" "0.883"
#> San Francisco "-163.11 (-272.65,-53.56)" "0.004"
#> San Joaquin "-2.07 (-106.6,102.45)" "0.969"
#> San Mateo "79.45 (-98.09,257)" "0.382"
#> Santa Barbara "47.84 (-52.72,148.4)" "0.353"
#> Santa Clara "-31.04 (-165.5,103.42)" "0.652"
#> Santa Cruz "57.37 (-43.49,158.24)" "0.267"
#> Shasta "-32.66 (-151.88,86.56)" "0.592"
#> Siskiyou "84.84 (-15.72,185.4)" "0.1"
#> Solano "48.84 (-51.72,149.4)" "0.343"
#> Sonoma "52.84 (-90.54,196.21)" "0.471"
#> Stanislaus "16.84 (-83.72,117.4)" "0.743"
#> Tehama "69.84 (-30.72,170.4)" "0.175"
#> Tulare "-68.08 (-189.66,53.49)" "0.274"
#> Tuolumne "17.84 (-82.72,118.4)" "0.729"
#> Ventura "12.01 (-106.02,130.04)" "0.842"
#> Yolo "-76.14 (-184.86,32.57)" "0.172"
#> mobility "-1.43 (-3.3,0.44)" "0.137"
#> tt2: 1 vs 0 "10.98 (-34.16,56.12)" "0.634"
#> adj. coeff.(95%CI) adj. P value
#> ell "-0.7 (-1.53,0.13)" "0.1"
#> meals "-3.29 (-3.96,-2.63)" "< 0.001"
#> cname: ref.=Alameda NA NA
#> Amador "-53.93 (-110.17,2.32)" "0.062"
#> Butte "-21.1 (-85.36,43.15)" "0.521"
#> Colusa "-38.75 (-98.1,20.6)" "0.203"
#> Contra Costa "-5.5 (-82.43,71.43)" "0.889"
#> El Dorado "-63.5 (-122.31,-4.68)" "0.036"
#> Fresno "40.55 (-40.49,121.58)" "0.328"
#> Humboldt "-114.4 (-170.09,-58.72)" "< 0.001"
#> Inyo "-32.29 (-112.96,48.37)" "0.434"
#> Kern "3.74 (-67.51,74.98)" "0.918"
#> Kings "-56.85 (-125.93,12.23)" "0.109"
#> Los Angeles "19.49 (-47.64,86.61)" "0.57"
#> Marin "6.26 (-89.25,101.77)" "0.898"
#> Mariposa "-87.63 (-143.96,-31.3)" "0.003"
#> Mendocino "7.71 (-106.73,122.14)" "0.895"
#> Merced "-17.59 (-92.74,57.57)" "0.647"
#> Monterey "-23.17 (-102.88,56.55)" "0.57"
#> Napa "-53.44 (-112.67,5.79)" "0.079"
#> Orange "58.31 (-6,122.61)" "0.078"
#> Placer "4.71 (-76.87,86.29)" "0.91"
#> Riverside "-55.23 (-125.6,15.14)" "0.126"
#> Sacramento "-28.47 (-110.05,53.11)" "0.495"
#> San Bernardino "-50.28 (-123.21,22.66)" "0.179"
#> San Diego "20.35 (-41.69,82.4)" "0.521"
#> San Francisco "-125.39 (-233.85,-16.93)" "0.025"
#> San Joaquin "-50.45 (-115.19,14.29)" "0.129"
#> San Mateo "-26.21 (-144.27,91.85)" "0.664"
#> Santa Barbara "-18.6 (-73.55,36.36)" "0.508"
#> Santa Clara "-9.75 (-85.89,66.39)" "0.802"
#> Santa Cruz "-20.95 (-79.87,37.98)" "0.487"
#> Shasta "72.01 (-31.59,175.6)" "0.175"
#> Siskiyou "37.72 (-22.34,97.78)" "0.22"
#> Solano "34.26 (-31.39,99.91)" "0.308"
#> Sonoma "11.83 (-65.01,88.66)" "0.763"
#> Stanislaus "-105.52 (-161.41,-49.63)" "< 0.001"
#> Tehama "99.7 (34.38,165.02)" "0.003"
#> Tulare "16.4 (-53.88,86.68)" "0.648"
#> Tuolumne "-60.52 (-117.27,-3.77)" "0.038"
#> Ventura "-2.86 (-63.78,58.07)" "0.927"
#> Yolo "12.21 (-87.11,111.53)" "0.81"
#> mobility "0.17 (-0.62,0.96)" "0.674"
#> tt2: 1 vs 0 "15.01 (-8.5,38.52)" "0.213"
#>
#> $last.lines
#> [1] "No. of observations = 200\nAIC value = 2333.9174\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
#> ell "1.02 (1,1.05)" "0.047"
#> meals "1.01 (0.99,1.03)" "0.255"
#> cname: ref.=Alameda NA NA
#> Amador "0 (0,0)" "< 0.001"
#> Butte "0 (0,0)" "< 0.001"
#> Colusa "0 (0,0)" "< 0.001"
#> Contra Costa "0 (0,0)" "< 0.001"
#> El Dorado "0 (0,0)" "< 0.001"
#> Fresno "1.74 (0.12,25.84)" "0.687"
#> Humboldt "0 (0,0)" "< 0.001"
#> Inyo "0 (0,0)" "< 0.001"
#> Kern "1.73 (0.09,33.26)" "0.717"
#> Kings "0 (0,0)" "< 0.001"
#> Los Angeles "1.86 (0.19,18.54)" "0.598"
#> Marin "0 (0,0)" "< 0.001"
#> Mariposa "0 (0,0)" "< 0.001"
#> Mendocino "0 (0,0)" "< 0.001"
#> Merced "0 (0,0)" "< 0.001"
#> Monterey "0 (0,0)" "< 0.001"
#> Napa "0 (0,0)" "< 0.001"
#> Orange "1.58 (0.11,22.23)" "0.734"
#> Placer "0 (0,0)" "< 0.001"
#> Riverside "2.95 (0.23,38.08)" "0.408"
#> Sacramento "0 (0,0)" "< 0.001"
#> San Bernardino "0.74 (0.04,14.17)" "0.839"
#> San Diego "3.89 (0.29,51.72)" "0.306"
#> San Francisco "0 (0,0)" "< 0.001"
#> San Joaquin "0 (0,0)" "< 0.001"
#> San Mateo "0 (0,0)" "< 0.001"
#> Santa Barbara "0 (0,0)" "< 0.001"
#> Santa Clara "0 (0,0)" "< 0.001"
#> Santa Cruz "0 (0,0)" "< 0.001"
#> Shasta "0 (0,0)" "< 0.001"
#> Siskiyou "0 (0,0)" "< 0.001"
#> Solano "0 (0,0)" "< 0.001"
#> Sonoma "0 (0,0)" "< 0.001"
#> Stanislaus "0 (0,0)" "< 0.001"
#> Tehama "0 (0,0)" "< 0.001"
#> Tulare "0 (0,0)" "< 0.001"
#> Tuolumne "0 (0,0)" "< 0.001"
#> Ventura "7.01 (0.56,88.41)" "0.134"
#> Yolo "0 (0,0)" "< 0.001"
#> mobility "1.01 (0.98,1.03)" "0.506"
#> tt2: 1 vs 0 "0 (0,0)" "< 0.001"
#> adj. OR.(95%CI)
#> ell "8642653.71 (3481743.02,21453468.19)"
#> meals "0.04 (0.03,0.05)"
#> cname: ref.=Alameda NA
#> Amador "Inf (Inf,Inf)"
#> Butte "Inf (Inf,Inf)"
#> Colusa "6.00369858230608e+173 (2.66764006028554e+164,1.35117166681497e+183)"
#> Contra Costa "0 (0,0)"
#> El Dorado "0.05 (0,0.83)"
#> Fresno "0 (0,0)"
#> Humboldt "Inf (Inf,Inf)"
#> Inyo "3.43810424017151e+27 (4.73418041953878e+25,2.49685472853967e+29)"
#> Kern "1.0235387865582e+47 (7.3369054568865e+44,1.42789307255652e+49)"
#> Kings "Inf (Inf,Inf)"
#> Los Angeles "0 (0,0)"
#> Marin "Inf (Inf,Inf)"
#> Mariposa "Inf (Inf,Inf)"
#> Mendocino "0 (0,0)"
#> Merced "Inf (Inf,Inf)"
#> Monterey "1.24710898684862e+117 (9.98245683482956e+110,1.55801407490399e+123)"
#> Napa "Inf (Inf,Inf)"
#> Orange "0 (0,0)"
#> Placer "Inf (Inf,Inf)"
#> Riverside "8.00352162045419e+65 (5.22981865875843e+62,1.2248294349901e+69)"
#> Sacramento "9.37864737889276e+272 (7.67834271605149e+257,1.14554702636201e+288)"
#> San Bernardino "0 (0,0)"
#> San Diego "0 (0,0)"
#> San Francisco "3.10414261410186e+147 (3.65023500899351e+139,2.63974822030432e+155)"
#> San Joaquin "3.77939736089423e+268 (4.84287658008561e+253,2.94945455976946e+283)"
#> San Mateo "Inf (Inf,Inf)"
#> Santa Barbara "Inf (Inf,Inf)"
#> Santa Clara "0 (0,0)"
#> Santa Cruz "1.16743603078086e+38 (4.00441011940044e+35,3.40351473831915e+40)"
#> Shasta "Inf (Inf,Inf)"
#> Siskiyou "Inf (Inf,Inf)"
#> Solano "0 (0,0.03)"
#> Sonoma "0 (0,0)"
#> Stanislaus "Inf (Inf,Inf)"
#> Tehama "Inf (Inf,Inf)"
#> Tulare "0 (0,0)"
#> Tuolumne "Inf (Inf,Inf)"
#> Ventura "2.48993768075818e+63 (9.37351475338794e+59,6.61415682075774e+66)"
#> Yolo "1.87101280579666e+225 (1.70374718749845e+213,2.05469974955311e+237)"
#> mobility "159609.3 (78913.87,322821.96)"
#> tt2: 1 vs 0 "0 (0,0)"
#> adj. P value
#> ell "< 0.001"
#> meals "< 0.001"
#> cname: ref.=Alameda NA
#> Amador "< 0.001"
#> Butte "< 0.001"
#> Colusa "< 0.001"
#> Contra Costa "< 0.001"
#> El Dorado "0.038"
#> Fresno "< 0.001"
#> Humboldt "< 0.001"
#> Inyo "< 0.001"
#> Kern "< 0.001"
#> Kings "< 0.001"
#> Los Angeles "< 0.001"
#> Marin "< 0.001"
#> Mariposa "< 0.001"
#> Mendocino "< 0.001"
#> Merced "< 0.001"
#> Monterey "< 0.001"
#> Napa "< 0.001"
#> Orange "< 0.001"
#> Placer "< 0.001"
#> Riverside "< 0.001"
#> Sacramento "< 0.001"
#> San Bernardino "< 0.001"
#> San Diego "< 0.001"
#> San Francisco "< 0.001"
#> San Joaquin "< 0.001"
#> San Mateo "< 0.001"
#> Santa Barbara "< 0.001"
#> Santa Clara "< 0.001"
#> Santa Cruz "< 0.001"
#> Shasta "< 0.001"
#> Siskiyou "< 0.001"
#> Solano "< 0.001"
#> Sonoma "< 0.001"
#> Stanislaus "< 0.001"
#> Tehama "< 0.001"
#> Tulare "< 0.001"
#> Tuolumne "< 0.001"
#> Ventura "< 0.001"
#> Yolo "< 0.001"
#> mobility "< 0.001"
#> tt2: 1 vs 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 (1.01,118.55)"
#> 3 "9.78 (1.37,69.94)" "0.023" "17.03 (1.69,171.6)"
#> 4 "22.89 (3.2,163.48)" "0.002" "22.56 (2.25,226.42)"
#> 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)
#TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = data.design, line = F)