powerlmm
powerlmm 0.4.0
Analisis daya untuk model efek campuran multilevel/linier longitudinal.
Tujuan powerlmm adalah untuk membantu merancang studi pengobatan longitudinal (kelompok paralel), dengan atau tanpa pengelompokan tingkat yang lebih tinggi (misalnya dikelompokkan secara longitudinal oleh terapis, kelompok, atau dokter), dan data yang hilang. Fitur utama dari paket ini adalah:
powerlmm dapat dipasang dari cran dan github.
# CRAN, version 0.4.0
install.packages( " powerlmm " )
# GitHub, dev version
devtools :: install_github( " rpsychologist/powerlmm " )Ini adalah contoh pengaturan model longitudinal tiga tingkat dengan lereng acak baik pada tingkat subjek dan kluster, dengan berbagai pola data yang hilang per kelompok perlakuan. Input standar relatif digunakan, tetapi nilai parameter mentah yang tidak standar juga dapat digunakan.
library( powerlmm )
d <- per_treatment( control = dropout_weibull( 0.3 , 2 ),
treatment = dropout_weibull( 0.2 , 2 ))
p <- study_parameters( n1 = 11 ,
n2 = 10 ,
n3 = 5 ,
icc_pre_subject = 0.5 ,
icc_pre_cluster = 0 ,
icc_slope = 0.05 ,
var_ratio = 0.02 ,
dropout = d ,
effect_size = cohend( - 0.8 ,
standardizer = " pretest_SD " ))
p
# >
# > Study setup (three-level)
# >
# > n1 = 11
# > n2 = 10 x 5 (treatment)
# > 10 x 5 (control)
# > n3 = 5 (treatment)
# > 5 (control)
# > 10 (total)
# > total_n = 50 (treatment)
# > 50 (control)
# > 100 (total)
# > dropout = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 (time)
# > 0, 0, 1, 3, 6, 9, 12, 16, 20, 25, 30 (%, control)
# > 0, 0, 1, 2, 4, 5, 8, 10, 13, 17, 20 (%, treatment)
# > icc_pre_subjects = 0.5
# > icc_pre_clusters = 0
# > icc_slope = 0.05
# > var_ratio = 0.02
# > effect_size = -0.8 (Cohen's d [SD: pretest_SD]) plot( p )get_power( p , df = " satterthwaite " )
# >
# > Power Analyis for Longitudinal Linear Mixed-Effects Models (three-level)
# > with missing data and unbalanced designs
# >
# > n1 = 11
# > n2 = 10 x 5 (treatment)
# > 10 x 5 (control)
# > n3 = 5 (treatment)
# > 5 (control)
# > 10 (total)
# > total_n = 50 (control)
# > 50 (treatment)
# > 100 (total)
# > dropout = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 (time)
# > 0, 0, 1, 3, 6, 9, 12, 16, 20, 25, 30 (%, control)
# > 0, 0, 1, 2, 4, 5, 8, 10, 13, 17, 20 (%, treatment)
# > icc_pre_subjects = 0.5
# > icc_pre_clusters = 0
# > icc_slope = 0.05
# > var_ratio = 0.02
# > effect_size = -0.8 (Cohen's d [SD: pretest_SD])
# > df = 7.971459
# > alpha = 0.05
# > power = 68%Ukuran klaster yang tidak sama juga didukung, ukuran cluster dapat berupa acak (sampel), atau distribusi marginal dapat ditentukan.
p <- study_parameters( n1 = 11 ,
n2 = unequal_clusters( 2 , 3 , 5 , 20 ),
icc_pre_subject = 0.5 ,
icc_pre_cluster = 0 ,
icc_slope = 0.05 ,
var_ratio = 0.02 ,
effect_size = cohend( - 0.8 ,
standardizer = " pretest_SD " ))
get_power( p )
# >
# > Power Analyis for Longitudinal Linear Mixed-Effects Models (three-level)
# > with missing data and unbalanced designs
# >
# > n1 = 11
# > n2 = 2, 3, 5, 20 (treatment)
# > 2, 3, 5, 20 (control)
# > n3 = 4 (treatment)
# > 4 (control)
# > 8 (total)
# > total_n = 30 (control)
# > 30 (treatment)
# > 60 (total)
# > dropout = No missing data
# > icc_pre_subjects = 0.5
# > icc_pre_clusters = 0
# > icc_slope = 0.05
# > var_ratio = 0.02
# > effect_size = -0.8 (Cohen's d [SD: pretest_SD])
# > df = 6
# > alpha = 0.05
# > power = 44%Ukuran cluster mengikuti distribusi Poisson
p <- study_parameters( n1 = 11 ,
n2 = unequal_clusters( func = rpois( 5 , 5 )), # sample from Poisson
icc_pre_subject = 0.5 ,
icc_pre_cluster = 0 ,
icc_slope = 0.05 ,
var_ratio = 0.02 ,
effect_size = cohend( - 0.8 ,
standardizer = " pretest_SD " ))
get_power( p , R = 100 , progress = FALSE ) # expected power by averaging over R realizations
# >
# > Power Analyis for Longitudinal Linear Mixed-Effects Models (three-level)
# > with missing data and unbalanced designs
# >
# > n1 = 11
# > n2 = rpois(5, 5) (treatment)
# > - (control)
# > n3 = 5 (treatment)
# > 5 (control)
# > 10 (total)
# > total_n = 24.96 (control)
# > 24.96 (treatment)
# > 49.92 (total)
# > dropout = No missing data
# > icc_pre_subjects = 0.5
# > icc_pre_clusters = 0
# > icc_slope = 0.05
# > var_ratio = 0.02
# > effect_size = -0.8 (Cohen's d [SD: pretest_SD])
# > df = 8
# > alpha = 0.05
# > power = 48% (MCSE: 1%)
# >
# > NOTE: n2 is randomly sampled. Values are the mean from R = 100 realizations.Beberapa fungsi kenyamanan juga disertakan, misalnya untuk membuat kurva daya.
x <- get_power_table( p ,
n2 = 5 : 10 ,
n3 = c( 4 , 8 , 12 ),
effect_size = cohend(c( 0.5 , 0.8 ), standardizer = " pretest_SD " ))plot( x ) Paket ini mencakup metode simulasi fleksibel yang membuatnya mudah untuk menyelidiki kinerja model yang berbeda. Sebagai contoh, mari kita bandingkan perbedaan daya antara LMM 2-level dengan 11 langkah berulang, untuk melakukan ANCOVA di posttest. Menggunakan sim_formula model yang berbeda dapat sesuai dengan set data yang sama selama simulasi.
p <- study_parameters( n1 = 11 ,
n2 = 40 ,
icc_pre_subject = 0.5 ,
cor_subject = - 0.4 ,
var_ratio = 0.02 ,
effect_size = cohend( - 0.8 ,
standardizer = " pretest_SD " ))
# 2-lvl LMM
f0 <- sim_formula( " y ~ time + time:treatment + (1 + time | subject) " )
# ANCOVA, formulas with no random effects are with using lm()
f1 <- sim_formula( " y ~ treatment + pretest " ,
data_transform = transform_to_posttest ,
test = " treatment " )
f <- sim_formula_compare( " LMM " = f0 ,
" ANCOVA " = f1 )
res <- simulate( p ,
nsim = 2000 ,
formula = f ,
cores = parallel :: detectCores( logical = FALSE )) Kami kemudian merangkum hasil menggunakan summary . Mari kita lihat secara khusus efek pengobatan.
summary( res , para = list ( " LMM " = " time:treatment " ,
" ANCOVA " = " treatment " ))
# > Model: summary
# >
# > Fixed effects: 'time:treatment', 'treatment'
# >
# > model M_est theta M_se SD_est Power Power_bw Power_satt
# > LMM -1.1 -1.1 0.32 0.32 0.93 0.93 .
# > ANCOVA -11.0 0.0 3.70 3.70 0.85 0.84 0.84
# > ---
# > Number of simulations: 2000 | alpha: 0.05
# > Time points (n1): 11
# > Subjects per cluster (n2 x n3): 40 (treatment)
# > 40 (control)
# > Total number of subjects: 80
# > ---
# > At least one of the models applied a data transformation during simulation,
# > summaries that depend on the true parameter values will no longer be correct,
# > see 'help(summary.plcp_sim)'Kami juga dapat melihat model tertentu, inilah hasilnya untuk LMM 2-LVL.
summary( res , model = " LMM " )
# > Model: LMM
# >
# > Random effects
# >
# > parameter M_est theta est_rel_bias prop_zero is_NA
# > subject_intercept 99.00 100.0 -0.00560 0 0
# > subject_slope 2.00 2.0 -0.00310 0 0
# > error 100.00 100.0 0.00086 0 0
# > cor_subject -0.39 -0.4 -0.03200 0 0
# >
# > Fixed effects
# >
# > parameter M_est theta M_se SD_est Power Power_bw Power_satt
# > (Intercept) 0.0048 0.0 1.30 1.30 0.050 . .
# > time 0.0011 0.0 0.25 0.25 0.054 . .
# > time:treatment -1.1000 -1.1 0.32 0.32 0.930 0.93 .
# > ---
# > Number of simulations: 2000 | alpha: 0.05
# > Time points (n1): 11
# > Subjects per cluster (n2 x n3): 40 (treatment)
# > 40 (control)
# > Total number of subjects: 80
# > ---
# > At least one of the models applied a data transformation during simulation,
# > summaries that depend on the true parameter values will no longer be correct,
# > see 'help(summary.plcp_sim)' Fungsi dasar paket juga diimplementasikan dalam aplikasi web yang mengkilap, yang ditujukan untuk pengguna yang kurang akrab dengan R. Luncurkan aplikasi dengan mengetik
library( powerlmm )
shiny_powerlmm()
