{"id":1912,"date":"2025-11-05T14:04:11","date_gmt":"2025-11-05T05:04:11","guid":{"rendered":"https:\/\/info.zanet.biz\/?p=1912"},"modified":"2025-11-06T16:14:01","modified_gmt":"2025-11-06T07:14:01","slug":"stan%e3%82%92%e4%bd%bf%e3%81%86%e3%83%99%e3%82%a4%e3%82%b8%e3%82%a2%e3%83%b3%e3%83%a1%e3%82%bf%e3%82%a2%e3%83%8a%e3%83%aa%e3%82%b7%e3%82%b9%e7%94%a8%e3%81%ae%e3%82%b3%e3%83%bc%e3%83%89%ef%bc%9a-3","status":"publish","type":"post","link":"https:\/\/info.zanet.biz\/?p=1912","title":{"rendered":"Stan\u3092\u4f7f\u3046\u30d9\u30a4\u30b8\u30a2\u30f3\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u7528\u306e\u30b3\u30fc\u30c9\uff1a\u6642\u9593\u30a4\u30d9\u30f3\u30c8\u30a2\u30a6\u30c8\u30ab\u30e0\u306eHR\u7528"},"content":{"rendered":"\n

\u6642\u9593\u30a4\u30d9\u30f3\u30c8\u30a2\u30a6\u30c8\u30ab\u30e0\u3064\u307e\u308a\u751f\u5b58\u5206\u6790\u306e\u30cf\u30b6\u30fc\u30c9\u6bd4Hazard Ratio (HR)\u306e\u7d71\u5408\u5024\u306895\uff05\u78ba\u4fe1\u533a\u9593Credible Interval\u3092\u3001Stan\u3092\u7528\u3044\u3066\u30d9\u30a4\u30b8\u30a2\u30f3\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3067\u6c42\u3081\u3001Forest plot\u3092\u4f5c\u6210\u3059\u308b\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u30c7\u30fc\u30bf\u306fExcel\u3067\u56f3\u306e\u3088\u3046\u306b\u6e96\u5099\u3057\u307e\u3059\u3002\u4ecb\u5165\u7fa4\u306e\u75c7\u4f8b\u6570\u3001\u5bfe\u7167\u7fa4\u306e\u75c7\u4f8b\u6570\u3001\u30cf\u30b6\u30fc\u30c9\u6bd4HR\u306e\u81ea\u7136\u5bfe\u6570\u3001\u305d\u306e\u6a19\u6e96\u8aa4\u5dee\u306e\u5024\u304c\u5fc5\u8981\u306b\u306a\u308a\u307e\u3059\u3002label\u306f\u7814\u7a76\u540d\u306e\u30e9\u30d9\u30eb\u3001\u5bfe\u7167\u3001\u4ecb\u5165\u306e\u30e9\u30d9\u30eb\u3001\u30a2\u30a6\u30c8\u30ab\u30e0\u3001\u52b9\u679c\u6307\u6a19\u306e\u30bf\u30a4\u30d7\u3001\u305d\u306e\u7565\u79f0\u3067\u3059\u3002<\/p>\n\n\n\n

\u4f7f\u7528\u6cd5\u306f\u4eca\u307e\u3067\u7d39\u4ecb\u3057\u305f\u3001RR, OR, HR, MD, SMD\u306e\u305f\u3081\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3068\u540c\u3058\u3067\u3059\u3002R\u3092\u8d77\u52d5\u3057\u3066\u304a\u304d\u3001\u30a8\u30c7\u30a3\u30bf\u30fc\u306b\u30b3\u30d4\u30fc\u3057\u305f\u4e0b\u8a18\u30b3\u30fc\u30c9\u3092\u8cbc\u308a\u4ed8\u3051\u3066\u3001\u6700\u521d\u306e5\u3064\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u8aad\u307f\u8fbc\u307f\u3092\u5b9f\u884c\u3057\u3066\u304a\u304d\u307e\u3059\u3002Excel\u306b\u623b\u3063\u3066\u3001\u30bb\u30ebB3\u304b\u3089G9\u307e\u3067\u306e\u7bc4\u56f2\u3092\u9078\u629e\u3057\u3066\u3001\u30b3\u30d4\u30fc\u64cd\u4f5c\uff08Ctrl+C)\u3092\u884c\u3063\u3066\u3001R\u306b\u623b\u308a\u3001exdato = read.delim(\u201cclipboard\u201d,sep=\u201d\\t\u201d,header=TRUE)\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002\u7d9a\u3044\u3066\u3001\u305d\u308c\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5168\u90e8\u5b9f\u884c\u3055\u305b\u308b\u3068\u3001Forest plot\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002\u52b9\u679c\u6307\u6a19\u306e\u70b9\u63a8\u5b9a\u5024\u306895\uff05\u78ba\u4fe1\u533a\u9593\u3001\u304a\u3088\u3073\u4e88\u6e2c\u533a\u9593\u3092\u63d0\u793a\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

Markov Chain Monte Carlo (MCMC)\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3046\u306e\u3067\u3001\u5c11\u3057\u6642\u9593\u304c\u304b\u304b\u308a\u307e\u3059\u3002chains = 4, warmup = 10000, iter = 30000\u306b\u8a2d\u5b9a\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u8a0880000\u500b\u304c\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3055\u308c\u307e\u3059\u3002\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u3001\u7e70\u308a\u8fd4\u3057\u306e\u56de\u6570\u306f\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u5909\u66f4\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n

Forest plot\u304c\u51fa\u529b\u3055\u308c\u305f\u6642\u70b9\u3067\u3001\u5404\u7814\u7a76\u306e\u52b9\u679c\u63a8\u5b9a\u5024\u300195\uff05\u78ba\u4fe1\u533a\u9593\u3001\u7d71\u5408\u5024\u306895\uff05\u78ba\u4fe1\u533a\u9593\u3001\u4e88\u6e2c\u533a\u9593\u306e\u5024\u3092\u30af\u30ea\u30c3\u30d7\u30dc\u30fc\u30c9\u306b\u30b3\u30d4\u30fc\u3057\u3066\u3044\u307e\u3059\u306e\u3067\u3001Excel\u8a55\u4fa1\u30b7\u30fc\u30c8\u306a\u3069\u306b\u8cbc\u308a\u4ed8\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

\u30cf\u30b6\u30fc\u30c9\u6bd4HR\u306e\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u7528\u30b3\u30fc\u30c9<\/summary>
\n
#####Bayesian meta-analysis of Hazard Raio (HR)######\n\nlibrary(rstan)\nlibrary(bayesplot)\nlibrary(dplyr)\nlibrary(HDInterval)\nlibrary(forestplot)\n\n# Get data via clipboard.\nexdato = read.delim(\"clipboard\",sep=\"\\t\",header=TRUE)\nlabem = exdato[,\"label\"]\nem = labem[6]\nlabe_study = labem[1]\nlabe_cont = labem[2]\nlabe_int = labem[3]\nlabe_outc = labem[4]\nlabe_em = labem[5]\n\nexdat=na.omit(exdato)\n# Number of studies\nK <- nrow(exdat)\n\n# \u30c7\u30fc\u30bf\u306e\u6e96\u5099\nif(colnames(exdat)[1]==\"author\"){\nstudy = exdat$author\n}\nif(colnames(exdat)[1]==\"study\"){\nstudy = exdat$study\n}\nnc=exdat$nc\nnt=exdat$nt\nmeta_data <- list(\n  K = K,\n  yi = exdat$yi,\n  sei = exdat$sei\n)\n\n#Stan model code\nstan_model_code <- \"\ndata {\n  int<lower=0> K;              \/\/ \u7814\u7a76\u6570\n  vector[K] yi;                \/\/ \u5404\u7814\u7a76\u306e logHR\n  vector<lower=0>[K] sei;      \/\/ \u5404\u7814\u7a76\u306e\u6a19\u6e96\u8aa4\u5dee\n}\nparameters {\n  real mu;                     \/\/ \u5168\u4f53\u5e73\u5747 logHR\n  real<lower=0> tau;           \/\/ \u7570\u8cea\u6027\uff08\u6a19\u6e96\u504f\u5dee\uff09\n  vector[K] theta;             \/\/ \u5404\u7814\u7a76\u306e\u771f\u306e\u52b9\u679c\n}\nmodel {\n  theta ~ normal(mu, tau);     \/\/ \u771f\u306e\u52b9\u679c\u306e\u5206\u5e03\n  yi ~ normal(theta, sei);     \/\/ \u89b3\u6e2c\u5024\u306e\u5206\u5e03\n}\ngenerated quantities {\n  real tau_sq = tau^2;         \/\/ \u7570\u8cea\u6027\u306e\u5206\u6563\n  real I2 = tau_sq \/ (tau_sq + mean(sei .* sei)); \/\/ I\u00b2\u306e\u63a8\u5b9a\n  real pred = normal_rng(mu, sqrt(tau_sq + mean(sei .* sei))); \/\/ \u4e88\u6e2c\u533a\u9593\u306e\u4e00\u4f8b\n}\"\n\n\n# Stan \u30e2\u30c7\u30eb\u306e\u30b3\u30f3\u30d1\u30a4\u30eb\u3068\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\nfit <- stan(\n  model_code = stan_model_code,\n  data = meta_data,\n  iter = 30000,\n  warmup = 10000,\n  chains = 4,\n  seed = 123,\n  control = list(adapt_delta = 0.98)\n)\n\n# \u7d50\u679c\u306e\u8868\u793a\nprint(fit, pars = c(\"mu\", \"tau\", \"tau_sq\", \"I2\", \"pred\"), probs = c(0.025, 0.5, 0.975))\n\n# \u4e8b\u5f8c\u5206\u5e03\u306e\u53ef\u8996\u5316\nposterior <- extract(fit)\n#mcmc_areas(as.data.frame(posterior), pars = c(\"mu\", \"tau\", \"I2\", \"pred\"))\n\n# Summary HR and 95% credible interval\nmu_HR = exp(mean(posterior$mu))\nmu_HR_lw = exp(quantile(posterior$mu,probs=0.025))\nmu_HR_up = exp(quantile(posterior$mu,probs=0.975))\np_val_HR = round(2*min(mean(posterior$mu >0), mean(posterior$mu <0)), 6)\t#Bayesian p-value for summary logRR\nif(mean(posterior$mu)>0){\nprob_direct_HR = round(mean(posterior$mu > 0),6)\nlabel = \"p(HR>1)=\"\n}else{\nprob_direct_HR = round(mean(posterior$mu < 0),6)\nlabel = \"p(HR<1)=\"\n}\n# Prediction Inerval and 95% credible interval\npi_HR_lw = exp(quantile(posterior$pred,probs=0.025))\npi_HR_up = exp(quantile(posterior$pred,probs=0.975))\n\n# Tau-squared mode and 95% HDI (High Density Interval) of logHR\ndens=density(posterior$tau_sq)\ntau_squared=dens$x[which.max(dens$y)]\t#mode\nci_tau_squared=hdi(posterior$tau_sq)\t#95% HDI\n\n# I-squared mode and 95% HDI (High Density Interval) of logHR\ndens=density(posterior$I2)\nI_squared=100*dens$x[which.max(dens$y)]\t\t#mode\nci_I_squared=100*hdi(posterior$I2)\t\t#95% HDI\n\n# logHR of each study = theta[,i]\nhr=rep(0,K)\nhr_lw=rep(0,K)\nhr_up=rep(0,K)\nfor(i in 1:K){\nhr[i] = exp(mean(posterior$theta[,i]))\nhr_lw[i] = exp(quantile(posterior$theta[,i],probs=0.025))\nhr_up[i] = exp(quantile(posterior$theta[,i],probs=0.975))\n}\n# \u5404\u7814\u7a76\u306e\u52b9\u679c\u63a8\u5b9a\u5024\u306e\u91cd\u307f%\u5404\u7814\u7a76\u306e\u52b9\u679c\u63a8\u5b9a\u5024\u306e\u91cd\u307f%:\u4e8b\u5f8c\u4e0d\u78ba\u5b9f\u6027\u306e\u7a0b\u5ea6\uff08\u63a8\u5b9a\u306e\u5b89\u5b9a\u6027\uff09\n# \u91cd\u307f\u3092\u683c\u7d0d\u3059\u308b\u30d9\u30af\u30c8\u30eb\u3092\u521d\u671f\u5316\nweights <- numeric(K)\nweight_percentages <- numeric(K)\nfor (i in 1:K) {\n  # i\u756a\u76ee\u306e\u7814\u7a76\u306elogRR\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u5024\u3092\u53d6\u5f97\n  theta_i_samples <- posterior$theta[, i]\n  # \u305d\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3055\u308c\u305f\u5024\u306e\u5206\u6563\u3092\u8a08\u7b97\n  # \u3053\u308c\u3092\u300c\u5b9f\u52b9\u7684\u306a\u9006\u5206\u6563\u300d\u3068\u8003\u3048\u308b\n  variance_of_theta_i <- var(theta_i_samples)\n  # \u91cd\u307f\u306f\u5206\u6563\u306e\u9006\u6570\n  weights[i] <- 1 \/ variance_of_theta_i\n}\n# \u5168\u91cd\u307f\u306e\u5408\u8a08\ntotal_weight <- sum(weights)\n# \u5404\u7814\u7a76\u306e\u91cd\u307f\u306e\u30d1\u30fc\u30bb\u30f3\u30c6\u30fc\u30b8\nweight_percentages <- (weights \/ total_weight) * 100\nwpc = format(round(weight_percentages,digits=1), nsmall=1)\nwp = weight_percentages\/100\n#Forest plot box sizes on weihts\nwbox=c(NA,NA,(K\/4)*sqrt(wp)\/sum(sqrt(wp)),0.5,0,NA)\t\n\n#Forest plot by forestplot\n#setting fs for cex\nfs=1\nif(K>20){fs=round((1-0.02*(K-20)),digits=1)}\n\nm=c(NA,NA,hr,mu_HR,mu_HR,NA)\nlw=c(NA,NA,hr_lw,mu_HR_lw,pi_HR_lw,NA)\nup=c(NA,NA,hr_up,mu_HR_up,pi_HR_up,NA)\n\nhete1=\"\"\nhete2=paste(\"I2=\",round(I_squared, 2),\"%\",sep=\"\")\n\nhete3=paste(\"tau2=\",format(round(tau_squared,digits=4),nsmall=4),sep=\"\")\n\n#hete2=paste(\"I2=\",round(mean_I_squared_logRR, 2), \"%\\n\",\" (\",round(ci_I_squared_logRR[1], 2),\" ~ \",\n#round(ci_I_squared_logRR[2], 2),\")\",sep=\"\")\n\n#hete3=paste(\"tau2=\",format(round(mean_tau_squared_logRR,digits=4),nsmall=4), \"\\n\",\n#\" (\",format(round(ci_tau_squared_logRR[1],digits=4),nsmall=4),\" ~ \",\n#format(round(ci_tau_squared_logRR[2],digits=4),nsmall=4),\")  \",sep=\"\")\n\nhete4=\"\"\nhete5=paste(\"p=\",p_val_HR, sep=\"\")\nhete6=paste(label,prob_direct_HR,sep=\"\")\n\nau=study\nsl=c(NA,toString(labe_study),as.vector(au),\"Summary Estimate\",\"Prediction Interval\",NA)\n\nspac=c(\"    \",NA,rep(NA,K),NA,NA,NA)\n\nncl=c(NA,\"Number\",nc,NA,NA,hete1)\nrcl=c(NA,NA,rep(NA,K),NA,NA,hete2)\n\nntl=c(labe_int,\"Number\",nt,NA,NA,hete3)\nrtl=spac\n\nml=c(labe_outc,labe_em,format(round(hr,digits=3),nsmall=3),format(round(mu_HR,digits=3),nsmall=3),NA,hete5)\nll=c(NA,\"95% CI lower\",format(round(hr_lw,digits=3),nsmall=3),format(round(mu_HR_lw,digits=3),nsmall=3),format(round(pi_HR_lw,digits=3),nsmall=3),hete6)\nul=c(NA,\"95% CI upper\",format(round(hr_up,digits=3),nsmall=3),format(round(mu_HR_up,digits=3),nsmall=3),format(round(pi_HR_up,digits=3),nsmall=3),NA)\nwpcl=c(NA,\"Weight(%)\",wpc,100,NA,NA)\nll=as.vector(ll)\nul=as.vector(ul)\nsum=c(TRUE,TRUE,rep(FALSE,K),TRUE,FALSE,FALSE)\nzerov=1\nlabeltext=cbind(sl,ntl,rtl,ncl,rcl,spac,ml,ll,ul,wpcl)\nhlines=list(\"3\"=gpar(lwd=1,columns=1:11,col=\"grey\"))\ndev.new()\nplot(forestplot(labeltext,mean=m,lower=lw,upper=up,is.summary=sum,graph.pos=7,\nzero=zerov,hrzl_lines=hlines,xlab=toString(labe_em),txt_gp=fpTxtGp(ticks=gpar(cex=fs),\nxlab=gpar(cex=fs),cex=fs),xticks.digits=2,vertices=TRUE,graphwidth=unit(50,\"mm\"),colgap=unit(3,\"mm\"),\nboxsize=wbox,\nlineheight=\"auto\",xlog=TRUE,new_page=FALSE))\n####\n###Pint indiv estimates with CI and copy to clipboard.\nnk=K+2\nefs=rep(\"\",nk)\nefeci=rep(\"\",nk)\nfor(i in 1:nk){\nefs[i]=ml[i+2]\nefeci[i]=paste(ll[i+2],\"~\",ul[i+2])\n}\n\nefestip=data.frame(cbind(efs,efeci))\nefestip[nk,1]=\"\"\nprint(efestip)\nwrite.table(efestip,\"clipboard\",sep=\"\\t\",row.names=FALSE,col.names=FALSE)\nprint(\"The estimates of each study and the summary estimate are in the clipboard.\")\n\n#############<\/code><\/pre>\n<\/div><\/details>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Forest plot\u304c\u51fa\u529b\u3055\u308c\u305f\u5f8c\u3001\u7d9a\u3051\u3066\u4ee5\u4e0b\u306eFunnel plot\u4f5c\u6210\u7528\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001Funnel plot\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
Funnel plot\u4f5c\u6210\u7528\u306e\u30b9\u30af\u30ea\u30d7\u30c8<\/summary>
\n
########################\nlibrary(metafor)\n\n#Plot funnel plot.\nif(em == \"HR\"){\nyi = hr\nvi=1\/weights\nsei = sqrt(1\/weights)\nmu = mu_HR\ndev.new(width=7,height=7)\nfunnel(yi, sei = sei, level = c(95), refline = mu,xlab = \"HR\", ylab = \"Standard Error\")\n}\n\n#Asymmetry test with Egger and Begg's tests.\negger=regtest(yi, sei=sei,model=\"lm\", ret.fit=FALSE)\nbegg=ranktest(yi, vi)\n\n#Print the results to the console.\nprint(\"Egger's test:\")\nprint(egger)\nprint(\"Begg's test\")\nprint(begg)\n\n#Add Begg and Egger to the plot.\nfsfn=1\nem=toString(exdat$label[6])\noutyes=toString(exdat$label[4])\nfunmax=par(\"usr\")[3]-par(\"usr\")[4]\ngyou=funmax\/12\nfxmax=par(\"usr\")[2]-(par(\"usr\")[2]-par(\"usr\")[1])\/40\ntext(fxmax,gyou*0.5,\"Begg's test\",pos=2,cex=fsfn)\nkentau=toString(round(begg$tau,digits=3))\ntext(fxmax,gyou*1.2,paste(\"Kendall's tau=\",kentau,sep=\"\"),pos=2,cex=fsfn)\nkenp=toString(round(begg$pval,digits=5))\ntext(fxmax,gyou*1.9,paste(\"p=\",kenp,sep=\"\"),pos=2,cex=fsfn)\n\ntext(fxmax,gyou*3.5,\"Egger's test\",pos=2,cex=fsfn)\ntstat=toString(round(egger$zval,digits=3))\ntext(fxmax,gyou*4.2,paste(\"t statistic=\",tstat,sep=\"\"),pos=2,cex=fsfn)\ntstap=toString(round(egger$pval,digits=5))\ntext(fxmax,gyou*5.1,paste(\"p=\",tstap,sep=\"\"),pos=2,cex=fsfn)\n#####################################<\/code><\/pre>\n<\/div><\/details>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Funnel plot\u306e\u975e\u5bfe\u79f0\u6027\u306e\u691c\u5b9a\u6cd5\u3067\u3042\u308bBegg\u306e\u691c\u5b9a\u3001Egger\u306e\u691c\u5b9a\u3092\u51fa\u7248\u30d0\u30a4\u30a2\u30b9\u306e\u8a55\u4fa1\u306b\u7528\u3044\u308b\u5834\u5408\u3001\u7814\u7a76\u6570\u304c10\u4ee5\u4e0a\uff085\u4ee5\u4e0a\u3068\u3044\u3046\u7814\u7a76\u8005\u3082\u3044\u308b\uff09\u5fc5\u8981\u3068\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u6642\u9593\u30a4\u30d9\u30f3\u30c8\u30a2\u30a6\u30c8\u30ab\u30e0\u3064\u307e\u308a\u751f\u5b58\u5206\u6790\u306e\u30cf\u30b6\u30fc\u30c9\u6bd4Hazard Ratio (HR)\u306e\u7d71\u5408\u5024\u306895\uff05\u78ba\u4fe1\u533a\u9593Credible Interval\u3092\u3001Stan\u3092\u7528\u3044\u3066\u30d9\u30a4\u30b8\u30a2\u30f3\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3067\u6c42\u3081\u3001Forest plot\u3092\u4f5c\u6210 … “Stan\u3092\u4f7f\u3046\u30d9\u30a4\u30b8\u30a2\u30f3\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u7528\u306e\u30b3\u30fc\u30c9\uff1a\u6642\u9593\u30a4\u30d9\u30f3\u30c8\u30a2\u30a6\u30c8\u30ab\u30e0\u306eHR\u7528” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32,2],"tags":[],"class_list":["post-1912","post","type-post","status-publish","format-standard","hentry","category-statistics","category-sr"],"_links":{"self":[{"href":"https:\/\/info.zanet.biz\/index.php?rest_route=\/wp\/v2\/posts\/1912","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/info.zanet.biz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/info.zanet.biz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/info.zanet.biz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/info.zanet.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1912"}],"version-history":[{"count":3,"href":"https:\/\/info.zanet.biz\/index.php?rest_route=\/wp\/v2\/posts\/1912\/revisions"}],"predecessor-version":[{"id":1918,"href":"https:\/\/info.zanet.biz\/index.php?rest_route=\/wp\/v2\/posts\/1912\/revisions\/1918"}],"wp:attachment":[{"href":"https:\/\/info.zanet.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1912"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/info.zanet.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1912"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/info.zanet.biz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1912"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}