setup {NMA}

Transforming arm-level data to contrast-based summary statistics and making objects for network meta-analysis

Description

A setup function to generate R objects that can be used for network meta-analysis. Users should prepare arm-level datasets, and the setup function transforms the arm-level data to the contrast-based summary statistics. Both of dichotomous and continuous outcomes can be treated. The type of outcome variable can be specified by the measure. If the measure is specified as OR, RR or RD, the outcome should be dichotomous, and d and n are needed to compute the summary statistics. Besides, if the measure is specified as MD or SMD, the outcome should be continuous, and m, s and n are needed to compute the summary statistics. Also, if the measure is specified as HR, the outcome should be survival (time-to-event), and d and n are needed to compute the summary statistics. Note HR corresponds to rate-ratio in ordinary sense and this option corresponds to the person-time analysis; hazard ratio accords to rate-ratio if the survival time distribution is exponential distribution. Several covariates can be involved as z for network meta-regression analysis (nmareg) and transitivity analysis (transitivity).

Usage

setup(study,trt,d,n,m,s,z,measure,ref,data)

Arguments

study

Study ID

trt

Treatment variable. It can be formed as both of numbered treatment (=1,2,...) and characters (e.g., "Placebo", "ARB", "Beta blocker").

d

Number of events (for dichotomous outcome and survival outcome).

n

Sample size (for dichotomous and continuous outcome) or total person-time at risk (for survival outcome).

m

Mean of the outcome variable (for continuous outcome).

s

Standard deviation of the outcome variable (for continuous outcome).

z

Covariate name vector to be used for network meta-regression analysis or transitivity analysis (optional).

measure

Outcome measure (can be OR (odds ratio), RR (risk ratio), and RD (risk difference) for dichotomous outcome, MD (mean difference) and SMD (standardized mean difference) for continuous outcome, and HR (hazard ratio) for survival outcome.

ref

Reference treatment category that should be involved in trt.

data

A data frame that involves the arm-based data.

Value

Contrast-based summary statistics are generated.

• coding: A table that presents the correspondence between the numerical code and treatment categories (the reference category is coded as 1).

• reference: Reference treatment category.

• measure: Outcome measure.

• covariate: Covariate name(s).

• N: The number of study.

• p: The dimension of the contrast-based statistics.

• df: The degree of freedom.

• study: The ID variable that specifies studies.

• trt: The original vector that specifies treatment categories.

• treat: A numerical vector that specifies treatment categories based on the coding table.

• d: The original vector that specifies number of events.

• n: The original vector that specifies sample sizes.

• m: The original vector that specifies means.

• s: The original vector that specifies standard deviations.

• Z: The data frame that specifies covariates matrix (design matrix).

• y: Contrast-based summary estimates.

• S: Vectored within-study covariance matrix.

References

Noma, H. (2023). Within-study covariance estimators for network meta-analysis with contrast-based approach. Jxiv, 490. doi:10.51094/jxiv.490.

Examples

data(heartfailure)

hf2 <- setup(study=study,trt=trt,d=d,n=n,measure="OR",ref="Placebo",data=heartfailure)
hf3 <- setup(study=study,trt=trt,d=d,n=n,measure="RR",ref="Placebo",data=heartfailure)
hf4 <- setup(study=study,trt=trt,d=d,n=n,measure="RD",ref="Placebo",data=heartfailure)

hf5 <- setup(study=study,trt=trt,d=d,n=n,z=c(SBP,DBP,pubyear),measure="OR",
ref="Placebo",data=heartfailure)

data(antidiabetic)

ad2 <- setup(study=id,trt=t,m=y,s=sd,n=n,measure="MD",ref="Placebo",data=antidiabetic)
ad3 <- setup(study=id,trt=t,m=y,s=sd,n=n,measure="SMD",ref="Placebo",data=antidiabetic)