confounded_meta: Estimates and inference for sensitivity analyses
In ConfoundedMeta: Sensitivity Analyses for Unmeasured Confounding in Meta-Analyses

Description Usage Arguments Details Examples

Computes point estimates, standard errors, and confidence interval bounds for (1) prop, the proportion of studies with true effect sizes above .q (or below .q for an apparently preventive .yr) as a function of the bias parameters; (2) the minimum bias factor on the relative risk scale (Tmin) required to reduce to less than .r the proportion of studies with true effect sizes more extreme than .q; and (3) the counterpart to (2) in which bias is parameterized as the minimum relative risk for both confounding associations (Gmin).

1 2	confounded_meta(.q, .r = NULL, .muB = NULL, .sigB = 0, .yr, .vyr = NULL, .t2, .vt2 = NULL, CI.level = 0.95, .tail = NULL)

`.q`	True effect size that is the threshold for "scientific significance"
`.r`	For `Tmin` and `Gmin`, value to which the proportion of large effect sizes is to be reduced
`.muB`	Mean bias factor on the log scale across studies
`.sigB`	Standard deviation of log bias factor across studies
`.yr`	Pooled point estimate (on log scale) from confounded meta-analysis
`.vyr`	Estimated variance of pooled point estimate from confounded meta-analysis
`.t2`	Estimated heterogeneity (tau^2) from confounded meta-analysis
`.vt2`	Estimated variance of tau^2 from confounded meta-analysis
`CI.level`	Confidence level as a proportion
`.tail`	`above` for the proportion of effects above `.q`; `below` for the proportion of effects below `.q`. By default, is set to `above` for relative risks above 1 and to `below` for relative risks below 1.

To compute all three point estimates (prop, Tmin, and Gmin) and inference, all arguments must be non-NULL. To compute only a point estimate for prop, arguments .r, .vyr, and .vt2 can be left NULL. To compute only point estimates for Tmin and Gmin, arguments .muB, .vyr, and .vt2 can be left NULL. To compute inference for all point estimates, .vyr and .vt2 must be supplied.

d = metafor::escalc(measure="RR", ai=tpos, bi=tneg,
ci=cpos, di=cneg, data=metafor::dat.bcg)

m = metafor::rma.uni(yi= d$yi, vi=d$vi, knha=FALSE,
                     measure="RR", method="DL" ) 
yr = as.numeric(m$b)  # metafor returns on log scale
vyr = as.numeric(m$vb)
t2 = m$tau2
vt2 = m$se.tau2^2 

# obtaining all three estimators and inference
confounded_meta( .q=log(0.90), .r=0.20, .muB=log(1.5), .sigB=0.1,
                 .yr=yr, .vyr=vyr, .t2=t2, .vt2=vt2,
                 CI.level=0.95 )

# passing only arguments needed for prop point estimate
confounded_meta( .q=log(0.90), .muB=log(1.5),
                 .yr=yr, .t2=t2, CI.level=0.95 )

# passing only arguments needed for Tmin, Gmin point estimates
confounded_meta( .q=log(0.90), .r=0.20,
                 .yr=yr, .t2=t2, CI.level=0.95 )

  Value       Est        SE     CI.lo     CI.hi
1  Prop 0.6450265 0.1328846 0.3845775 0.9054755
2  Tmin 2.9341378 0.7320888 1.4992701 4.3690055
3  Gmin 5.3163693 1.4801290 2.4153697 8.2173689
  Value       Est SE CI.lo CI.hi
1  Prop 0.6427633 NA    NA    NA
2  Tmin        NA NA    NA    NA
3  Gmin        NA NA    NA    NA
Warning messages:
1: In confounded_meta(.q = log(0.9), .muB = log(1.5), .yr = yr, .t2 = t2,  :
  Cannot compute inference without .vyr and .vt2. Returning only point estimates.
2: In confounded_meta(.q = log(0.9), .muB = log(1.5), .yr = yr, .t2 = t2,  :
  Cannot compute Tmin or Gmin without .r. Returning only prop.
  Value      Est SE CI.lo CI.hi
1  Prop       NA NA    NA    NA
2  Tmin 2.934138 NA    NA    NA
3  Gmin 5.316369 NA    NA    NA
Warning message:
In confounded_meta(.q = log(0.9), .r = 0.2, .yr = yr, .t2 = t2,  :
  Cannot compute inference without .vyr and .vt2. Returning only point estimates.