Plots for sensitivity analyses — sens

Produces line plots (type="line") showing the bias factor on the relative risk (RR) scale vs. the proportion of studies with true RRs above q (or below it for an apparently preventive relative risk). The plot secondarily includes a X-axis scaled based on the minimum strength of confounding to produce the given bias factor. The shaded region represents a 95% pointwise confidence band. Alternatively, produces distribution plots (type="dist") for a specific bias factor showing the observed and true distributions of RRs with a red line marking exp(q).

sens_plot(
  type,
  q,
  muB = NA,
  Bmin = log(1),
  Bmax = log(5),
  sigB = 0,
  yr,
  vyr = NA,
  t2,
  vt2 = NA,
  breaks.x1 = NA,
  breaks.x2 = NA,
  CI.level = 0.95
)

Arguments

type	`dist` for distribution plot; `line` for line plot (see Details)
q	True effect size that is the threshold for "scientific significance"
muB	Single mean bias factor on log scale (only needed for distribution plot)
Bmin	Lower limit of lower X-axis on the log scale (only needed for line plot)
Bmax	Upper limit of lower X-axis on the log scale (only needed for line plot)
sigB	Standard deviation of log bias factor across studies (length 1)
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
breaks.x1	Breaks for lower X-axis (bias factor) on RR scale (optional for line plot; not used for distribution plot)
breaks.x2	Breaks for upper X-axis (confounding strength) on RR scale (optional for line plot; not used for distribution plot)
CI.level	Pointwise confidence level as a proportion

Details

Arguments vyr and vt2 can be left NA, in which case no confidence band will appear on the line plot.

Examples

# with variable bias and with confidence band
sens_plot( type="line", q=log(1.1), Bmin=log(1), Bmax=log(4), sigB=0.1,
           yr=log(1.3), vyr=0.005, t2=0.4, vt2=0.03 )
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Use of `t$eB` is discouraged. Use `eB` instead.
#> Warning: Use of `t$phat` is discouraged. Use `phat` instead.
#> Warning: Use of `t$lo` is discouraged. Use `lo` instead.
#> Warning: Use of `t$hi` is discouraged. Use `hi` instead.
#> Warning: Use of `t$eB` is discouraged. Use `eB` instead.
#> Warning: Use of `t$phat` is discouraged. Use `phat` instead.

# with fixed bias and without confidence band
sens_plot( type="line", q=log(1.1), Bmin=log(1), Bmax=log(4),
           yr=log(1.3), t2=0.4 )
#> No confidence interval because vyr or vt2 is NULL
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
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#> Cannot compute inference without vyr and vt2. Returning only point estimates.
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#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
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#> Cannot compute inference without vyr and vt2. Returning only point estimates.
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#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Cannot compute inference without vyr and vt2. Returning only point estimates.
#> Warning: Use of `t$eB` is discouraged. Use `eB` instead.
#> Warning: Use of `t$phat` is discouraged. Use `phat` instead.

# apparently preventive
sens_plot( type="line", q=log(0.90), Bmin=log(1), Bmax=log(4),
           yr=log(0.6), vyr=0.005, t2=0.4, vt2=0.04 )
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Phat is close to 0 or 1. We recommend using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
#> Warning: Use of `t$eB` is discouraged. Use `eB` instead.
#> Warning: Use of `t$phat` is discouraged. Use `phat` instead.
#> Warning: Use of `t$lo` is discouraged. Use `lo` instead.
#> Warning: Use of `t$hi` is discouraged. Use `hi` instead.
#> Warning: Use of `t$eB` is discouraged. Use `eB` instead.
#> Warning: Use of `t$phat` is discouraged. Use `phat` instead.

# distribution plot: apparently causative
# commented out because takes 5-10 seconds to run
# sens_plot( type="dist", q=log(1.1), muB=log(2),
#           yr=log(1.3), t2=0.4 )
           
# distribution plot: apparently preventive
# commented out because takes 5-10 seconds to run
# sens_plot( type="dist", q=log(0.90), muB=log(1.5),
#           yr=log(0.7), t2=0.2 )