Generate (population-average) relative treatment effects. If a ML-NMR or meta-regression model was fitted, these are specific to each study population.
Usage
relative_effects(
x,
newdata = NULL,
study = NULL,
all_contrasts = FALSE,
trt_ref = NULL,
probs = c(0.025, 0.25, 0.5, 0.75, 0.975),
predictive_distribution = FALSE,
summary = TRUE
)Arguments
- x
A
stan_nmaobject created bynma()- newdata
Only used if a regression model is fitted. A data frame of study details, one row per study, giving the covariate values at which to produce relative effects. Column names must match variables in the regression model. If
NULL, relative effects are produced for all studies in the network.- study
Column of
newdatawhich specifies study names, otherwise studies will be labelled by row number.- all_contrasts
Logical, generate estimates for all contrasts (
TRUE), or just the "basic" contrasts against the network reference treatment (FALSE)? DefaultFALSE.- trt_ref
Reference treatment to construct relative effects against, if
all_contrasts = FALSE. By default, relative effects will be against the network reference treatment. Coerced to character string.- probs
Numeric vector of quantiles of interest to present in computed summary, default
c(0.025, 0.25, 0.5, 0.75, 0.975)- predictive_distribution
Logical, when a random effects model has been fitted, should the predictive distribution for relative effects in a new study be returned? Default
FALSE.- summary
Logical, calculate posterior summaries? Default
TRUE.
Value
A nma_summary object if summary = TRUE, otherwise a list
containing a 3D MCMC array of samples and (for regression models) a data
frame of study information.
See also
plot.nma_summary() for plotting the relative effects.
Examples
## Smoking cessation
# \donttest{
# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re", run.donttest = TRUE)
# }
# \donttest{
# Produce relative effects
smk_releff_RE <- relative_effects(smk_fit_RE)
smk_releff_RE
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS
#> d[Group counselling] 1.11 0.43 0.28 0.83 1.09 1.38 1.96 2243
#> d[Individual counselling] 0.85 0.24 0.38 0.69 0.85 1.00 1.34 1190
#> d[Self-help] 0.50 0.40 -0.30 0.25 0.50 0.76 1.28 1897
#> Tail_ESS Rhat
#> d[Group counselling] 2523 1
#> d[Individual counselling] 2147 1
#> d[Self-help] 2560 1
plot(smk_releff_RE, ref_line = 0)
# Relative effects for all pairwise comparisons
relative_effects(smk_fit_RE, all_contrasts = TRUE)
#> mean sd 2.5% 25% 50%
#> d[Group counselling vs. No intervention] 1.11 0.43 0.28 0.83 1.09
#> d[Individual counselling vs. No intervention] 0.85 0.24 0.38 0.69 0.85
#> d[Self-help vs. No intervention] 0.50 0.40 -0.30 0.25 0.50
#> d[Individual counselling vs. Group counselling] -0.26 0.41 -1.07 -0.52 -0.25
#> d[Self-help vs. Group counselling] -0.61 0.48 -1.60 -0.92 -0.60
#> d[Self-help vs. Individual counselling] -0.35 0.41 -1.19 -0.61 -0.35
#> 75% 97.5% Bulk_ESS Tail_ESS
#> d[Group counselling vs. No intervention] 1.38 1.96 2243 2523
#> d[Individual counselling vs. No intervention] 1.00 1.34 1190 2147
#> d[Self-help vs. No intervention] 0.76 1.28 1897 2560
#> d[Individual counselling vs. Group counselling] 0.00 0.53 3067 2803
#> d[Self-help vs. Group counselling] -0.29 0.34 3251 2655
#> d[Self-help vs. Individual counselling] -0.09 0.44 2409 2741
#> Rhat
#> d[Group counselling vs. No intervention] 1
#> d[Individual counselling vs. No intervention] 1
#> d[Self-help vs. No intervention] 1
#> d[Individual counselling vs. Group counselling] 1
#> d[Self-help vs. Group counselling] 1
#> d[Self-help vs. Individual counselling] 1
# Relative effects against a different reference treatment
relative_effects(smk_fit_RE, trt_ref = "Self-help")
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS
#> d[No intervention] -0.50 0.40 -1.28 -0.76 -0.50 -0.25 0.30 1897
#> d[Group counselling] 0.61 0.48 -0.34 0.29 0.60 0.92 1.60 3251
#> d[Individual counselling] 0.35 0.41 -0.44 0.09 0.35 0.61 1.19 2409
#> Tail_ESS Rhat
#> d[No intervention] 2560 1
#> d[Group counselling] 2655 1
#> d[Individual counselling] 2741 1
# Transforming to odds ratios
# We work with the array of relative effects samples
LOR_array <- as.array(smk_releff_RE)
OR_array <- exp(LOR_array)
# mcmc_array objects can be summarised to produce a nma_summary object
smk_OR_RE <- summary(OR_array)
# This can then be printed or plotted
smk_OR_RE
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS
#> d[Group counselling] 3.32 1.54 1.32 2.28 2.97 3.99 7.10 2243 2523
#> d[Individual counselling] 2.41 0.60 1.47 2.00 2.33 2.73 3.83 1190 2147
#> d[Self-help] 1.78 0.74 0.74 1.28 1.64 2.13 3.59 1897 2560
#> Rhat
#> d[Group counselling] 1
#> d[Individual counselling] 1
#> d[Self-help] 1
plot(smk_OR_RE, ref_line = 1)
# }
## Plaque psoriasis ML-NMR
# \donttest{
# Run plaque psoriasis ML-NMR example if not already available
if (!exists("pso_fit")) example("example_pso_mlnmr", run.donttest = TRUE)
# }
# \donttest{
# Produce population-adjusted relative effects for all study populations in
# the network
pso_releff <- relative_effects(pso_fit)
pso_releff
#> ---------------------------------------------------------------- Study: FIXTURE ----
#>
#> Covariate values:
#> durnpso prevsys bsa weight psa
#> 1.6 0.62 0.34 8.34 0.14
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d[FIXTURE: ETN] 1.66 0.09 1.49 1.60 1.66 1.72 1.84 4598 3403 1
#> d[FIXTURE: IXE_Q2W] 3.03 0.10 2.85 2.96 3.03 3.09 3.22 6002 3534 1
#> d[FIXTURE: IXE_Q4W] 2.62 0.09 2.43 2.55 2.61 2.68 2.81 6290 3002 1
#> d[FIXTURE: SEC_150] 2.22 0.12 2.00 2.14 2.22 2.30 2.45 4332 3534 1
#> d[FIXTURE: SEC_300] 2.53 0.12 2.30 2.45 2.52 2.60 2.77 4934 3331 1
#>
#> -------------------------------------------------------------- Study: UNCOVER-1 ----
#>
#> Covariate values:
#> durnpso prevsys bsa weight psa
#> 2 0.73 0.28 9.24 0.28
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS
#> d[UNCOVER-1: ETN] 1.51 0.09 1.34 1.45 1.51 1.56 1.67 5094 3396
#> d[UNCOVER-1: IXE_Q2W] 2.92 0.09 2.76 2.87 2.92 2.98 3.09 5939 3525
#> d[UNCOVER-1: IXE_Q4W] 2.51 0.08 2.35 2.46 2.51 2.57 2.67 6261 3139
#> d[UNCOVER-1: SEC_150] 2.12 0.12 1.88 2.03 2.11 2.20 2.35 5012 3511
#> d[UNCOVER-1: SEC_300] 2.42 0.12 2.18 2.34 2.42 2.50 2.66 5648 3752
#> Rhat
#> d[UNCOVER-1: ETN] 1
#> d[UNCOVER-1: IXE_Q2W] 1
#> d[UNCOVER-1: IXE_Q4W] 1
#> d[UNCOVER-1: SEC_150] 1
#> d[UNCOVER-1: SEC_300] 1
#>
#> -------------------------------------------------------------- Study: UNCOVER-2 ----
#>
#> Covariate values:
#> durnpso prevsys bsa weight psa
#> 1.87 0.64 0.27 9.17 0.24
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS
#> d[UNCOVER-2: ETN] 1.51 0.08 1.34 1.45 1.51 1.56 1.67 5035 3505
#> d[UNCOVER-2: IXE_Q2W] 2.92 0.08 2.76 2.87 2.92 2.98 3.09 6035 3397
#> d[UNCOVER-2: IXE_Q4W] 2.51 0.08 2.35 2.46 2.51 2.57 2.67 6391 3232
#> d[UNCOVER-2: SEC_150] 2.12 0.12 1.89 2.03 2.11 2.20 2.35 5005 3667
#> d[UNCOVER-2: SEC_300] 2.42 0.12 2.19 2.34 2.42 2.50 2.66 5603 3444
#> Rhat
#> d[UNCOVER-2: ETN] 1
#> d[UNCOVER-2: IXE_Q2W] 1
#> d[UNCOVER-2: IXE_Q4W] 1
#> d[UNCOVER-2: SEC_150] 1
#> d[UNCOVER-2: SEC_300] 1
#>
#> -------------------------------------------------------------- Study: UNCOVER-3 ----
#>
#> Covariate values:
#> durnpso prevsys bsa weight psa
#> 1.78 0.59 0.28 9.01 0.2
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS
#> d[UNCOVER-3: ETN] 1.53 0.08 1.37 1.48 1.53 1.58 1.69 4966 3444
#> d[UNCOVER-3: IXE_Q2W] 2.94 0.08 2.78 2.88 2.94 3.00 3.10 6124 3454
#> d[UNCOVER-3: IXE_Q4W] 2.53 0.08 2.37 2.48 2.53 2.58 2.69 6486 3242
#> d[UNCOVER-3: SEC_150] 2.13 0.11 1.91 2.06 2.13 2.21 2.36 4870 3615
#> d[UNCOVER-3: SEC_300] 2.44 0.12 2.21 2.36 2.44 2.51 2.67 5469 3246
#> Rhat
#> d[UNCOVER-3: ETN] 1
#> d[UNCOVER-3: IXE_Q2W] 1
#> d[UNCOVER-3: IXE_Q4W] 1
#> d[UNCOVER-3: SEC_150] 1
#> d[UNCOVER-3: SEC_300] 1
#>
plot(pso_releff, ref_line = 0)
# Produce population-adjusted relative effects for a different target
# population
new_agd_means <- data.frame(
bsa = 0.6,
prevsys = 0.1,
psa = 0.2,
weight = 10,
durnpso = 3)
relative_effects(pso_fit, newdata = new_agd_means)
#> ------------------------------------------------------------------ Study: New 1 ----
#>
#> Covariate values:
#> durnpso prevsys bsa weight psa
#> 3 0.1 0.6 10 0.2
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d[New 1: ETN] 1.25 0.23 0.82 1.09 1.25 1.40 1.71 7482 3159 1
#> d[New 1: IXE_Q2W] 2.89 0.22 2.46 2.74 2.88 3.03 3.36 8703 3138 1
#> d[New 1: IXE_Q4W] 2.48 0.22 2.06 2.32 2.47 2.62 2.93 8985 3121 1
#> d[New 1: SEC_150] 2.08 0.22 1.65 1.93 2.08 2.23 2.53 8107 3052 1
#> d[New 1: SEC_300] 2.38 0.23 1.94 2.23 2.38 2.54 2.86 8676 3121 1
#>
# }