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 0.45 0.26 0.82 1.10 1.39 2.05 1541
#> d[Individual counselling] 0.85 0.24 0.39 0.69 0.84 1.01 1.36 985
#> d[Self-help] 0.48 0.41 -0.30 0.22 0.48 0.74 1.32 1483
#> Tail_ESS Rhat
#> d[Group counselling] 2173 1
#> d[Individual counselling] 1773 1
#> d[Self-help] 1676 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 0.45 0.26 0.82 1.10
#> d[Individual counselling vs. No intervention] 0.85 0.24 0.39 0.69 0.84
#> d[Self-help vs. No intervention] 0.48 0.41 -0.30 0.22 0.48
#> d[Individual counselling vs. Group counselling] -0.26 0.42 -1.10 -0.53 -0.25
#> d[Self-help vs. Group counselling] -0.62 0.49 -1.66 -0.94 -0.62
#> d[Self-help vs. Individual counselling] -0.37 0.42 -1.19 -0.64 -0.36
#> 75% 97.5% Bulk_ESS Tail_ESS
#> d[Group counselling vs. No intervention] 1.39 2.05 1541 2173
#> d[Individual counselling vs. No intervention] 1.01 1.36 985 1773
#> d[Self-help vs. No intervention] 0.74 1.32 1483 1676
#> d[Individual counselling vs. Group counselling] 0.02 0.56 2431 2536
#> d[Self-help vs. Group counselling] -0.30 0.33 2629 2708
#> d[Self-help vs. Individual counselling] -0.10 0.44 2025 2188
#> 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] -0.48 0.41 -1.32 -0.74 -0.48 -0.22 0.30 1483
#> d[Group counselling] 0.62 0.49 -0.33 0.30 0.62 0.94 1.66 2629
#> d[Individual counselling] 0.37 0.42 -0.44 0.10 0.36 0.64 1.19 2025
#> Tail_ESS Rhat
#> d[No intervention] 1676 1
#> d[Group counselling] 2708 1
#> d[Individual counselling] 2188 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] 3.36 1.71 1.29 2.28 3.01 4.02 7.79 1541 2173
#> d[Individual counselling] 2.42 0.62 1.48 1.98 2.32 2.74 3.91 985 1773
#> d[Self-help] 1.77 0.80 0.74 1.25 1.62 2.10 3.74 1483 1676
#> 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 1.48 1.60 1.66 1.72 1.83 4488 3204 1
#> d[FIXTURE: IXE_Q2W] 3.03 0.10 2.84 2.96 3.03 3.09 3.22 5216 3145 1
#> d[FIXTURE: IXE_Q4W] 2.61 0.10 2.42 2.55 2.61 2.68 2.81 4937 3107 1
#> d[FIXTURE: SEC_150] 2.22 0.12 1.98 2.14 2.22 2.30 2.45 4933 3080 1
#> d[FIXTURE: SEC_300] 2.52 0.13 2.27 2.43 2.52 2.60 2.78 5430 3388 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 0.08 1.34 1.45 1.51 1.56 1.68 4102 3107
#> d[UNCOVER-1: IXE_Q2W] 2.92 0.09 2.76 2.86 2.92 2.98 3.09 4629 2964
#> d[UNCOVER-1: IXE_Q4W] 2.51 0.08 2.35 2.45 2.51 2.57 2.68 4558 2663
#> d[UNCOVER-1: SEC_150] 2.11 0.12 1.87 2.03 2.11 2.20 2.36 4469 3161
#> d[UNCOVER-1: SEC_300] 2.42 0.13 2.18 2.33 2.41 2.50 2.67 5343 3122
#> 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 1.36 1.45 1.51 1.56 1.67 4131 3271
#> d[UNCOVER-2: IXE_Q2W] 2.92 0.08 2.76 2.86 2.92 2.98 3.09 4849 3328
#> d[UNCOVER-2: IXE_Q4W] 2.51 0.08 2.35 2.45 2.51 2.56 2.67 4642 3130
#> d[UNCOVER-2: SEC_150] 2.11 0.12 1.88 2.03 2.11 2.19 2.35 4760 3086
#> d[UNCOVER-2: SEC_300] 2.42 0.13 2.18 2.33 2.41 2.50 2.67 5515 3526
#> 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 1.38 1.47 1.53 1.58 1.68 4256 3103
#> d[UNCOVER-3: IXE_Q2W] 2.94 0.09 2.78 2.88 2.94 3.00 3.11 5091 3214
#> d[UNCOVER-3: IXE_Q4W] 2.53 0.08 2.36 2.47 2.53 2.58 2.69 4771 3171
#> d[UNCOVER-3: SEC_150] 2.13 0.12 1.90 2.05 2.13 2.21 2.37 5082 3259
#> d[UNCOVER-3: SEC_300] 2.43 0.12 2.20 2.35 2.43 2.52 2.69 5726 3397
#> 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 1.10 1.25 1.40 1.70 6243 3243 1
#> d[New 1: IXE_Q2W] 2.89 0.22 2.48 2.74 2.88 3.03 3.34 7743 3196 1
#> d[New 1: IXE_Q4W] 2.47 0.21 2.06 2.33 2.47 2.61 2.90 7607 2476 1
#> d[New 1: SEC_150] 2.08 0.22 1.66 1.93 2.07 2.22 2.52 7724 3220 1
#> d[New 1: SEC_300] 2.38 0.22 1.95 2.23 2.38 2.53 2.82 8040 3291 1
#>
# }