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 0.44 0.28 0.81 1.10 1.39 1.99 2276
#> d[Individual counselling] 0.85 0.24 0.38 0.69 0.84 1.00 1.34 1454
#> d[Self-help] 0.48 0.40 -0.29 0.23 0.48 0.74 1.24 2017
#> Tail_ESS Rhat
#> d[Group counselling] 2783 1
#> d[Individual counselling] 2018 1
#> d[Self-help] 2344 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 0.44 0.28 0.81 1.10
#> d[Individual counselling vs. No intervention] 0.85 0.24 0.38 0.69 0.84
#> d[Self-help vs. No intervention] 0.48 0.40 -0.29 0.23 0.48
#> d[Individual counselling vs. Group counselling] -0.26 0.42 -1.09 -0.53 -0.26
#> d[Self-help vs. Group counselling] -0.62 0.49 -1.62 -0.93 -0.62
#> d[Self-help vs. Individual counselling] -0.37 0.41 -1.21 -0.62 -0.36
#> 75% 97.5% Bulk_ESS Tail_ESS
#> d[Group counselling vs. No intervention] 1.39 1.99 2276 2783
#> d[Individual counselling vs. No intervention] 1.00 1.34 1454 2018
#> d[Self-help vs. No intervention] 0.74 1.24 2017 2344
#> d[Individual counselling vs. Group counselling] 0.02 0.54 2804 2901
#> d[Self-help vs. Group counselling] -0.30 0.33 2901 2223
#> d[Self-help vs. Individual counselling] -0.10 0.42 2369 2658
#> 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 0.40 -1.24 -0.74 -0.48 -0.23 0.29 2017
#> d[Group counselling] 0.62 0.49 -0.33 0.30 0.62 0.93 1.62 2901
#> d[Individual counselling] 0.37 0.41 -0.42 0.10 0.36 0.62 1.21 2369
#> Tail_ESS Rhat
#> d[No intervention] 2344 1
#> d[Group counselling] 2223 1
#> d[Individual counselling] 2658 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] 3.33 1.60 1.32 2.25 3.00 4.00 7.33 2276 2783
#> d[Individual counselling] 2.40 0.60 1.46 1.99 2.33 2.73 3.80 1454 2018
#> d[Self-help] 1.75 0.73 0.75 1.25 1.61 2.10 3.46 2017 2344
#> 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 4126 3465 1
#> d[FIXTURE: IXE_Q2W] 3.03 0.10 2.84 2.96 3.03 3.10 3.22 4743 3161 1
#> d[FIXTURE: IXE_Q4W] 2.62 0.09 2.44 2.55 2.62 2.68 2.81 4789 3156 1
#> d[FIXTURE: SEC_150] 2.22 0.12 1.99 2.14 2.22 2.30 2.45 4515 3651 1
#> d[FIXTURE: SEC_300] 2.52 0.12 2.29 2.44 2.52 2.61 2.76 5273 3387 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 1.68 4385 3251
#> d[UNCOVER-1: IXE_Q2W] 2.93 0.08 2.76 2.87 2.93 2.98 3.10 4976 3293
#> d[UNCOVER-1: IXE_Q4W] 2.51 0.08 2.36 2.46 2.51 2.57 2.68 4906 3423
#> d[UNCOVER-1: SEC_150] 2.11 0.12 1.89 2.03 2.11 2.20 2.35 5221 3546
#> d[UNCOVER-1: SEC_300] 2.42 0.12 2.18 2.34 2.42 2.50 2.66 6081 3687
#> 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 1.35 1.45 1.51 1.56 1.67 4393 3154
#> d[UNCOVER-2: IXE_Q2W] 2.92 0.08 2.77 2.87 2.92 2.98 3.09 5093 3454
#> d[UNCOVER-2: IXE_Q4W] 2.51 0.08 2.36 2.46 2.51 2.56 2.67 5009 3328
#> d[UNCOVER-2: SEC_150] 2.11 0.12 1.89 2.03 2.12 2.19 2.35 5263 3449
#> d[UNCOVER-2: SEC_300] 2.42 0.12 2.18 2.34 2.42 2.50 2.65 6168 3723
#> 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 4312 3328
#> d[UNCOVER-3: IXE_Q2W] 2.94 0.08 2.78 2.89 2.94 3.00 3.11 5021 3515
#> d[UNCOVER-3: IXE_Q4W] 2.53 0.08 2.37 2.48 2.53 2.58 2.69 5014 3420
#> d[UNCOVER-3: SEC_150] 2.13 0.11 1.91 2.05 2.13 2.21 2.36 5137 3554
#> d[UNCOVER-3: SEC_300] 2.44 0.12 2.21 2.36 2.43 2.51 2.67 6070 3595
#> 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 0.22 0.81 1.10 1.25 1.40 1.71 7016 2903 1
#> d[New 1: IXE_Q2W] 2.89 0.22 2.47 2.74 2.89 3.04 3.33 7537 3302 1
#> d[New 1: IXE_Q4W] 2.48 0.22 2.05 2.33 2.47 2.62 2.90 7736 3098 1
#> d[New 1: SEC_150] 2.08 0.23 1.64 1.92 2.08 2.23 2.53 7796 2943 1
#> d[New 1: SEC_300] 2.38 0.23 1.94 2.23 2.38 2.53 2.84 7727 2935 1
#>
# }