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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_nma object created by nma()

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 newdata which 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)? Default FALSE.

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
#> 
# }