Define the mean value of the normal distribution of the missingness parameter
Source:R/missingness.param.prior_function.R
missingness_param_prior.RdGenerates the mean value of the normal distribution of the missingness
parameter in the proper format depending on the assumed structure of the
missingness parameter. run_model inherits
missingness_param_prior through the argument mean_misspar
(see 'Argument' in run_model).
Arguments
- assumption
Character string indicating the structure of the informative missingness parameter. Set
assumptionequal to one of the following:"HIE-COMMON","HIE-TRIAL","HIE-ARM","IDE-COMMON","IDE-TRIAL","IDE-ARM","IND-CORR", or"IND-UNCORR". The default argument is"IDE-ARM". The abbreviations"IDE","HIE", and"IND"stand for identical, hierarchical and independent, respectively."CORR"and"UNCORR"stand for correlated and uncorrelated, respectively.- mean_misspar
A numeric value or a vector of two numeric values for the mean of the normal distribution of the informative missingness parameter (see 'Details'). The default argument is 0 and corresponds to the missing-at-random assumption for
assumption = "IDE-ARM".
Value
A scalar or numeric vector to be passed to run_model.
Details
run_model considers the informative missingness odds
ratio in the logarithmic scale for binary outcome data (Spineli, 2019a;
Turner et al., 2015; White et al., 2008), the informative missingness
difference of means when measure is "MD" or "SMD",
and the informative missingness ratio of means in the logarithmic scale
when measure is "ROM" (Spineli et al., 2021;
Mavridis et al., 2015).
When assumption is trial-specific (i.e., "IDE-TRIAL" or
"HIE-TRIAL"), or independent (i.e., "IND-CORR" or
"IND-UNCORR"), only one numeric value can be assigned to
mean_misspar because the same missingness scenario is applied to all
trials and trial-arms of the dataset, respectively. When assumption
is "IDE-ARM" or "HIE-ARM", a maximum of two
different or identical numeric values can be assigned as a vector to
mean_misspars: the first value refers to the experimental arm,
and the second value refers to the control arm of a trial.
In the case of a network, the first value is considered for all
non-reference interventions and the second value is considered for the
reference intervention of the network (see 'Argument' ref in
run_model). This is necessary to ensure transitivity in the
assumptions for the missingness parameter across the comparisons in the
network (Spineli, 2019b).
Currently, there are no empirically-based prior distributions for the
informative missingness parameters. The users may refer to
Mavridis et al. (2015) and Spineli (2019) to determine mean_misspar
for an informative missingness parameter.
References
Mavridis D, White IR, Higgins JP, Cipriani A, Salanti G. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis. Stat Med 2015;34(5):721–41. doi: 10.1002/sim.6365
Spineli LM, Kalyvas C, Papadimitropoulou K. Continuous(ly) missing outcome data in network meta-analysis: a one-stage pattern-mixture model approach. Stat Methods Med Res 2021;30(4):958–75. doi: 10.1177/0962280220983544
Spineli LM. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis. BMC Med Res Methodol 2019a;19(1):86. doi: 10.1186/s12874-019-0731-y
Spineli LM. Modeling missing binary outcome data while preserving transitivity assumption yielded more credible network meta-analysis results. J Clin Epidemiol 2019b;105:19–26. doi: 10.1016/j.jclinepi.2018.09.002
Turner NL, Dias S, Ades AE, Welton NJ. A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-analysis. Stat Med 2015;34(12):2062–80. doi: 10.1002/sim.6475
White IR, Higgins JP, Wood AM. Allowing for uncertainty due to missing data in meta-analysis–part 1: two-stage methods. Stat Med 2008;27(5):711–27. doi: 10.1002/sim.3008