missingness_param_prior {rnmamod}

Define the mean value of the normal distribution of the missingness parameter

Description

Generates 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).

Usage

missingness_param_prior(assumption, mean_misspar)

Arguments

assumption

Character string indicating the structure of the informative missingness parameter. Set assumption equal 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".

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.

Value

A scalar or numeric vector to be passed to run_model.

Author(s)

Loukia M. Spineli

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

See Also

run_model