Pattern-mixture model with Taylor series for continuous outcome

Applies the pattern-mixture model under a specific assumption about the informative missingness parameter in trial-arms with continuous missing participant outcome data and uses the Taylor series to obtain the effect size and standard error for each trial (Mavridis et al., 2015).

Usage

taylor_continuous(data, measure, mean_value, var_value, rho)

Format

The columns of the data-frame in the argument data refer to the following ordered elements for a continuous outcome:

id	A unique identifier for each trial.
y1	The observed mean outcome in the first arm of the comparison.
y2	The observed mean outcome in the second arm of the comparison.
sd1	The observed standard deviation of the outcome in the first arm of the comparison.
sd2	The observed standard deviation of the outcome in the second arm of the comparison.
m1	The number of missing participants in the first arm of the comparison.
m2	The number of missing participants in the second arm of the comparison.
n1	The number randomised in the first arm of the comparison.
n2	The number randomised in the second arm of the comparison.
t1	An identifier for the intervention in the first arm of the comparison.
t2	An identifier for the intervention in the second arm of the comparison.

Arguments

data

A data-frame in the long arm-based format. Two-arm trials occupy one row in the data-frame. Multi-arm trials occupy as many rows as the number of possible comparisons among the interventions. See 'Format' for the specification of the columns.

measure

Character string indicating the effect measure with values "MD", "SMD", or "ROM" for the mean difference, standardised mean difference, and ratio of means, respectively.

mean_value

A numeric value for the mean of the normal distribution of the informative missingness parameter. The same value is considered for all trial-arms of the dataset. The default argument is 0 and corresponds to the missing-at-random assumption. For the informative missingness ratio of means, the mean value is defined in the logarithmic scale.

var_value

A positive non-zero number for the variance of the normal distribution of the informative missingness parameter. When the measure is "MD", or "SMD" the default argument is 1; when the measure is "ROM" the default argument is 0.04. The same value is considered for all trial-arms of the dataset.

rho

A numeric value in the interval [-1, 1] that indicates the correlation coefficient between two informative missingness parameters in a trial. The same value is considered across all trials of the dataset. The default argument is 0 and corresponds to uncorrelated missingness parameters.

Value

A data-frame that additionally includes the following elements:

EM

The effect size adjusted for the missing participants and obtained using the Taylor series.

se.EM

The standard error of the effect size adjusted for the missing participants and obtained using the Taylor series.

Details

The taylor_continuous function is integrated in the unrelated_effects_plot function.

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

See also

run_model, unrelated_effects_plot

Author

Loukia M. Spineli