Pattern-mixture model with Taylor series for a binary outcome
Source:R/Taylor.IMOR_function.R
taylor_imor.RdApplies the pattern-mixture model under a specific assumption about the informative missingness odds ratio in trial-arms with binary missing participant outcome data and uses the Taylor series to obtain the odds ratio (in the logarithmic scale) and standard error for each trial (White et al., 2008).
Format
The columns of the data-frame in the argument data refer to
the following ordered elements for a binary outcome:
| id | A unique identifier for each trial. |
| r1 | The observed number of events in the first arm of the comparison. |
| r2 | The observed number of events 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 of participants randomised in the first arm of the comparison. |
| n2 | The number of participants 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.
- mean_value
A numeric value for the mean of the normal distribution of the informative missingness odds ratio in the logarithmic scale. 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.
- var_value
A positive non-zero number for the variance of the normal distribution of the informative missingness odds ratio in the logarithmic scale. The default argument is 1.
- rho
A numeric value in the interval [-1, 1] that indicates the correlation coefficient between two 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 odds ratio in the logarithmic scale (log OR) adjusted for missing participants and obtained using the Taylor series.
- se.EM
The standard error of the log OR adjusted for missing participants and obtained using the Taylor series.
Details
The taylor_imor function is integrated in the
unrelated_effects_plot function.
References
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