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Table 4 Outline of the imputation and missingness models

From: Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting

Model

Response

Predictors

Imputation Model

\( Y=\Big\{{\displaystyle \begin{array}{l}1\kern1.25em if\ recrudescence\\ {}0\kern1.25em if\ new\ infection\end{array}}\operatorname{} \)

• age (years)

• mg/kg dose of partner drug

• transmission intensity a

• treatment regimen

• time of recurrence

• parasitaemia (log)

• study sites b

• parasite density (log) on the day of recurrence

Missingness model

\( {Y}_{obs}=\Big\{{\displaystyle \begin{array}{l}1\kern1.25em if\ outcome\ is\ observed\\ {}0\kern1em if\ outcome\ is\ indeterminate\end{array}}\operatorname{} \)

• age (years)

• mg/kg dose of partner drug

• transmission intensity a

• treatment regimen

• time of recurrence

• recurrence status (yes/no) c

  1. a Transmission settings were categorised as low if Malaria Atlas Project estimate were less than or equal to 0.10, moderate if >0.10 and ≤ 0.40, and high if greater than 0.40
  2. b Study site was not added in the missingness model as it led to convergence issues
  3. c Excluded in the IPW-E approach