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Under the assumption that data are missing at random (MAR), we assume instead that the participants' missing outcomes are independent of the missing data indicator (see below), given the covariates and any observed outcomes.
To further explore if imputations were necessary or beneficial, dummy variables (ie, yes or no) were created as missing data indicator for each covariate with missing observations.
If required, for each remaining covariate, we will include a missing data indicator variable (at present <0.1% data are missing for each variable).
We will include a missing data indicator value for each covariate (at present, there is <1% missing for each variable).
For those variables with more than 5% missing values we included in the propensity score model the variable itself as well as a missing data indicator [ 25].
Following the seminal work of Rubin (1976, see also; Little and Rubin 2002; Schafer 1997; Enders 2010) let R be a missing data indicator, taking the value of 1 if the data are observed, and 0 if the data are missing.
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Let Y i denote the ith participant's vector of outcomes, so that participants may provide more than a single outcome, and let R i denote the corresponding vector of missing data indicators, where R i, j = 1 if Y i, j is observed, where Y i, j and R i, j are the jth entries of Y i and R i respectively.
Unfortunately, due to over 50% missing data for all indicators, none of the indicators in the poverty and inequality databas were seen in the selected indicators.
† = In the analysis of the final visit measure adjusting for a baseline that has occasional missing data, the missing indicator method is used.
It is unknown how missing data for severity indicators, or for outcome (n = 128; 4 %), may have influenced results.
A relatively large amount of missing data on the indicators of pubertal development.
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