Bias dynamics for parameter estimation with missing data mechanisms under logistic model

Abstract

Missing data affects the validity of statistical conclusions through biasing estimated parameters, increasing standard errors and reducing statistical power. We evaluated the effect of missingness, sample size and missing data mechanisms on bias of the estimated parameters. Our findings, based on survey and simulated data indicate that higher proportions of missing data and smaller sample size considerably increase bias in estimated parameters. Further, bias was higher for the missing at random than missing completely at random mechanism. Moreover, whereas multiple imputation renders a viable solution to the missing data problem, imputation with more than 35% of missing data may generate unreliable model estimates.