Models for missing data are necessarily based on untestable assumptions whose effect on the conclusions are usually assessed via sensitivity analysis. To avoid the usual normality assumption and/or hard-to-interpret sensitivity parameters proposed by many authors for such purposes, we consider a simple approach for estimating means, standard deviations and correlations. We do not make distributional assumptions and adopt a pattern-mixture model parameterization which has easily interpreted sensitivity parameters. We use the so-called estimated ignorance and uncertainty intervals to summarize the results and illustrate the proposal with a practical example. We present results for both the univariate and the multivariate cases.