Participation bias assessment in three high-impact journals

Studies into participation bias have examined participation trends, where it occurs, the factors affecting it, and methods to try to reduce it. However, some authors only discuss participation bias at the end of the study, some acknowledge it and apply a method to try to reduce it, while others ignore it or dismiss it as negligible. Issues of three high-impact epidemiology journals were examined; 81 articles were read and reviewed for potential participation bias. Categories were used to classify the approach taken to participation bias and the results recorded. Of the 81 articles considered, 42 (51%) were eligible and could have suffered from participation bias. It was found that 57% of these articles ignored the effects of participation bias, while 17% only considered it briefly in the discussion. Few articles (22%) attempted to reduce the participation bias, with over half of these using unsuitable methods (55%). This review highlights how participation bias is often not considered and hence the conclusions drawn from these studies may not be correct

Adjusted jackknife for imputation under unequal probability sampling without replacement

Imputation is commonly used to compensate for item non-response in sample surveys. If we treat the imputed values as if they are true values, and then compute the variance estimates by using standard methods, such as the jackknife, we can seriously underestimate the true variances. We propose a modified jackknife variance estimator which is defined for any without-replacement unequal probability sampling design in the presence of imputation and non-negligible sampling fraction. Mean, ratio and random-imputation methods will be considered. The practical advantage of the method proposed is its breadth of applicabilit

Calibrated imputation in surveys under a quasi-model-assisted approach

We propose to use calibrated imputation to compensate for missing values. This technique consists of finding final imputed values that are as close as possible to preliminary imputed values and are calibrated to satisfy constraints. Preliminary imputed values, potentially justified by an imputation model, are obtained through deterministic single imputation. Using appropriate constraints, the resulting imputed estimator is asymptotically unbiased for estimation of linear population parameters such as domain totals. A quasi-model-assisted approach is considered in the sense that inferences do not depend on the validity of an imputation model and are made with respect to the sampling design and a non-response model. An imputation model may still be used to generate imputed values and thus to improve the efficiency of the imputed estimator. This approach has the characteristic of handling naturally the situation where more than one imputation method is used owing to missing values in the variables that are used to obtain imputed values. We use the Taylor linearization technique to obtain a variance estimator under a general non-response model. For the logistic non-response model, we show that ignoring the effect of estimating the non-response model parameters leads to overestimating the variance of the imputed estimator. In practice, the overestimation is expected to be moderate or even negligible, as shown in a simulation study. Copyright 2005 Royal Statistical Society.