Sentence examples for missing data such from inspiring English sources

Two patients were excluded afterwards because of missing data such as endpoint INR values and desired target INR values.

One challenge with working with motorcycle data are the inherit difficulties associated with missing data – such as motorcycle-specific: vehicle miles traveled (VMT) and average daily traffic (ADT).

Fourth, more sophisticated statistical methods anchored in the literature on missing data, such as the model-based approach to the correction of data not missing at random, could also be used to adjust biases stemming from the structure of online vacancy data.

Although we recognize that this may introduce a certain level of bias, it was not feasible, given the scope of the project (integrating the data from 5 separate longitudinal studies) to use accepted methods to deal with missing data, such as multiple imputation.

The first, based on a pre-defined (in the protocol) procedure further developed with the Data Monitoring and the Clinical Development Committees, complemented the WHO definitions (see below) with a set of rules allowing the evaluation of each individually randomised patient, e.g. patients having taken not-allowed anti-malarial drugs or with halfway missing data such as blood parasitaemia (Table 2).

Techniques for dealing with missing data, such as multiple imputation [ 18, 19], can be utilized for this specific dataset.

We have used four criteria from The Cochrane Collaboration's tool for assessing risk of bias 15: blinding of outcome assessment, complete outcome data such as missing data, selective reporting such as not reporting prespecified outcomes and other sources of bias such as bias related to the specific study design and competing interest.

Studies have shown that estimates are not sensitive to the choice of treatment of missing data at such minimal levels (Little & Rubin, 2002), and so we do not expect the listwise deletion of these small numbers of cases to introduce bias.

We first formulated the missing data paradigm such that it can be applied to an acoustic model that requires no compromises on accuracy and uses standard feature representations, i.e., a formulation that covers cepstral as well as LDA-features as they are commonly used in today's speech recognizers.

An alternative is to explore a range of values consistent with hypotheses concerning the missing data mechanism, such as those outlined above.

The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce.