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最理想的遗失资料(Missing data)敏感度分析软件，是根据FDA及ICH的条文，可以选择四种方法。
SOLAS是一款缺失数研究的软件，全球统计学家和数据分析师选择用它来处理不完整数据或╱缺失值。
SOLAS被政府机构和主要出版、研究和生命科学组织在全球范围内使用。这些组织信任SOLAS解决他们关键缺失数据问题和提高数★据的质量和后续分析。
SOLAS提供原则□ 性,前缘与缺失值的方法来分析数据,和特性选择9不同数Ψ 据归责技术允许您做：
容易遵守FDA灵敏度分析的要◎求
选择最好的、最合适的技术为您的特定数据▃结构架构
仔细你↓的丢失的数据协议,以确保最好的结构,大多数科学计算准确的罪名来实现你的分析目标和业务目标。
新的转折点情节功能允许您可视化缺失数据的ζ 敏感性分析
SOLAS™ is developed in close collaboration with Prof. Donald B. Rubin, the leading authority on Multiple Imputation.
SOLAS™ 3.0 for Missing Data Analysis offers principled approaches to missing data now has its own scripting language and features a choice of 6 imputation techniques, including 2 Multiple Imputation techniques based on the work of Prof. Donald B. Rubin. Data can be imported from a wide variety of file types including SAS (Unix/Windows), SPSS, Splus, Stata and many more. Once the data is imported, the missing data pattern can be displayed and a decision upon the most appropriate technique made. Once imputation is complete the imputed datasets can be analysed within SOLAS or exported to a variety of other packages in the correct format. It’s that simple!
"Solas is currently the only program that implements multiple imputation noniteratively and with substantial flexibility, even including ad-hoc methods, such as LOCF, as points of comparison for sensitivity analysis."
Prof. Donald B. Rubin, Harvard.
The incorrect analysis of datasets with incomplete data can lead to biased analysis and incorrect inference. SOLAS™ 3.0 provides researchers with a range of imputation approaches in an easy to use, validated software package that includes principled, informed solutions to the problems presented by incomplete datasets.
Why should I use SOLAS™ 3.0?
Choice of six imputation techniques, including 2 Multiple Imputation Techniques
The only software you will need to perform Missing Data Sensitivity Analysis as required by regulatory guidelines
The only commercially available and supported software that offers Multiple Imputation
Script Language to facilitate easy running of simulations
Complete control over the Donor Pool selections
Can be applied to longitudinal and single observation datasets
Easy to use, Windows-based, validated software package
What is Multiple Imputation?
The issue of Missing Data is the subject of increasing debate in contemporary statistics. In any given study, missing data can have many causes. For instance, respondents may be unwilling to answer some questions (item non-response) or refuse to participate in a study (unit non-responses). In addition, transcription errors and dropouts in follow up studies and clinical trials can frequently occur.
The incorrect analysis of datasets with incomplete data can lead to biased analysis and incorrect inferences. SOLAS™ provides researchers with a range of single and multiple imputation approaches so that the user can apply the most appropriate approach to their problem. When some data are missing, standard variable by variable analysis may be based on divergent sets of cases, and standard multivariate methods are designed only for the analysis of complete cases. The real problem with single imputation is that the single value being imputed, cannot itself reflect the uncertainty about the actual value. Therefore analyses that treat imputed values like observed values will systematically underestimate this uncertainty, leading to standard errors that are too small, p-values that are systematically too significant and confidence intervals which systematically cover less than their nominal coverages.
Enter Multiple Imputation - First proposed by Rubin in the 1970’s, the method imputes several values (M) for each missing value, to represent the uncertainty about which values to impute. Analytical incorporation of the uncertainty due to missing data is generally very complicated. Multiple Imputation is a technique to perform this incorporation of the uncertainty about missing data, making use of available software advances in this area.
With Multiple Imputation, the first set of (M) imputed values is used to form the first completed dataset and so on. The M versions of completed datasets are analyzed by standard complete data methods and the results are combined using simple rules ( this is automatic in SOLAS™, further details are available on the main SOLAS™ page) to yield single combined estimates, standard errors, p-values, that formally incorporate missing data uncertainty. The pooling of the results of the analyses performed on the multiply imputed datasets, implies that the resulting point estimates are averaged over the M completed sample points, and the resulting standard errors and p-values are adjusted according to the variance of the corresponding M completed sample point estimates. This variance called the ’between imputation variance’, provides a measure of the extra inferential uncertainty due to missing data.
Note: Multiple Imputation has been proven in independent research to be able to correct for the systematic inferential failings produced by ignoring missing data and the ad-hoc approaches of single imputation.
With Multiple Imputation, when the statistical model adequately describes the data and the imputations are generated from the predictive distribution of the missing data, given the observed data, the difference between M imputed values for each missing data entry will properly reflect the extra uncertainty due to the missing data.
Major Advantages of Multiple Imputation:
Better statistical validity than ad-hoc approaches
Multiple Imputation is statistically efficient in that it uses the entire observed dataset in the statistical analysis, efficiency being the degree to which all information about the parameter of interest, available in the dataset, is used.
Multiple Imputation saves money, since for the same statistical power, multiple imputation requires a smaller sample size than listwise deletion
Once imputations have been generated by a knowledgeable user, researchers can use them for their own statistical analyses
Imputation Techniques and Post Imputation Analysis in SOLAS
SOLAS™ 3.0 provides the user with a choice of 6 imputation techniques, two of which are Multiple Imputation techniques.
