Abstract
This chapter deals with a very important topic that needs serious attention from the proponents of Randomized Controlled Trials (RCTs) on invasive and noninvasive surgical procedures. For RCTs, missing data is inevitable irrespective of disease areas and not accounting for missing data mechanisms in the analysis can pose serious concerns about the validity of the trial results. This chapter provides a brief background on missing data which includes common notations, missing data patterns, and missing data mechanisms. The impacts of missing data on the trial findings in absence of a strategy are discussed. Different strategies that can be used during the conduct of the RCT and also during the data analysis are introduced. Variety of analytical methods to deal with missing data with different missing mechanisms are introduced and discussed. The importance of including a sensitivity analysis is also pointed out in this chapter.
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References
National Research Council of the National Academies. The prevention and treatment of missing data in clinical trials. Washington DC: National Academies Press; 2010.
Schafer JL. Analysis of incomplete multivariate data. New York: Chapman & Hall; 1997.
Rubin DB. Inference and missing data. Biometrika. 1976;63:581–92.
Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. Wiley: New York; 2002.
European Medicines Agency. Guideline on missing data in confirmatory clinical trials. 2009. EMA/CPMP/EWP/1776/99 rev. 1, Committee for Medicinal Products for Human Use (July). Available from http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/09/WC500096793.pdf.
Biswas K. Prevention and management of missing data during conduct of a clinical study. Biostatistics Psychiatry. 2012;24(4):235–7.
Rubin DB. Multiple imputation for non-response in surveys. New York: Wiley; 1987.
Pigott TD. A review of Methods for Missing Data. Educ Res Eval. 2001;7(4):353–83.
Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147–77.
Ratitch B, O’Kelly M. Implementation of pattern-mixture models using standard SAS/STAT procedures. 2011. PharmaSug2011—Paper SP04. Available: http://pharmasug.org/proceedings/2011/SP/PharmaSUG-2011-SP04.pdf.
Little RJA. Modeling the dropout mechanism in repeated-measures studies. J Am Stat Assoc. 1995;90:1112–21.
Verbeke G, Molenbergs G. Linear mixed models for longitudinal data. New York: Springer; 2000.
SAS/STAT(R) 13.1 User’s guide, the MIANALYZE procedure. SAS Institute; Available from http://support.sas.com/documentation/cdl/en/statug/66859/HTML/default/viewer.htm#statug_mianalyze_examples13.htm.
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Biswas, K. (2017). Missing Data. In: Itani, K., Reda, D. (eds) Clinical Trials Design in Operative and Non Operative Invasive Procedures. Springer, Cham. https://doi.org/10.1007/978-3-319-53877-8_19
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DOI: https://doi.org/10.1007/978-3-319-53877-8_19
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