Open AccessReported methods for handling missing change standard deviations in meta-analyses of exercise therapy interventions in patients with heart failure: A systematic review
Author(s) -
Melissa J. Pearson,
Neil A. Smart
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0205952
Subject(s) - meta analysis , missing data , medicine , heart failure , systematic review , medline , cochrane library , psychological intervention , imputation (statistics) , physical therapy , statistics , mathematics , psychiatry , political science , law
Background Well-constructed systematic reviews and meta-analyses are key tools in evidenced-based healthcare. However, a common problem with performing a meta-analysis is missing data, such as standard deviations (SD). An increasing number of methods are utilised to calculate or impute missing SDs, allowing these studies to be included in analyses. The aim of this review was to investigate the methods reported and utilised for handling missing change SDs in meta-analyses, using the topic of exercise therapy in heart failure patients as a model. Methods A systematic search of PubMed, EMBASE and Cochrane Library from 1 January 2014 to 31st March 2018 was conducted for meta-analyses of exercise based trials in heart failure. Studies were eligible to be included if they performed a meta-analysis of change in exercise capacity in heart failure patients after a training intervention. Results Twenty two publications performed a meta-analysis on the effect of exercise therapy on exercise capacity in heart failure patients. Eleven (50%) publications did not directly report the approach for dealing with missing change SDs. Approaches reported and utilised to deal with missing change SDs included imputation, actual and approximate algebraic recalculation using study level summary statistics and exclusion of studies. Conclusion Change SDs are often not reported in trial papers and while in the first instance meta-analysts should attempt to obtain missing data from trial authors, this information is frequently not forthcoming. Meta-analysts are then forced to make a decision on how these trials and missing data are to be handled. Whilst not one approach is favoured for dealing with this matter, authors need to clearly report the approach to be utilised for missing change SDs. Where change SDs are imputed meta-analyst are encouraged to explore several options and have a sound rationale as to the choice, and where data is imputed, sensitivity analysis should be conducted.