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Data Analysis

Analysis of the data collected during a clinical trial should be carried out promptly and should follow a carefully written Statistical Analysis Plan (SAP). See WCTU SOP 21 'Statistical Analysis Plan' for more information. A template document has been developed to ensure consistency in preparing the trial SAP.

All outcome measures stated in the protocol should be fully analysed and the analysis should then be discussed by the trial management group to assist interpretation and discuss the implications of the findings.

Quantitative Data Analysis

  • Quantitative research techniques generate numerical output that need to be summarised, analysed and interpreted.
  • Characteristics of the data may be described and explored by drawing graphs and charts, doing cross tabulations and summarising using appropriate statistics.
  • Further analysis will build on these initial findings, seeking patterns and relationships in the data by exploring correlations, performing multiple regressions, or analyses of variance.
  • Advanced modelling techniques may eventually be used to build sophisticated explanations of how the data addresses the original question.
  • Although methods used can vary greatly, the following steps are common in quantitative data analysis:
    • Clinical data forms are designed appropriately to pick up the quantitative data in a manner such that it is easy to summarise and analyse
    • Data that is entered on the computer is often entered on the database so little manipulation has to be done to it for analysis purposes (Procedures such as code, reviewing missing data etc. are usually undertaken)
    • Data is validated by the statisitician prior to any analysis. Range checks, missing data and outliers are identified and checked with the data manager prior to analysis
    • Statistical analysis is carried out using statistical software packages such as; SAS, STATA or SPSS. More sophisticated analysis can be done in packages such as MLWin, BUGs etc.

Qualitative Data Analysis

  • Qualitative data analysis describes and summarises the mass of words generated by interviews or observational data.
  • It allows researchers to seek relationships between various themes that have been identified or relate behaviour or ideas to biographical characteristics of respondents.
  • Implications for policy or practice may be derived from the data, or interpretation sought of puzzling findings from previous studies.
  • Ultimately theory could be developed and tested using advanced analytical techniques.

Although methods of analysis can vary greatly (e.g., Interpretative phenomenological analysis, Grounded Theory, Thematic analysis, Discourse analysis)the following steps are typical for qualitative data analysis

    • Familiarisation with the data through repeated reading, listening etc.
    • Transcription of interview etc. material.
    • Organisation and indexing of data for easy retrieval and identification (e.g. by hand or computerized programmes such as Nvivo -formally NUD*IST)
    • Anonymising of sensitive data.
    • Coding (may be called indexing).
    • Identification of themes.
    • Development of provisional categories.
    • Exploration of relationships between categories.
    • Refinement of themes and categories.
    • Development of theory and incorporation of pre-existing knowledge.

Further reading:

  • Armitage P, Berry G & Matthews JNS (2001); Statistical Methods in medical research (4th Edition). Blackwell Publishing
  • Pocock SJ (1983); Clinical Trials: A Practical Approach. NewYork: John Wiley & Sons
  • Altmans D (1997); Practical Statistics for Medical Research. Chapman and Hall, London 
  • Pope C, Ziebland S, Mays N. Qualitative research in health care: Analysing qualitative data. BMJ 2000;320(7227):114-16.
  • Bryman A, Burgess R, editors. Analysing Qualitative Data. London: Routledge, 1994.