Management and interpretation of data obtained from clinical trials in pain management.

Conducting a clinical trial involves various stages of planning and implementation. The three major components involved in clinical trials are the management of data, the quality control to ensure data integrity, and the interpretation of the data at the conclusion of the trial. Although each proces...

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Publicado en:Pain Practice Vol. 8; no. 6; pp. 461 - 473
Autores principales: Theodore BR, Gatchel RJ
Formato: equations & formulas Journal Article
Publicado: Wiley-Blackwell Nov/Dec2008
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Management and interpretation of data obtained from clinical trials in pain management.
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        au:
          Theodore BR
          Gatchel RJ
        affil: Department of Psychology, College of Science, The University of Texas at Arlington, 501 S. Nedderman Drive, Arlington, TX 76019-0528, USA
      sug:
        subj:
          Clinical Trials Methods
          Data Analysis Methods
          Data Management Methods
          Pain Therapy
          Statistics Methods
          Bias (Research)
          Causal Attribution
          Effect Size
          Odds Ratio
          Pearson's Correlation Coefficient
          Quality Assurance
          Statistical Significance
          Treatment Outcomes Evaluation
      ab: Conducting a clinical trial involves various stages of planning and implementation. The three major components involved in clinical trials are the management of data, the quality control to ensure data integrity, and the interpretation of the data at the conclusion of the trial. Although each process is distinct and involves different levels of effort and knowledge to implement, all processes are intimately linked. Data management techniques include the process of data entry and the implementation of an organized, comprehensive approach to quality control. Some guidelines for quality control screening are recommended to address various common issues related to clinical data, such as missing data, invalid cases, subject 'outliers,' and violation of distributional assumptions relevant to statistical analyses. In order to aid in interpreting the data, conditions that need to be met to make causal inferences are discussed. Taking into account baseline characteristics of the patient sample is also discussed as an extension to maintaining the internal validity of the study. Additionally, some common threats to statistical conclusion validity, including Type I error inflation and the problem of overpowered tests, are highlighted. Finally, the concept of the effect size as an important complement to statistical significance and how the various types of effect size measures can be interpreted within the context of a clinical trial are discussed.
      pubtype: Academic Journal
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        equations & formulas
        Journal Article
      ougenre: Article
    language: English
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