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Crit Care Resusc. 2009 Mar;11(1):46-57.

The NICE-SUGAR (Normoglycaemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation) Study: statistical analysis plan.

Author information

1
The George Institute for International Health, University of Sydney, Sydney, NSW. sfinfer@george.org.au.

Abstract

BACKGROUND:

The Normoglycaemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation (NICE-SUGAR) Study is the largest study to date of glycaemic control in critically ill patients.

OBJECTIVE:

To describe in detail and make public the study's pre-determined statistical analysis plan, which was finalised while data collection was still ongoing, and to which the investigators will adhere in analysing the data from the trial.

METHODS:

The data collected by researchers as part of the trial protocol were reviewed and formally assessed. Information relevant to baseline characteristics was selected and, for each item, statistically relevant descriptive elements were described. Information relevant to the process of care and delivery of prescribed trial therapy was similarly classified and, for each item, appropriate descriptive statistical analysis was planned with appropriate comparison between groups. Finally, trial outcomes were classified as primary, secondary or tertiary, and an appropriate statistical comparison between groups was planned and described.

RESULTS:

A standard analysis plan was developed for the results of the NICE-SUGAR Study. This plan allows a comprehensive description of baseline characteristics, features of the process of care, and trial treatment delivery, along with pre-determined statistical assessment of relevant outcome measures in a way that is transparent, available to the public, verifiable and pre-determined before completion of data collection.

CONCLUSION:

We have developed a pre-determined statistical analysis plan for the NICE-SUGAR Study. This plan will be followed to avoid analysis bias arising from prior knowledge of the study findings.

Comment in

PMID:
19281445
[Indexed for MEDLINE]

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