Intention-to-treat analysis with treatment discontinuation and missing data in clinical trials

To assure readers that study results are scientifically valid, the methods of a clinical trial should be described adequately. Since randomization, blinding, and intention-to-treat-analysis are major bias-reducing techniques, these aspects should be reported most accurately. The Consolidated standards of reporting trials (CONSORT) are recommendations to improve the reporting of trials. CONSORT requires that trial authors describe basic methodological aspects that readers need to appraise the strengths of report ed clinical trials. This article presents the CONSORT recommendations and explains some of their main aspects. From now on, the Journal of Hand Surgery will use CONSORT to assist authors of randomized controlled trials in improving the description of their studies. We believe that this decision increases the scientific validity of study reports and helps readers when critically appraising articles.

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Clinical trials—meta-analysis, confidence limits and ‘intention to treat” analysis

Journal of Obstetrics and Gynaecology ◽

10.3109/01443619009151189 ◽

1990 ◽

Vol 10 (4) ◽

pp. 259-260 ◽

Cited By ~ 4

Author(s):

Denis F. Hawkins

Keyword(s):

Clinical Trials ◽

Meta Analysis ◽

Confidence Limits ◽

Intention To Treat ◽

Treat Analysis

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Intention-to-treat analysis and the goals of clinical trials*

Clinical Pharmacology & Therapeutics ◽

10.1016/0009-9236(95)90260-0 ◽

1995 ◽

Vol 57 (1) ◽

pp. 6-15 ◽

Cited By ~ 206

Author(s):

Lewis B. Sheiner ◽

Donald B. Rubin

Keyword(s):

Clinical Trials ◽

Intention To Treat ◽

Treat Analysis

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A novel complete-case analysis to determine statistical significance between treatments in an intention-to-treat population of randomized clinical trials involving missing data

Statistical Methods in Medical Research ◽

10.1177/0962280216651307 ◽

2016 ◽

Vol 27 (4) ◽

pp. 1067-1075 ◽

Cited By ~ 1

Author(s):

Wei Liu ◽

Jinhui Ding

Keyword(s):

Clinical Trials ◽

Missing Data ◽

Randomized Clinical Trials ◽

Statistical Significance ◽

Outcome Data ◽

Complete Case ◽

Significant Treatment ◽

Significance Level ◽

Intention To Treat ◽

The Impact

The application of the principle of the intention-to-treat (ITT) to the analysis of clinical trials is challenged in the presence of missing outcome data. The consequences of stopping an assigned treatment in a withdrawn subject are unknown. It is difficult to make a single assumption about missing mechanisms for all clinical trials because there are complicated reactions in the human body to drugs due to the presence of complex biological networks, leading to data missing randomly or non-randomly. Currently there is no statistical method that can tell whether a difference between two treatments in the ITT population of a randomized clinical trial with missing data is significant at a pre-specified level. Making no assumptions about the missing mechanisms, we propose a generalized complete-case (GCC) analysis based on the data of completers. An evaluation of the impact of missing data on the ITT analysis reveals that a statistically significant GCC result implies a significant treatment effect in the ITT population at a pre-specified significance level unless, relative to the comparator, the test drug is poisonous to the non-completers as documented in their medical records. Applications of the GCC analysis are illustrated using literature data, and its properties and limits are discussed.

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