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Comparative Study
. 2002 Oct 1;68(2):121-30.
doi: 10.1016/s0376-8716(02)00111-4.

Intention-to-treat meets missing data: implications of alternate strategies for analyzing clinical trials data

Affiliations
Comparative Study

Intention-to-treat meets missing data: implications of alternate strategies for analyzing clinical trials data

Charla Nich et al. Drug Alcohol Depend. .

Abstract

True intention-to-treat analyses are rare in reports of randomized clinical trials. To highlight the complex issues that arise in conducting and interpreting data from intention-to-treat analyses in studies with substantial levels of protocol violation (e.g. attrition, noncompliance, or withdrawal of participants), data from a clinical trial of treatment for cocaine dependence were analyzed using three strategies to manage missing data: Strategy 1 addressed the effectiveness of treatments based on data collected from participants up to the point of dropout. Strategy 2 addressed the effectiveness of treatments based on data from the full intended duration of the protocol including data collected after participant dropout. The third strategy used a more novel approach, which used an intention-to-treat strategy for the full duration of the trial and the full sample, but also evaluated the effect of treatment retention outcomes by including an independent variable to reflect active treatment retention as a time-varying covariate. Conclusions about the relative efficacy of the study treatments varied to some extent depending on the analytic strategy used. These findings suggest that investigators should make every effort to conduct intent-to-treat analyses, but also to make use of multiple analytic strategies to fully understand the effects of the treatments studied. Moreover, regardless of the strategy used, investigators should clearly describe their handling of data from participants who violate the protocol.

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Figures

Fig. 1
Fig. 1
Individual data points used in three statistical models. (a) Strategy 1. Standard analysis using random effect regression model on within-treatment data only. (b) Strategy 2. Random effect regression model on all data for the full intended duration of the protocol, including data points collected after treatment termination. (c) Strategy 3. Random effect regression model on all data for full intended duration including both data points collected after treatment termination and a covariate for whether the outcome was collected during treatment. Actual data point (●); Actual data point gathered after treatment termination (○).
Fig. 2
Fig. 2
Comparison of model estimates from data collected within-treatment (Strategy 1, n=122/903) to mean observed values from data collected both within-treatment and after treatment termination (Strategy 2, n=122/1308).

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