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Intent-to-treat analysis for longitudinal clinical trials: coping with the challenge of missing values.

Abstract：

:Drop-out is a common phenomenon in clinical trials of drug treatments involving longitudinal assessments for a fixed duration of follow-up. For these trials intent-to-treat (IT) analysis is usually preferred because time effects are seen in practice. The IT analysis mandates that all subjects randomized to a treatment arm should be included in the analysis. The purpose of the present paper is to acquaint both clinicians and statisticians with recent statistical methodological advances in handling drop-outs and their usage for IT analysis. We discuss a sensitivity analysis of 12-month outcome data to investigate the efficacy of drug therapy from a longitudinal double-blind placebo-controlled clinical trial in the maintenance therapy of geriatric major depressive illness. Outcome measures consist of monthly Hamilton depression scores. The sensitivity analysis includes endpoint analysis, last observation carried forward analysis, repeated measures models and imputation models. Imputation models are based on multiple imputations of missing responses deriving from an 'as-treated' model. The model used imputed doses from a plausible treatment scenario after drop-out and a 'propensity-adjusted' model where the imputations for the drop-outs were obtained from the adhering subjects with the same probability to remain on study (propensity) given the observed trajectory prior to withdrawal. Issues related to bias and efficiency of the estimates obtained by different analyses are discussed. We recommend a more widespread use of imputation models for the IT analysis.

journal_name

J Psychiatr Res

journal_title

Journal of psychiatric research

authors

Mazumdar S,Liu KS,Houck PR,Reynolds CF 3rd

doi

10.1016/s0022-3956(98)00058-2

subject

Has Abstract

pub_date

1999-03-01 00:00:00

pages

87-95

issue

2

eissn

0022-3956

issn

1879-1379

pii

S0022395698000582

journal_volume

33

pub_type

临床试验,杂志文章,随机对照试验

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