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Sensitivity analysis of longitudinal normal data with drop‐outs
Author(s) -
Minini Pascal,
Chavance Michel
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1702
Subject(s) - statistics , drop out , confidence interval , dropout (neural networks) , drop (telecommunication) , mathematics , sensitivity (control systems) , computer science , machine learning , telecommunications , electronic engineering , economics , engineering , demographic economics
We propose to perform a sensitivity analysis to evaluate the extent to which results from a longitudinal study can be affected by informative drop‐outs. The method is based on a selection model, where the parameter relating the dropout probability to the current observation is not estimated, but fixed to a set of values. This allows to evaluate several hypotheses for the degree of informativeness of the drop‐out process. Expectation and variance of missing data, conditional on the drop‐out time are computed, and a stochastic EM algorithm is used to obtain maximum likelihood estimates. Simulations show that when the drop‐out parameter is correctly specified, unbiased estimates of the other parameters are obtained, and coverage percentages of their confidence intervals are close to their theoretical value. More interestingly, misspecification of the drop‐out parameter does not considerably alter these results. This method was applied to a randomized clinical trial, designed to demonstrate non‐inferiority of an inhaled corticosteroid in terms of bone density, compared with a reference treatment. Sensitivity analysis showed that the conclusion of non‐inferiority was robust against different hypotheses for the drop‐out process. Copyright © 2004 John Wiley & Sons, Ltd.