A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials

Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity analyses are not widely used. We propose a mean score approach for exploring global sensitivity to departures from missing at random or other assumptions about incomplete outcome data in a randomised trial. We assume a single outcome analysed under a generalised linear model. One or more sensitivity parameters, specified by the user, measure the degree of departure from missing at random in a pattern mixture model. Advantages of our method are that its sensitivity parameters are relatively easy to interpret and so can be elicited from subject matter experts; it is fast and non-stochastic; and its point estimate, standard error and confidence interval agree perfectly with standard methods when particular values of the sensitivity parameters make those standard methods appropriate. We illustrate the method using data from a mental health trial

What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry

Medical Research Council Clinical Trails Unit at UCL [Studentship to MS; MC EX G0800814 to JRC,TPM]

The impact of missing data on clinical trials : a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder.

CAPRISA, 2013.Rationale and objective Hypericum perforatum (St John's wort) is used to treat depression, but the effectiveness has not been established. Recent guidelines described the analysis of clinical trials with missing data, inspiring the reanalysis of this trial using proper missing data methods. The objective was to determine whether hypericum was superior to placebo in treating major depression. Methods A placebo-controlled, randomized clinical trial was conducted for 8 weeks to determine the effectiveness of hypericum or sertraline in reducing depression, measured using the Hamilton depression scale. We performed sensitivity analyses under different assumptions about the missing data process. Results Three hundred forty participants were randomized, with 28 % lost to follow-up. The missing data mechanism was not missing completely at random. Under missing at random assumptions, some sensitivity analyses found no difference between either treatment arm and placebo, while some sensitivity analyses found a significant difference from baseline to week 8 between sertraline and placebo (−1.28, 95 % credible interval [−2.48; −0.08]), but not between hypericum and placebo (0.56, [−0.64;1.76]). The results were similar when the missing data process was assumed to be missing not at random. Conclusions There is no difference between hypericum and placebo, regardless of the assumption about the missing data process. There is a significant difference between sertraline and placebo with some statistical methods used. It is important to conduct an analysis that takes account of missing data using valid statistically principled methods. The assumptions about the missing data process could influence the results

Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data

<p>Abstract</p> <p>Background</p> <p>Multiple Imputation as usually implemented assumes that data are Missing At Random (MAR), meaning that the underlying missing data mechanism, given the observed data, is independent of the unobserved data. To explore the sensitivity of the inferences to departures from the MAR assumption, we applied the method proposed by Carpenter <it>et al.</it> (2007).</p> <p>This approach aims to approximate inferences under a Missing Not At random (MNAR) mechanism by reweighting estimates obtained after multiple imputation where the weights depend on the assumed degree of departure from the MAR assumption.</p> <p>Methods</p> <p>The method is illustrated with epidemiological data from a surveillance system of hepatitis C virus (HCV) infection in France during the 2001–2007 period. The subpopulation studied included 4343 HCV infected patients who reported drug use. Risk factors for severe liver disease were assessed. After performing complete-case and multiple imputation analyses, we applied the sensitivity analysis to 3 risk factors of severe liver disease: past excessive alcohol consumption, HIV co-infection and infection with HCV genotype 3.</p> <p>Results</p> <p>In these data, the association between severe liver disease and HIV was underestimated, if given the observed data the chance of observing HIV status is high when this is positive. Inference for two other risk factors were robust to plausible local departures from the MAR assumption.</p> <p>Conclusions</p> <p>We have demonstrated the practical utility of, and advocate, a pragmatic widely applicable approach to exploring plausible departures from the MAR assumption post multiple imputation. We have developed guidelines for applying this approach to epidemiological studies.</p

A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures

Background: Retaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data. Methods: A systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included. Results: Among the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n = 54, 66%). Other papers used more sophisticated methods including multiple imputation (n = 5) or fully Bayesian modeling (n = 1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n = 7), the missing indicator method (n = 1), and mean value substitution (n = 3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated. Conclusions: This review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies. © 2012 Karahalios et al.; licensee BioMed Central Ltd