scholarly journals Sensitivity analysis for non-monotone missing binary data in longitudinal studies: Application to the NIDA collaborative cocaine treatment study

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3057-3073 ◽  
Author(s):  
Garrett M Fitzmaurice ◽  
Stuart R Lipsitz ◽  
Roger D Weiss
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Drug Use ◽  
Longitudinal Studies ◽  
Binary Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Missing At Random ◽  
Selection Model ◽  
Longitudinal Binary Data

Conventional approaches for handling missingness in substance use disorder trials commonly rely upon a single deterministic “worst value” imputation that posits a perfect relationship between missingness and drug use (“missing value = presumed drug use”); this yields biased estimates of treatment effects and their standard errors. Instead, deterministic imputations should be replaced by probabilistic versions that encode researchers prior beliefs that those with missing data are more likely to be using drugs at those occasions. Motivated by this problem, we present a method for handling non-monotone missing binary data in longitudinal studies. Specifically, we consider a joint model that combines a not missing at random (NMAR) selection model with a generalized linear mixed model for longitudinal binary data. The selection model links the distribution of a missing outcome to the corresponding distribution of the outcome for those observed at that occasion via a fixed and known sensitivity parameter. The mixed model for longitudinal binary data assumes the random effects have bridge distributions; the latter yields regression parameters that have both subject-specific and marginal interpretations. This approach is completely transparent about what is being assumed about missing data and can be used as the basis for sensitivity analysis.

scholarly journals A generalized linear mixed model for longitudinal binary data with a marginal logit link function

2011 ◽  
Vol 5 (1) ◽  
pp. 449-467 ◽  
Author(s):  
Michael Parzen ◽  
Souparno Ghosh ◽  
Stuart Lipsitz ◽  
Debajyoti Sinha ◽  
Garrett M. Fitzmaurice ◽  
...  
Keyword(s):  
Binary Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Link Function ◽  
Longitudinal Binary Data

Analysis of crossover designs for longitudinal binary data with ignorable and nonignorable dropout

2021 ◽  
pp. 096228022110471
Author(s):  
Xi Wang ◽  
Vernon M. Chinchilli
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Binary Data ◽  
Likelihood Estimation ◽  
Real Data ◽  
Primary Objective ◽  
Crossover Designs ◽  
Longitudinal Binary Data ◽  
Pseudo Likelihood Estimation ◽  
Nonignorable Dropout

Longitudinal binary data in crossover designs with missing data due to ignorable and nonignorable dropout is common. This paper evaluates available conditional and marginal models and establishes the relationship between the conditional and marginal parameters with the primary objective of comparing the treatment mean effects. We perform extensive simulation studies to investigate these models under complete data and the selection models under missing data with different parametric distributions and missingness patterns and mechanisms. The generalized estimating equations and the generalized linear mixed-effects models with pseudo-likelihood estimation are advocated for valid and robust inference. We also propose a controlled multiple imputation method as a sensitivity analysis of the missing data assumption. Lastly, we implement the proposed models and the sensitivity analysis in two real data examples with binary data.

scholarly journals Subjective Well-Being in Early Adolescence: Observations from a Five-Year Longitudinal Study

2020 ◽  
Vol 17 (21) ◽  
pp. 8249
Author(s):  
Mònica González-Carrasco ◽  
Marc Sáez ◽  
Ferran Casas
Keyword(s):  
Data Collection ◽  
Longitudinal Studies ◽  
Early Adolescence ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Well Being ◽  
Subjective Well Being ◽  
Cross Sectional ◽  
Additional Information ◽  
Stage Of Development

This article aims to redress the lack of longitudinal studies on adolescents’ subjective well-being (SWB) and highlight the relevance of knowledge deriving from such research in designing public policies for improving their health and wellbeing in accordance with the stage of development they are in. To achieve this, the evolution of SWB during early adolescence (in adolescents aged between 10 and 14 in the first data collection) was explored over a five year period, considering boys and girls together and separately. This involved comparing different SWB scales and contrasting results when considering the year of data collection versus the cohort (year of birth) participants belonged to. The methodology comprised a generalized linear mixed model using the INLA (Integrated Nested Laplace Approximation) estimation within a Bayesian framework. Results support the existence of a decreasing-with-age trend, which has been previously intuited in cross-sectional studies and observed in only a few longitudinal studies and contrasts with the increasing-with-age tendency observed in late adolescence. This decrease is also found to be more pronounced for girls, with relevant differences found between instruments. The decreasing-with-age trend observed when the year of data collection is taken into account is also observed when considering the cohort, but the latter provides additional information. The results obtained suggest that there is a need to continue studying the evolution of SWB in early adolescence with samples from other cultures; this, in turn, will make it possible to establish the extent to which the observed decreasing-with-age trend among early adolescents is influenced by cultural factors.

The Impact of Clinical Review Bias on Child Language Grammaticality

2019 ◽  
Vol 41 (4) ◽  
pp. 214-221
Author(s):  
Anny P. Castilla-Earls ◽  
Brittany Harvey ◽  
Katrina Fulcher-Rood ◽  
Christopher D. Barr
Keyword(s):  
Clinical Review ◽  
Language Impairment ◽  
Binary Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Child Language ◽  
Communication Disorders ◽  
The Impact ◽  
English Students ◽  
Language Sample

The purpose of this study was to examine the impact of clinical review bias on the coding of grammaticality in child language. Seventy-four native-English students studying communication disorders and sciences made judgments about the grammaticality of 250 utterances presented in five language samples. Each language sample included grammatical, ungrammatical, and ambiguous utterances. Participants were randomly assigned to a blind or nonblind group. The nonblind group was presented with diagnostic information, whereas the blind group was not. We employed a generalized linear mixed model to examine the binary data. Our results suggest that both blind and nonblind participants were accurate in judging grammatical and ungrammatical utterances. However, nonblind participants were slightly more likely to judge ambiguous utterances as ungrammatical when the language sample identified the child as having a language impairment, suggesting that there was an effect of clinical review bias in this study. This effect, although statistically significant, was small.

scholarly journals P427 A hybrid approach of handling missing data in inflammatory bowel disease (IBD) trials: results from VISIBLE 1 and VARSITY

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S388-S389
Author(s):  
J Chen ◽  
S Hunter ◽  
K Kisfalvi ◽  
R A Lirio
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Statistical Power ◽  
Hybrid Approach ◽  
Missing At Random ◽  
P Value ◽  
Two Phase ◽  
Treatment Difference ◽  
Mayo Score ◽  
The Impact

Abstract Background Missing data is common in IBD trials. Depending on the volume and nature of missing data, it can reduce statistical power for detecting treatment difference, introduce potential bias and invalidate conclusions. Non-responder imputation (NRI), where patients (patients) with missing data are considered treatment failures, is widely used to handle missing data for dichotomous efficacy endpoints in IBD trials. However, it does not consider the mechanisms leading to missing data and can potentially underestimate the treatment effect. We proposed a hybrid (HI) approach combining NRI and multiple imputation (MI) as an alternative to NRI in the analyses of two phase 3 trials of vedolizumab (VDZ) in patients with moderate-to-severe UC – VISIBLE 11 and VARSITY2. Methods VISIBLE 1 and VARSITY assessed efficacy using dichotomous endpoints based on complete Mayo score. Full methodologies reported previously.1,2 Our proposed HI approach is aimed at imputing missing Mayo scores, instead of imputing the missing dichotomous efficacy endpoint. To assess the impact of dropouts for different missing data mechanisms (categorised as ‘missing not at random [MNAR]’ and ‘missing at random [MAR]’, HI was implemented as a potential sensitivity analysis, where dropouts owing to safety or lack of efficacy were imputed using NRI (assuming MNAR) and other missing data were imputed using MI (assuming MAR). For MI, each component of the Mayo score was imputed via a multivariate stepwise approach using a fully conditional specification ordinal logistic method. Missing baseline scores were imputed using baseline characteristics data. Missing scores from each subsequent visit were imputed using all previous visits in a stepwise fashion. Fifty imputation datasets were computed for each component of Mayo score. The complete Mayo score and relevant efficacy endpoints were derived subsequently. The analysis was performed within each imputed dataset to determine treatment difference, 95% CI and p-value, which were then combined via Rubin’s rules3. Results Tables 1 and 2 show a comparison of efficacy in the two studies using the primary NRI analysis vs. the alternative HI approach for handling missing data. Conclusion HI and NRI approaches can provide consistent efficacy analyses in IBD trials. The HI approach can serve as a useful sensitivity analysis to assess the impact of dropouts under different missing data mechanisms and evaluate the robustness of efficacy conclusions. Reference

scholarly journals Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies

2019 ◽  
Vol 11 (1) ◽  
pp. 184-205
Author(s):  
Tian Li ◽  
Julian M. Somers ◽  
Xiaoqiong J. Hu ◽  
Lawrence C. McCandless
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Longitudinal Studies

scholarly journals A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

2020 ◽  
Author(s):  
Suzie Cro ◽  
Tim P Morris ◽  
Brennan C Kahan ◽  
Victoria R Cornelius ◽  
James R Carpenter
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Treatment Effect ◽  
Missing At Random ◽  
Outcome Data ◽  
Sensitivity Analyses ◽  
Free World ◽  
Randomised Trials ◽  
Primary Analysis ◽  
Missing Not At Random

Abstract Background: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking.Methods: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a ‘pandemic-free world’ and ‘world including a pandemic’ are of interest. Results: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a ‘pandemic-free world’, participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the ‘world including a pandemic’, all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption – potentially incorporating a pandemic time-period indicator and participant infection status – or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses.Conclusions: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

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