scholarly journals Methods to Analyze Treatment Effects in the Presence of Missing Data for a Continuous Heavy Drinking Outcome Measure When Participants Drop Out from Treatment in Alcohol Clinical Trials

2014 ◽  
Vol 38 (11) ◽  
pp. 2826-2834 ◽  
Author(s):  
Katie Witkiewitz ◽  
Daniel E. Falk ◽  
Henry R. Kranzler ◽  
Raye Z. Litten ◽  
Kevin A. Hallgren ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Treatment Effects ◽  
Outcome Measure ◽  
Heavy Drinking ◽  
Drop Out ◽  
Drinking Outcome

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

scholarly journals SLE clinical trials: impact of missing data on estimating treatment effects

2019 ◽  
Vol 6 (1) ◽  
pp. e000348 ◽  
Author(s):  
Mimi Kim ◽  
Joan T Merrill ◽  
Cuiling Wang ◽  
Shankar Viswanathan ◽  
Ken Kalunian ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Multiple Imputation ◽  
Treatment Effects ◽  
Standard Of Care ◽  
Response Rates ◽  
Alternative Methods ◽  
Loss To Follow Up ◽  
Collective Data

ObjectiveA common problem in clinical trials is missing data due to participant dropout and loss to follow-up, an issue which continues to receive considerable attention in the clinical research community. Our objective was to examine and compare current and alternative methods for handling missing data in SLE trials with a particular focus on multiple imputation, a flexible technique that has been applied in different disease settings but not to address missing data in the primary outcome of an SLE trial.MethodsData on 279 patients with SLE randomised to standard of care (SoC) and also receiving mycophenolate mofetil (MMF), azathioprine or methotrexate were obtained from the Lupus Foundation of America-Collective Data Analysis Initiative Database. Complete case analysis (CC), last observation carried forward (LOCF), non-responder imputation (NRI) and multiple imputation (MI) were applied to handle missing data in an analysis to assess differences in SLE Responder Index-5 (SRI-5) response rates at 52 weeks between patients on SoC treated with MMF versus other immunosuppressants (non-MMF).ResultsThe rates of missing data were 32% in the MMF and 23% in the non-MMF groups. As expected, the NRI missing data approach yielded the lowest estimated response rates. The smallest and least significant estimates of differences between groups were observed with LOCF, and precision was lowest with the CC method. Estimated between-group differences were magnified with the MI approach, and imputing SRI-5 directly versus deriving SRI-5 after separately imputing its individual components yielded similar results.ConclusionThe potential advantages of applying MI to address missing data in an SLE trial include reduced bias when estimating treatment effects, and measures of precision that properly reflect uncertainty in the imputations. However, results can vary depending on the imputation model used, and the underlying assumptions should be plausible. Sensitivity analysis should be conducted to demonstrate robustness of results, especially when missing data proportions are high.

scholarly journals Breathalyser-Based eHealth Data Suggest That Self-Reporting of Abstinence Is a Poor Outcome Measure for Alcohol Use Disorder Clinical Trials

2020 ◽  
Vol 55 (3) ◽  
pp. 237-245 ◽  
Author(s):  
Markku D Hämäläinen ◽  
Andreas Zetterström ◽  
Maria Winkvist ◽  
Marcus Söderquist ◽  
Patrik Öhagen ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Alcohol Use ◽  
Alcohol Use Disorder ◽  
Outcome Measure ◽  
Heavy Drinking ◽  
Control Group ◽  
Significant Difference ◽  
And Control ◽  
Positive Effect

Abstract Aims To evaluate the efficacy and monitoring capabilities of a breathalyser-based eHealth system for patients with alcohol use disorder (AUD) and to investigate the quality and validity of timeline follow-back (TLFB) as outcome measure in clinical trials and treatment. Methods Patients (n = 115) were recruited to clinical trials from a 12-step aftercare programme (12S-ABS) and from hospital care with abstinence (HC-ABS) or controlled drinking (HC-CDR) as goal and randomly divided into an eHealth and a control group. The effect of the eHealth system was analysed with TLFB-derived primary outcomes—change in number of abstinent days (AbsDay) and heavy drinking days (HDDs) compared to baseline—and phosphatidyl ethanol (PEth) measurements. Validity and quality of TLFB were evaluated by comparison with breath alcohol content (BrAC) and eHealth digital biomarkers (DBs): Addiction Monitoring Index (AMI) and Maximum Time Between Tests (MTBT). TLFB reports were compared to eHealth data regarding reported abstinence. Results The primary outcome (TLFB) showed no significant difference between eHealth and control groups, but PEth did show a significant difference especially at months 2 and 3. Self-reported daily abstinence suffered from severe quality issues: of the 28-day TLFB reports showing full abstinence eHealth data falsified 34% (BrAC measurements), 39% (MTBT), 54% (AMI) and 68% (BrAC/MTBT/AMI). 12S-ABS and HC-ABS patients showed severe under-reporting. Conclusions No effect of the eHealth system was measured with TLFB, but a small positive effect was measured with PEth. The eHealth system revealed severe quality problems with TLFB, especially regarding abstinence—should measurement-based eHealth data replace TLFB as outcome measure for AUD?

Assessing and interpreting treatment effects in longitudinal clinical trials with missing data

2003 ◽  
Vol 53 (8) ◽  
pp. 754-760 ◽  
Author(s):  
Craig H Mallinckrodt ◽  
Todd M Sanger ◽  
Sanjay Dubé ◽  
David J DeBrota ◽  
Geert Molenberghs ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Treatment Effects

Missing repeated measures data in clinical trials

2021 ◽  
Author(s):  
Stephanie L Pugh ◽  
Paul D Brown ◽  
Danielle Enserro
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Repeated Measures ◽  
Drop Out ◽  
Sensitivity Analyses ◽  
Analysis Methods ◽  
Patient Reported ◽  
The Impact ◽  
Proper Interpretation

Abstract Clinical trials typically collect longitudinal data, data that is collected repeated over time, such as labs, scans, or patient-reported outcomes. Due to a variety of reasons, this data can be missing, whether a patient stops attending clinical visits (i.e., drop-out), or misses assessments intermittently. Understanding the reasons for missing data as well as predictors of missing data can aid in determination of the missing data mechanism. The analysis methods employed are dependent on the missing data mechanism and may make certain assumptions about the missing data itself. Methods for non-ignorable missing data, which assumes that the missing data depends on the missing data itself, make stronger assumptions and include pattern mixture-models and shared parameter models. Missing data that is ignorable after adjusting for other covariates, can be analyzed using methods that adjust for covariates, such as mixed effects models or multiple imputation. Missing data that is ignorable can be analyzed using standard approaches that require complete case data, such as change from baseline or proportion of patients who declined at a specified time point. In clinical trials, truly ignorable data is rare, resulting in additional analysis methods required for proper interpretation of the results. Conducting several analyses under different assumptions, called sensitivity analyses, can determine the extent of the impact of the missing data.

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