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Introduction: In this study, penalized imputation (PI), a common approach to handle missing smoking status data and sometimes referred to as "missing=smoking," is compared with other missing data approaches using data from internet-based smoking cessation trials. Two hypotheses were tested: (1) PI leads to more conservative effect estimates than complete observations analysis; and (2) PI and multiple imputation (MI) lead to similar effect estimates under balanced (equal missingness proportions among the trial arms) and unbalanced missingness.Methods: First, the outcomes of 22 trials included in a recent Cochrane review on internet-based smoking cessation interventions were reanalyzed using only the complete observations, and after applying PI. Second, in a simulation study outcomes under PI, complete observations analysis, and two types of MI were compared. For this purpose, individual patient data from one of the Cochrane review trials were used. Results of the missing data approaches were compared with reference data without missing observations, upon which balanced and unbalanced missingness scenarios were imposed.Results: In the reanalysis of 22 trials, relative risks (RR = 1.15 [1.00; 1.33]) after PI were nearly identical to those under complete observations analysis (RR = 1.14 [0.98; 1.32]). In the simulation study, PI was the only approach that led to deviations from the reference data beyond its 95% confidence interval.Conclusions: Analyses after PI led to pooled results equivalent to complete observations analyses. PI also led to significant deviations from the reference in the simulation studies. PI biases the reported effects of interventions, favoring the condition with the lowest proportion of missingness. Therefore, more sophisticated missing data approaches than PI should be applied. [ABSTRACT FROM AUTHOR]

Background: Patients with substance use disorder (SUD) or Attention Deficit Hyperactivity Disorder (ADHD) have a high risk of drop out from treatment. Few studies have investigated predictors of therapy drop out and outcome in SUD patients with comorbid ADHD. Recently, integrated cognitive behavioral therapy (CBT/Integrated) was shown to be more effective than standard CBT (CBT/SUD) in the treatment of SUD + ADHD.Objective: To investigate the association of demographic, clinical and neurocognitive variables with drop-out and treatment outcome, and to examine which of these variables are suitable for patient-treatment matching.Methods: We performed an RCT in which 119 patients were allocated to CBT/Integrated (n = 60) or CBT/SUD (n = 59). In addition, 55 patients had dropped out before randomization. Demographic variables, clinical characteristics and measures of cognitive functioning (Stroop, Tower of London (ToL) and Balloon Analogue Risk Task (BART)) were included as predictors. Outcome measures were: early treatment drop-out, ADHD symptom severity, and substance use severity at end of treatment and follow up.Results: Primary substance of abuse (drugs as opposed to alcohol only) and lower accuracy scores on the ToL were significant predictors of early treatment drop-out. Having more depression and anxiety symptoms and using ADHD medication at baseline significantly predicted more ADHD symptoms at end of treatment, and higher accuracy scores on the ToL significantly predicted higher substance use at end of treatment. No significant predictor-by-treatment interactions were found.Conclusion: The results add to the existing realization that also relatively mild cognitive deficits are a risk factor for treatment drop-out in these patients. [ABSTRACT FROM AUTHOR]

Background: Antidepressant trials are criticized due to potential methodological flaws. Root causes of failing methodology can be found in insufficient inter-rater reliability (IRR) and training practices, leading to higher placebo response and reduced study-power. However, it is unknown to what extent reliability estimates or training procedures are currently included in antidepressant reports. Therefore, we aimed to determine the proportion of publications concerning double-blind randomized controlled antidepressant trials that report on IRR coefficients and training procedures.Methods: We extracted all double-blind randomized clinical trials (RCTs) from the meta-analysis of Cipriani et al. (2018) concerning the period from 2000 until January 2016. Further, we conducted a Medline-search for double-blind RCTs from January 2016 until January 2020 for additional reports. We identified IRR coefficients and training procedures in these publications.Results: In total we identified 179 double-blind RCTs. Only 4.5% reported an IRR coefficient whereas 27.9% reported training procedures.Limitations: We did not contact individual authors for additional information regarding implementation of training procedures or inter-rater reliability assessment.Conclusions: There is a substantial lack of reporting IRR coefficients and training procedures in RCTs with antidepressant medication. Considering the large implications of insufficient reliability, we urge researchers to conduct and report training procedures and reliability estimations. [ABSTRACT FROM AUTHOR]