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High levels of missing outcome data for biologically confirmed substance use (BCSU) threaten the validity of substance use disorder (SUD) clinical trials. Underlying attributes of clinical trials could explain BCSU missingness and identify targets for improved trial design.
We reviewed 21 clinical trials funded by the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN) and published from 2005 to 2018 that examined pharmacologic and psychosocial interventions for SUD. We used configurational analysis-a Boolean algebra approach that identifies an attribute or combination of attributes predictive of an outcome-to identify trial design features and participant characteristics associated with high levels of BCSU missingness. Associations were identified by configuration complexity, consistency, coverage, and robustness. We limited results using a consistency threshold of 0.75 and summarized model fit using the product of consistency and coverage.
For trial design features, the final solution consisted of two pathways: psychosocial treatment as a trial intervention OR larger trial arm size (complexity=2, consistency=0.79, coverage=0.93, robustness score=0.71). For participant characteristics, the final solution consisted of two pathways: interventions targeting individuals with poly- or nonspecific substance use OR younger age (complexity=2, consistency=0.75, coverage=0.86, robustness score=1.00).
Conclusions: Psychosocial treatments, larger trial arm size, interventions targeting individuals with poly- or nonspecific substance use, and younger age among trial participants were predictive of missing BCSU data in SUD clinical trials. Interventions to mitigate missing data that focus on these attributes may reduce threats to validity and improve utility of SUD clinical trials.
Related protocols: CTN-0002, CTN-0003, CTN-0004, CTN-0006, CTN-0007, CTN-0009. CTN-0013, CTN-0014, CTN-0015, CTN-0017, CTN-0021, CTN-0029, CTN-0030, CTN-0031, CTN-0037, CTN-0044, CTN-0046, CTN-0048, CTN-0051, CTN-0053In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. This paper considers different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (CTN-0029), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome.
In conclusion, complete case analysis is wasteful and drops the power level to a large degree, resulting in an undetectable treatment effect. Also, it can introduce bias in the estimate of the treatment effects when the missingness mechanism depends on the outcome variable. This method is therefore invalid and the authors do not recommend it. Instead, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.
Related protocols: CTN-0029
This secondary analysis of data from National Drug Abuse Treatment Clinical Trials Network protocol CTN-0003 (“Suboxone (Buprenorphine/Naloxone) Taper: A Comparison of Taper Schedules”) compared three missing data strategies: 1) Latent growth model that assumes the data are missing at random (MAR), 2) Diggle-Kenward missing not at random (MNAR) model where dropout is a function of previous/concurrent urinalysis (UA) submissions, and 3) Wu-Carroll MNAR model where dropout is a function of the growth factors. CTN-0003 examined a 7-day versus 28-day taper for buprenorphine/naloxone to see which taper schedule reduced the likelihood of submitting an opioid-positive UA during treatment.
The MAR model showed a significant effect (B=-0.45, p <0.05) of trial arm on the opioid-positive UA slope (i.e., 28-day taper participants were less likely to submit a positive UA over time) with a small effect size (d=0.20). The MNAR Diggle-Kenward model demonstrated a significant (B=-0.64, p<0.01) effect of trial arm on the slope with a large effect size (d=0.82). The MNAR Wu-Carroll model evidenced a significant (B=-0.41, p<0.05) effect of trial arm on the UA slope that was relatively small (d=0.31).
Conclusions: This performance comparison of three missing data strategies (latent growth model, Diggle-Kenward selection model, Wu-Carrol selection model) on sample data indicates a need for increased use of sensitivity analyses in clinical trial research. Given the potential sensitivity of the trial arm effect to missing data assumptions, it is critical for researchers to consider whether the assumptions associated with each model are defensible.
The purpose of this study was to examine sensitivity to missing data procedures on treatment effects in a randomized controlled trial (RCT) of osmotic-release methylphenidate (OROS) for adolescents with co-occurring attention-deficit/hyperactivity disorder (ADHD) and substance use disorders (SUD). Data came from a National Drug Abuse Treatment Clinical Trials Network study (CTN-0028, N=303), which evaluated the safety/efficacy of a 16-week RCT of OROS vs. placebo in adolescents aged 13-18 with ADHD who were also receiving cognitive-behavioral therapy for their SUD. The two primary outcomes were clinician-reported ADHD symptoms and self-reported past 28 days of substance use (SU). A parallel grow model was used to assess the effect sizes assuming missing at random (MAR) compared to two missing not at random (MNAR) models: Diggle-Kenward (DK) selection model and Wu-Carroll (WC) selection model.
The MAR model found no significant treatment effect on ADHD or SU, and the effect sizes were small for both ADHD and SU. The MNAR DK model also produced non-significant treatment effects with similar effect sizes of ADHD and SU. The MNAR WC model evidenced a significant effect of OROS relative to placebo on SU, and the effect sizes for both ADHD and SU were larger than reported in the other models.
Conclusions: While the MAR model and one MNAR model found similarly sized effects as the original RCT, the second MNAR model produced different results for both of the outcomes. This sensitivity analysis highlights an important need for future RCTs of co-morbid mental illness and SUDs to carefully evaluate the missing data assumptions made when assessing treatment effects.
Related protocols: CTN-0028
A review of substance use clinical trials indicates that sub-optimal methods are the most commonly used procedures to deal with longitudinal missing information. In this study of data from the National Drug Abuse Treatment Clinical Trials Network buprenorphine protocol CTN-0003, listwise deletion (i.e., using complete cases only), positive urine analysis (UA) imputation, and multiple imputation (MI) were used to evaluate the effect of baseline substance use and buprenorphine/naloxone tapering schedule (7 or 28 days) on the probability of a positive UA (UA+) across the 4-week treatment period. The listwise deletion generalized estimating equations (GEE) model demonstrated that those in the 28-day taper group were less likely to submit a UA+ for opioids during the treatment period, as did the positive UA imputation model. The MI model also demonstrated a similar effect of taper group, but the effect size was more similar to that of the listwise deletion model.
Conclusions: The missing data situation described in this investigation generalizes to many other substance use psychopharmacology clinical trials wherein there is missing data on the outcome of interest only. Future researchers may find utilization of the MI procedure in conjunction with the common method of GEE analysis as a helpful analytic approach when the missing at random assumption is justifiable.
Related protocols: CTN-0003
Substance abuse treatment research is often characterized by having a non-trivial amount of missing data, especially in longitudinal, randomized clinical trials. A review of substance abuse clinical trials indicates that listwise deletion and single imputation (i.e., imputing missing values with a positive urine analysis) are the most commonly used procedures to deal with missing information. Due to the prevalence and complex nature of missing data in substance abuse research, the best missing data procedures need to be employed and tailored to each unique missing data situation so as to maximize the likelihood of arriving at the most accurate conclusions. This poster reports on a study that compared the outcomes from two common missing data handling procedures, using data sets from protocol CTN-0003, with the outcomes from the multiple imputation (MI) procedure in the context of generalized estimating equations (GEE) in order to demonstrate that interpretations of treatment effectiveness and other baseline covariate effects can change as a function of how the missing information is handled.
Conclusions: The investigation demonstrated how treatment efficacy can vary as a function of how the missing information is treated when there is a significant amount of it. Missing data theory suggests that listwise deletion and single imputation procedures should not be used to account for missing information, and that MI has advantages with respect to internal and external validity when the assumption of missing at random can be reasonably supported. Future researchers may find utilization of the MI procedure in conjunction with the common method of GEE analysis as a helpful analytic approach compared to commonly used procedures when the missing at random assumption is justifiable.
Related protocols: CTN-0003
Because the analysis of a treatment’s efficacy can vary as a function of how the missing data in a substance abuse clinical trial is handled analytically, there is a potential problem of not thoroughly evaluating and reporting the mechanism of missing information as part of the analytic strategy. Appropriate decision-making regarding how the missing data is handled is critical in order to make sound clinical inference based on randomized trials of substance abuse treatment, but missing not at random (MNAR) methodologies have no been explored in this context with real data. This investigation uses data from protocol CTN-0003 to compare 3 different modeling strategies for the handling of missing values (i.e., missing at random (MAR) model versus 2 different missing not at random (MNAR) models; Diggle-Kenward and Wu-Carroll selection modeling), to determine whether the treatment effect (i.e., impact of trial arm on linear urine analysis (UA) slope) is dependent on the missing data strategy used.
Conclusions: The trial arm effect on the UA slope (i.e., UA change over time), and other covariate effects, changed in a meaningful way across the MAR and MNAR growth models, indicating that missing data assumptions are critical to understand and explain in clinical trials. It is not only important for the research team to consider what the most likely missing data assumption is (i.e., MAR or MNAR), but also to consider whether or not the additional assumptions associated with each MNAR and MAR model are reasonable. This investigation highlights the potential for these modern approaches to missing data to shed new light on outcomes of interest (e.g., time-specific dropout) other than the primary outcome of UA.
Related protocols: CTN-0003
Two common procedures for the treatment of missing information, listwise deletion and positive urine analysis (UA) imputation (e.g., if the participant fails to provide urine for analysis, then score the UA positive), may result in significant biases during the interpretation of treatment effects. To compare these approaches and to offer a possible alternative, these two procedures were compared to the multiple imputation (MI) procedure with publicly available data from a recent clinical trial (National Drug Abuse Treatment Clinical Trials Network protocol CTN-0003, Ling et al, 2009). Listwise deletion, single imputation (i.e., positive UA imputation), and MI missing data procedures were used to comparatively examine the effect of the protocol’s two different buprenorphine/naloxone tapering schedules (7- or 28-days) for opioid addiction on the likelihood of a positive UA. The listwise deletion of missing data resulted in a nonsignificant effect for the taper while the positive UA imputation procedure resulted in a significant effects, replicating the original findings by Ling et al (2009). Although the MI procedure also resulted in a significant effect, the effect size was meaningfully smaller and the standard errors meaningfully larger when compared to the positive UA procedure. This study demonstrates that the researcher can obtain markedly different results depending on how the missing data are handled. Missing data theory suggests that listwise deletion and single imputation procedures should not be used to account for missing information, and that MI has advantages with respect to internal and external validity when the assumption of missing at random can be reasonably supported. Consistent with previous investigation of missing data in substance abuse treatment, the authors encourage researchers to understand and report the missing data mechanism as well as use newer procedures for the treatment of missing information (i.e., MI or direct maximum likelihood procedures) that are based on a research-specified “best estimate” of the missing values.
Related protocols: CTN-0003
Attrition in studies of substance use disorder treatment is problematic, potentially introducing bias into data analysis. This secondary analysis of data from protocol CTN-0010 (Buprenorphine/Naloxone-Facilitated Rehabilitation for Opioid Dependent Adolescents/Young Adults) aimed to determine the effect of participant compensation amounts on rates of missing data and observed rates of drug use. In the study, treatment-seeking opioid-dependent subjects aged 15-21 were randomized to a 2-week detoxification with buprenorphine/naloxone (DETOX) or 12 weeks buprenorphine/naloxone (BUP). Participants were compensated $5 for weekly urine drug screens and self-reported drug use information and $75 for more extensive assignments at weeks 4, 8, and 12. Though BUP assignment decreased the likelihood of missing data, there were significantly less missing data at 4, 8, and 12 weeks than other weeks, and the effect of compensation on the probability of urine screens being positive was more pronounced in DETOX subjects.
Conclusions: These findings suggest that variations in the amount of compensation for completing assignments can differentially affect outcome measurements, depending on treatment group assignment. Adequate financial compensation may minimize bias when treatment condition is associated with differential dropout and may be a cost-effective way to reduce attrition. Moreover, active users may be more likely than non-active users to drop out if compensation is inadequate, especially in control groups or in groups who are not receiving active treatment.
Related protocols: CTN-0010
This presentation from the “CTN Design & Analysis” workshop at the 2011 Steering Committee Meeting addresses the problem of missing data from CTN trials. The focus is mostly on primary outcomes data, which may be missing for a variety of reasons, including discontinuation of the study, outcomes undefined for some participants (such as quality of life measures after death), or attrition. Though CTN studies are focused on efficacy, not perfection (that is, it’s not “Does treatment work if perfectly delivered?” but instead “Is this a good treatment strategy?”), researchers should still strive to collect complete data from all participants, even those who do not complete the study, as results will never be believable, no matter how sophisticated the statistical method, if there is too much missing data. A variety of approaches for dealing with missing data are discussed, including ways to design trials to help minimize the likelihood of missing data. Ways to analyze missing data are also provided, including repeated-measure designs, linear and quadratic time trend or spline models, and the importance of sensitivity analysis. The presentation uses protocol CTN-0010 to provide a case study about ways to work with and around missing data.
Related protocols: CTN-0010
This ancillary investigation of data from protocol CTN-0010 (“Buprenorphine/Naloxone-Facilitated Rehabilitation for Opioid Dependent Adolescents/Young Adults”) examined the effect of monetary incentives on rates of missing data and observed rates of drug use among opioid dependent subjects aged 15-21 during participation in a randomized trial. Subjects seeking treatment for opioid dependence were randomized to 2 weeks of detoxification with buprenorphine/naloxone (DETOX) or 12 weeks of buprenorphine/naloxone (BUP), each with weekly individual and group drug counseling. At weeks 4, 8, and 12, extensive assessments were done and participants were given $75. At all other weeks, assessment was limited to urine drug screen and self-report of drug use, and compensation was only $5. A comparison of drug screens that were missing, positive for opioids, and negative for opioids in the high-reimbursement weeks versus the low-reimbursement rates found that rates of missing data were significantly lower for the high-reimbursement weeks than for the low ones.
This study demonstrated in quantitative terms the effect of participant compensation on rates of missing data and rates of documented drug use and abstinence. The results demonstrate the importance of adequate compensation to maintain follow-up rates, and suggest that the higher compensation rates preferentially enhanced follow-up rates among those who were using opioids.
Related protocols: CTN-0010