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The importance of randomization in clinical trials has long been acknowledged for avoiding selection bias. Yet, bias concerns re-emerge with selective attrition. This study takes a causal inference perspective in addressing distinct scenarios of missing outcome data (MCAR, MAR and MNAR). This study adopts a causal inference perspective in providing an overview of empirical strategies to estimate the average treatment effect, improve precision of the estimator, and to test whether the underlying identifying assumptions hold. We propose to use Random Forest Lee Bounds (RFLB) to address selective attrition and to obtain more precise average treatment effect intervals. When assuming MCAR or MAR, the often untenable identifying assumptions with respect to causal inference can hardly be verified empirically. Instead, missing outcome data in clinical trials should be considered as potentially non-random unobserved events (i.e. MNAR). Using simulated attrition data, we show how average treatment effect intervals can be tightened considerably using RFLB, by exploiting both continuous and discrete attrition predictor variables. Bounding approaches should be used to acknowledge selective attrition in randomized clinical trials in acknowledging the resulting uncertainty with respect to causal inference. As such, Random Forest Lee Bounds estimates are more informative than point estimates obtained assuming MCAR or MAR.

This study explored the economic costs and response rate of mail and web-based surveys with practicing dentists. A random sample of 6,000 practicing dentists was randomly assigned into three groups of 2,000: choice (mail or web-based), postal mail, or web-based. The Florida Tobacco Control Survey 2009, which is composed of 28 questions (including subject demographic questions), served as the survey instrument. A total of 1,232 surveys were returned by the three different groups (21% overall response rate). Response rates were best for the mail (26%) with the worst response rate coming from the Web group (11%). However, a cost-effectiveness analysis revealed that web surveys were 2.68 times more cost effective.

Background Inappropriate medication and polypharmacy increase morbidity, hospitalisation rate, costs and mortality in multimorbid patients. At hospital discharge of elderly patients, polypharmacy is often even more pronounced than at admission. However, the optimal discharge strategy in view of sustained medication appropriateness remains unclear. In particular, unreflectingly switching back to the pre-hospitalisation medication must be avoided. Therefore, both the patients and the follow-up physicians should be involved in the discharge process. In this study, we aim to test whether a brief medication review which takes the patients’ priorities into account, combined with a standardised communication strategy at hospital discharge, leads to sustained medication appropriateness and extends readmission times among elderly multimorbid patients. Methods The study is designed as a two-armed, double-blinded, cluster-randomised trial, involving 42 senior hospital physicians (HPs) with their junior HPs and 2100 multimorbid patients aged 60 years or older. Using a randomised minimisation strategy, senior HPs will be assigned to either intervention or control group. Following instructions of the study team, the senior HPs in the intervention group will teach their junior HPs how to integrate a simple medication review tool combined with a defined communication strategy into their ward’s discharge procedure. The untrained HPs in the control group will provide data on usual care, and their patients will be discharged following usual local routines. Primary outcome is the time until readmission within 6 months after discharge, and secondary outcomes cover readmission rates, number of emergency and GP visits, classes and numbers of drugs prescribed, proportions of potentially inappropriate medications, and the patients’ quality of life after discharge. Additionally, the characteristics of both the HPs as well as the patients will be collected before the intervention. Process evaluation outcomes will be assessed parallel to the ongoing core study using qualitative research methods. Discussion So far, interventions to reduce polypharmacy are still scarce at the crucial interface between HPs and GPs. To our knowledge, this trial is the first to analyse the combination of a brief deprescribing intervention with a standardised communication strategy at hospital discharge and in the early post-discharge period. Trial registration ISRCTN, ISRCTN18427377 . Registered 11 January 2018

In randomized controlled trials (RCTs), outcome variables are often patient-reported outcomes measured with questionnaires. Ideally, all available item information is used for score construction, which requires an item response theory (IRT) measurement model. However, in practice, the classical test theory measurement model (sum scores) is mostly used, and differences between response patterns leading to the same sum score are ignored. The enhanced differentiation between scores with IRT enables more precise estimation of individual trajectories over time and group effects. The objective of this study was to show the advantages of using IRT scores instead of sum scores when analyzing RCTs. Two studies are presented, a real-life RCT, and a simulation study. Both IRT and sum scores are used to measure the construct and are subsequently used as outcomes for effect calculation. The bias in RCT results is conditional on the measurement model that was used to construct the scores. A bias in estimated trend of around one standard deviation was found when sum scores were used, where IRT showed negligible bias. Accurate statistical inferences are made from an RCT study when using IRT to estimate construct measurements. The use of sum scores leads to incorrect RCT results.

Background Randomized controlled trials (RCTs) provide the highest level of scientific evidence, but successful participant recruitment is critical to ensure the external and internal validity of results. This study describes the strategies associated with recruiting older adults at increased falls risk residing in retirement villages into an 18-month cluster RCT designed to evaluate the effects of a dual-task exercise program on falls and physical and cognitive function. Methods Recruitment of adults aged ≥65 at increased falls risk residing within retirement villages (size 60–350 residents) was initially designed to occur over 12 months using two distinct cohorts (C). Recruitment occurred via a three-stage approach that included liaising with: 1) village operators, 2) independent village managers, and 3) residents. To recruit residents, a variety of different approaches were used, including distribution of information pack, on-site presentations, free muscle and functional testing, and posters displayed in common areas. Results Due to challenges with recruitment, three cohorts were established between February 2014 and April 2015 (14 months). Sixty retirement villages were initially invited, of which 32 declined or did not respond, leaving 28 villages that expressed interest. A total of 3947 individual letters of invitation were subsequently distributed to residents of these villages, from which 517 (13.1%) expressions of interest (EOI) were received. Across three cohorts with different recruitment strategies adopted there were only modest differences in the number of EOI received (10.5 to 15.3%), which suggests that no particular recruitment approach was most effective. Following the initial screening of these residents, 398 (77.0%) participants were deemed eligible to participate, but a final sample of 300 (58.0% of the 517 EOI) consented and was randomized; 7.6% of the 3947 residents invited. Principal reasons for not participating, despite being eligible, were poor health, lack of time and no GP approval. Conclusion This study highlights that there are significant challenges associated with recruiting sufficient numbers of older adults from independent living retirement villages into an exercise intervention designed to improve health and well-being. Trial registration Australian New Zealand Clinical Trials Registry: ACTRN12613001 161718 . Date registered 23rd October 2013.

A survey of randomized controlled trials found that almost a quarter of trials had more than 10% of responses missing for the primary outcome. There are a number of ways in which data could be missing: the subject is unable to provide it, or they withdraw, or become lost to follow-up. Such attrition means that balance in baseline characteristics for those randomized may not be maintained in the subsample who has outcome data. For individual trials, if the attrition is systematic and linked to outcome, then this will result in biased estimates of the overall effect. It then follows that if such trials are combined in a meta-analysis, it will result in a biased estimate of the overall effect and be misleading. The aim of this study was to investigate the impact of attrition on baseline imbalance within individual trials and across multiple trials. In this article, we used individual patient data from a convenience sample of 10 trials evaluating interventions for the treatment of musculoskeletal disorders. Meta-analyses using the mean difference at baseline between the trial arms were carried out using individual patient data from these trials. The analyses were first carried out using all randomized participants and secondly only including participants with outcome data on the quality-of-life score. Meta-regression was carried out to evaluate whether the level of baseline imbalance was associated with the level of attrition. The overall attrition rates for the quality-of-life score ranged between 4% and 28% of the total randomized patients. All trials showed some level of differential attrition between the treatment arms, ranging from 1% to 14%. Attrition within the control group ranged from 3% to 25% and within the intervention group, it ranged from 0% to 31%. For individual trials, there was no indication that attrition altered the results in favor of either the treatment or the control. Forest plots highlighted that the attrition had some impact on the baseline imbalance for the primary outcome score as more heterogeneity was introduced (I-squared value of 0.4% for the initial data set vs. I-squared value of 16.9% for the analyzed data set). However, the standardized mean difference increased only slightly (from 0.01 to 0.03 with 95% confidence interval [CI]: −0.05, 0.10). Meta-regression showed little or no evidence of a significant dose–response relationship between the level of attrition and the baseline imbalance (coefficient 0.73, 95% CI: −0.81, 2.28). Although, in theory, attrition can introduce selection bias in randomized trials, we did not find sufficient evidence to support this claim in our convenience sample of trials. However, the number of trials included was relatively small, which may have led to small but important differences in outcomes being missed. In addition, only 2 of 10 trials included had attrition levels greater than 15% suggesting a low level of potential bias. Meta-analyses and systematic reviews should always consider the impact of attrition on baseline imbalances and where possible any baseline imbalances in the analyzed data set and their impact on the outcomes reported.

Baseline data collected on each patient at randomisation in controlled clinical trials can be used to describe the population of patients, to assess comparability of treatment groups, to achieve balanced randomisation, to adjust treatment comparisons for prognostic factors, and to undertake subgroup analyses. We assessed the extent and quality of such practices in major clinical trial reports. A sample of 50 consecutive clinical-trial reports was obtained from four major medical journals during July to September, 1997. We tabulated the detailed information on uses of baseline data by use of a standard form. Most trials presented baseline comparability in a table. These tables were often unduly large, and about half the trials inappropriately used significance tests for baseline comparison. Methods of randomisation, including possible stratification, were often poorly described. There was little consistency over whether to use covariate adjustment and the criteria for selecting baseline factors for which to adjust were often unclear. Most trials emphasised the simple unadjusted results and covariate adjustment usually made negligible difference. Two-thirds of the reports presented subgroup findings, but mostly without appropriate statistical tests for interaction. Many reports put too much emphasis on subgroup analyses that commonly lacked statistical power. Clinical trials need a predefined statistical analysis plan for uses of baseline data, especially covariate-adjusted analyses and subgroup analyses. Investigators and journals need to adopt improved standards of statistical reporting, and exercise caution when drawing conclusions from subgroup findings.

Introduction and hypothesis Missing data is frequently observed in clinical trials; high rates of missing data may jeopardize trial outcome validity. Purpose We determined the rates of missing data over time, by type of data collected and compared demographic and clinical factors associated with missing data among women who participated in two large randomized clinical trials of surgery for stress urinary incontinence, the Stress Incontinence Surgical Treatment Efficacy Trial (SISTEr) and the Trial of Midurethral Sling (TOMUS). Methods The proportions of subjects who attended and missed each follow-up visit were calculated. The chi-squared test, Fisher’s exact test and t test were used to compare women with and without missing data, as well as the completeness of the data for each component of the composite primary outcome. Results Data completeness for the primary outcome computation in the TOMUS trial (62.3 %) was nearly double that in the SISTEr trial (35.7 %). The follow-up visit attendance rate decreased over time. A higher proportion of subjects attended all follow-up visits in the TOMUS trial and overall there were fewer missing data for the period that included the primary outcome assessment at 12 months. The highest levels of complete data for the composite outcome variables were for the symptoms questionnaire (SISTEr 100 %, TOMUS 99.8 %) and the urinary stress test (SISTEr 96.1 %, TOMUS 96.7 %). In both studies, the pad test was associated with the lowest levels of complete data (SISTEr 85.1 %, TOMUS 88.3 %) and approximately one in ten subjects had incomplete voiding diaries at the time of primary outcome assessment. Generally, in both studies, a higher proportion of younger subjects had missing data. This analysis lacked a patient perspective as to the reasons for missing data that could have provided additional information on subject burden, motivations for adherence and study design. In addition, we were unable to compare the effects of the different primary outcome assessment time-points in an identically designed trial. Conclusions Missing visits and data increased with time. Questionnaire data and physical outcome data (urinary stress test) that could be assessed during a visit were least prone to missing data, whereas data for variables that required subject effort while away from the research team (pad test, voiding diary) were more likely to be missing. Older subjects were more likely to provide complete data.

Very little is known about predictors of response rates to long-term follow-up mail-out surveys, including whether the timing of an incentive affects response rates. We aimed to determine whether the timing of the incentive affects response rates and what baseline demographic and psychological factors predict response rates to a 12 year follow-up survey. Participants were 450 randomly selected people from the Penrith population, Australia who had previously participated in a mail-out survey 12 years earlier. By random allocation, 150 people received no incentive, 150 received a lottery ticket inducement with the follow-up survey and 150 received a lottery ticket inducement on the return of a completed survey. The overall response rate for the study was 63%. There were no significant differences in terms of response rates between the no incentive (58.8%;95%CI 49.8%,67.3%), incentive with survey (65.1%;95%CI 56.2%,73.3%) and promised incentive (65.3%;95%CI 56.1%,73.7%) groups. Independent predictors of responding to the 12 year survey were being older (OR=1.02, 95%CI 1.01,1.05,P=0.001) and being less neurotic as reported on the first survey 12 years earlier (OR=0.92, 95%CI 0.86,0.98, P=0.010). Psychological factors may play a role in determining who responds to long-term follow-up surveys although timing of incentives does not.

The New York Heart Association (NYHA) functional class is a subjective estimate of a patient's functional ability based on symptoms that do not always correlate with the objective estimate of functional capacity, peak oxygen consumption (peak V o 2). In addition, relationships between these 2 measurements have not been examined in the current medical era when patients are using β-blockers, aldosterone antagonists, and cardiac resynchronization therapy (CRT). Using baseline data from the HF-ACTION (Heart Failure and A Controlled Trial Investigating Outcomes of Exercise TraiNing) study, we examined this relationship. One thousand seven hundred fifty-eight patients underwent a symptom-limited metabolic stress test and stopped exercise due to dyspnea or fatigue. The relationship between NYHA functional class and peak V o 2 was examined. In addition, the effects of β-blockers, aldosterone antagonists, and CRT therapy on these relationships were compared. The NYHA II patients have a significantly higher peak V o 2 (16.1 ± 4.6 vs 13.0 ± 4.2 mL/kg per minute), a lower ventilation (Ve)/V co 2 slope (32.8 ± 7.7 vs 36.8 ± 10.4), and a longer duration of exercise (11.0 ± 3.9 vs 8.0 ± 3.4 minutes) than NYHA III/IV patients. Within each functional class, there was no difference in any of the exercise parameters between patients on or off of β-blockers, aldosterone antagonists, or CRT therapy. Finally, with increasing age, a significant difference in peak V o 2, Ve/V co 2 slope, and exercise time was found. For patients being treated with current medical therapy, there still is a difference in true functional capacity between NYHA functional class II and III/IV patients. However, within each NYHA functional class, the presence or absence or contemporary heart failure therapies does not alter exercise parameters.