Optimal strategies to improve uptake of and adherence to HIV prevention among young people at risk for HIV acquisition in the USA (ATN 149): a randomised, controlled, factorial trial.

BACKGROUND: Pre-exposure prophylaxis (PrEP), condom use, post-exposure prophylaxis (PEP), and sexual partner reduction help to prevent HIV acquisition but have low uptake among young people. We aimed to assess the efficacy of automated text messaging and monitoring, online peer support, and strengths-based telehealth coaching to improve uptake of and adherence to PrEP, condom use, and PEP among adolescents aged 12-24 years at risk of HIV acquisition in Los Angeles, CA, USA, and New Orleans, LA, USA. METHODS: We conducted a four-arm randomised controlled factorial trial, assessing interventions designed to support uptake and adherence of HIV prevention options (ie, PrEP, PEP, condom use, and sexual partner reduction). We recruited young people aged 12-24 years who were at risk of HIV acquisition from 13 community-based organisations, adolescent medicine clinics, and organisations serving people who are unstably housed, people who were previously incarcerated, and other vulnerable young people, and through dating apps, peer referrals, and social venues and events in Los Angeles, CA, USA, and New Orleans, LA, USA. Young people who tested seronegative and reported being gay, bisexual, or other men who have sex with men, transgender men or women, or gender diverse (eg. non-binary or genderqueer) were eligible for inclusion. Participants were randomly assigned to one of four intervention groups in a factorial design: automated text messaging and monitoring (AMMI) only, AMMI plus peer support via private social media, AMMI plus strengths-based telehealth coaching by near-peer paraprofessionals, or AMMI plus peer support and coaching. Assignment was further stratified by race or ethnicity and sexual orientation within each interviewer's group of participants. Participants were masked to intervention assignment until after baseline interviews when offered their randomly assigned intervention, and interviewers were masked throughout the study. Interventions were available throughout the 24-month follow-up period, and participants completed baseline and follow-up assessments, including rapid diagnostic tests for sexually transmitted infections, HIV, and substance use, at 4-month intervals over 24 months. The primary outcomes were uptake and adherence to HIV prevention options over 24 months, measured by self-reported PrEP use and adherence, consistent condom use with all partners, PEP prescription and adherence, and number of sexual partners in participants with at least one follow-up. We used Bayesian generalised linear modelling to assess changes in outcomes over time comparing the four study groups. This study is registered with ClinicalTrials.gov (NCT03134833) and is completed. FINDINGS: We screened 2314 adolescents beginning May 1, 2017, to enrol 1037 participants (45%) aged 16-24 years between May 6, 2017, and Aug 30, 2019, of whom 895 (86%) had follow-up assessments and were included in the analytical sample (313 assigned to AMMI only, 205 assigned to AMMI plus peer support, 196 assigned to AMMI plus coaching, and 181 assigned to AMMI plus peer support and coaching). Follow-up was completed on Nov 8, 2021. Participants were diverse in race and ethnicity (362 [40%] Black or African American, 257 [29%] Latinx or Hispanic, 184 [21%] White, and 53 [6%] Asian or Pacific Islander) and other sociodemographic factors. At baseline, 591 (66%) participants reported anal sex without a condom in the past 12 months. PrEP use matched that in young people nationally, with 101 (11%) participants reporting current PrEP use at baseline, increasing at 4 months to 132 (15%) and continuing to increase in the AMMI plus peer support and coaching group (odds ratio 2·31, 95% CI 1·28-4·14 vs AMMI control). There was no evidence for intervention effect on condom use, PEP use (ie, prescription or adherence), PrEP adherence, or sexual partner numbers. No unanticipated or study-related adverse events occurred. INTERPRETATION: Results are consistent with hypothesised synergistic intervention effects of evidence-based functions of informational, motivational, and reminder messaging; peer support for HIV prevention; and strengths-based, goal-focused, and problem-solving telehealth coaching delivered by near-peer paraprofessionals. These core functions could be flexibly scaled via combinations of technology platforms and front-line or telehealth HIV prevention workers. FUNDING: Adolescent Medicine Trials Network for HIV/AIDS Interventions, US National Institutes of Health.

Does haste make waste? Prevalence and types of errors reported after publication of studies of COVID-19 therapeutics.

BACKGROUND: The COVID-19 pandemic spurred publication of a rapid proliferation of studies on potential therapeutic agents. While important for the advancement of clinical care, pressure to collect, analyze, and report data in an expedited manner could potentially increase the rate of important errors, some of which would be captured in published errata. We hypothesized that COVID-19 therapeutic studies published in the early years of the pandemic would be associated with a high rate of published errata and that, within these errata, there would be a high prevalence of serious errors. METHODS: We performed a review of published errata associated with empirical studies of COVID-19 treatments. Errata were identified via a MEDLINE and Embase search spanning January 2020 through September 2022. Errors located within each published erratum were characterized by location within publication, error type, and error seriousness. RESULTS: Of 47 studies on COVID-19 treatments with published errata, 18 met inclusion criteria. Median time from publication of the original article to publication of the associated erratum was 76 days (range, 12-511 days). A majority of errata addressed issues with author attribution or conflict of interest disclosures (39.5%) or numerical results (25.6%). Only one erratum contained a serious error: a typographical error which could have misled readers into believing that the treatment in question had serious adverse effects when in fact it did not. CONCLUSIONS: Despite accelerated publication times, we found among studies of COVID-19 treatments the majority of errata (17/18) reported minor errors that did not lead to misinterpretation of the study results. Retractions, an indicator of scientific misdirection even more concerning than errata, were beyond the scope of this review.

Accounting for Context in Randomized Trials after Assignment.

Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization - i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.

Conduct and Implementation of Personalized Trials in Research and Practice.

The mainstay of evidence development in medicine is the parallel-group randomized controlled trial (RCT), which generates estimates of treatment efficacy or effectiveness for the average person in the trial. In contrast, personalized trials (sometimes referred to as 'single-person trials' or 'N-of-1 trials') assess the comparative effectiveness of two or more treatments in a single individual. These single-subject, randomized crossover trials have been used in a scattershot fashion in medicine for over 40 years but have not been widely adopted. An important barrier is the paucity of strong evidence that personalized trials improve outcomes. However, the principal impediment may have less to do with proof of efficacy than with practical aspects of design and implementation. These include decisions about treatment regimen flexibility, blinding, and washout periods as well as organizational, clinician, and patient-level challenges. After reviewing the essential elements of personalized trials, this article addresses these speed bumps and fundamentally asks, 'Why have personalized trials not been more widely adopted, and how can they be made more readily deployable and useful?' The article concludes by suggesting ways in which emerging technologies and approaches promise to overcome existing barriers and open promising vistas for the next generation of personalized-trial researchers and practitioners.

Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care.

The term 'data science' usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is of, by, and for an individual, the Per-DS investigator, acting simultaneously as her own investigator, study participant, and beneficiary, and making personalized decisions for study design and implementation. The scope of Per-DS studies may include systematic monitoring of physiological or behavioral patterns, case-crossover studies for symptom triggers, pre-post trials for exposure-outcome relationships, and personalized (N-of-1) trials for effectiveness. Per-DS studies produce personal knowledge generalizable to the individual's future self (thus benefiting herself) rather than knowledge generalizable to an external population (thus benefiting others). This endeavor requires a pivot from data mining or extraction to data gardening, analogous to home gardeners producing food for home consumption-the Per-DS investigator needs to 'cultivate the field' by setting goals, specifying study design, identifying necessary data elements, and assembling instruments and tools for data collection. Then, she can implement the study protocol, harvest her personal data, and mine the data to extract personal knowledge. To facilitate Per-DS studies, Per-DS investigators need support from community-based, scientific, philanthropic, business, and government entities, to develop and deploy resources such as peer forums, mobile apps, 'virtual field guides,' and scientific and regulatory guidance.

Using a multistakeholder collaboratory and patient surveys to inform the conduct of personalized (N-of-1) trials.

OBJECTIVE: Personalized trials have the potential to improve the precision of treatment selection and foster patient involvement in clinical decision making. Little is known about the attitudes of patients with multimorbidities. To address this, stakeholders designed and conducted a national survey that determined general attitudes and features of personalized trials that may increase their use among patients with multimorbidities in clinical and research practice. METHOD: A multistakeholder collaboratory of patients, clinicians, scientists, methodologists, statisticians, and research disseminators designed a survey to determine the conditions, symptoms, and design attributes most applicable to personalized trials according to patients. A sample of U.S. patients with two or more prespecified personalized-trial-amenable chronic conditions completed the online survey. RESULTS: Multimorbid participants (N = 501; M age = 56.1 years) showed that some conditions, symptoms or use cases for personalized trials include pain (57.6%), hypertension (38.8%), diabetes (28.8%), sleep problems (27.4%), and depression (23.0%). Overall, 82.0% of the participants with multimorbidities were interested in participating in personalized trials. The percentage that were interested varied by trial attributes, including physician involvement (86.4%), patient-driven treatment selection (88.0%), clinician blinding (59.2%), placebo treatment options (57.5%), and out-of-pocket costs (41.8%). CONCLUSION: Participants with multimorbidities identified prevalent use cases that are suited to personalized trials. Participants also identified design features of such trials, including patient-driven treatment selection, active comparators, and nonblinding. This study demonstrates that eliciting input from a collaboratory and patients with multimorbidities can inform research priorities for this rapidly growing patient population and increase adoption by researchers and clinicians alike. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey.

OBJECTIVE: To describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences. DESIGN: Each participant was presented with 18 conjoint questions via an online survey. Each question provided two choices of Personalised Trials that were defined by up to eight attributes, including treatment types, clinician involvement, study logistics and trial burden on a patient. SETTING: Online survey of adults with at least two common chronic conditions in the USA. PARTICIPANTS: A nationally representative sample of 501 individuals were recruited from the Chronic Illness Panel by Harris Poll Online. Participants were recruited from several sources, including emails, social media and telephone recruitment of the target population. MAIN OUTCOME MEASURES: The choice of Personalised Trial design that the participant preferred with each conjoint question. RESULTS: There was large variability in participants' preferences for the design of Personalised Trials. On average, they preferred certain attributes, such as a short time commitment and no cost. Notably, a population-level analysis correctly predicted 62% of the conjoint responses. An empirical Bayesian analysis of the conjoint data, which supported the estimation of individual-level preferences, improved the accuracy to 86%. Based on estimates of individual-level preferences, patients with chronic pain preferred a long study duration (p≤0.001). Asthma patients were less averse to participation burden in terms of data-collection frequency than patients with other conditions (p=0.002). Patients with hypertension were more cost-sensitive (p<0.001). CONCLUSION: These analyses provide a framework for elucidating individual-level preferences when implementing novel patient-centred interventions. The data showed that patient preference in Personalised Trials is highly variable, suggesting that individual differences must be accounted for when marketing Personalised Trials. These results have implications for advancing precise interventions in Personalised Trials by indicating when rigorous scientific principles, such as frequent monitoring, is feasible in a substantial subset of patients.

Effect of Mobile Device-Assisted N-of-1 Trial Participation on Analgesic Prescribing for Chronic Pain: Randomized Controlled Trial.

OBJECTIVES: Opioids and non-steroidal anti-inflammatory drugs (NSAIDs) are frequently prescribed for chronic musculoskeletal pain, despite limited evidence of effectiveness and well-documented adverse effects. We assessed the effects of participating in a structured, personalized self-experiment ("N-of-1 trial") on analgesic prescribing in patients with chronic musculoskeletal pain. METHODS: We randomized 215 patients with chronic pain to participate in an N-of-1 trial facilitated by a mobile health app or to receive usual care. Medical records of participating patients were reviewed at enrollment and 6 months later to assess analgesic prescribing. We established thresholds of ≥ 50, ≥ 20, and > 0 morphine milligram equivalents (MMEs) per day to capture patients taking relatively high doses only, patients taking low-moderate as well as relatively high doses, and patients taking any dose of opioids, respectively. RESULTS: There was no significant difference between the N-of-1 and control groups in the percentage of patients prescribed any opioids (relative odds ratio (ROR) = 1.05; 95% confidence interval [CI] = 0.61 to 1.80, p = 0.87). There was a clinically substantial but statistically not significant reduction of the percentage of patients receiving ≥ 20 MME (ROR = 0.58; 95% CI = 0.33 to 1.04, p = 0.07) and also in the percentage receiving ≥ 50 MME (ROR = 0.50; 95% CI = 0.19 to 1.34, p = 0.17). There was a significant reduction in the proportion of patients in the N-of-1 group prescribed NSAIDs compared with control (relative odds ratio = 0.53; 95% CI = 0.29 to 0.96, p = 0.04), with no concomitant increase in average pain intensity. There was no significant change in use of adjunctive medications (acetaminophen, gabapentenoids, or topicals). DISCUSSION: These exploratory results suggest that participation in N-of-1 trials may reduce long-term use of NSAIDs; there is also a weak signal for an effect on use of opioids. Additional research is needed to confirm these results and elucidate possible mechanisms. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02116621.

Effectiveness of a Disability Preventive Intervention for Minority and Immigrant Elders: The Positive Minds-Strong Bodies Randomized Clinical Trial.

OBJECTIVE: To test the acceptability and effectiveness of a disability prevention intervention, Positive Minds-Strong Bodies (PMSB), offered by paraprofessionals to mostly immigrant elders in four languages. DESIGN: Randomized trial of 307 participants, equally randomized into intervention or enhanced usual care. SETTING: Community-based organizations in Massachusetts, New York, Florida, and Puerto Rico serving minority elders. Data collected at baseline, 2, 6, and 12 months, between May 2015 and March 2019. PARTICIPANTS: English-, Spanish-, Mandarin-, or Cantonese-speaking adults, age 60+, not seeking disability prevention services, but eligible per elevated mood symptoms and minor to moderate physical dysfunction. INTERVENTIONS: Ten individual sessions of cognitive behavioral therapy (PM) concurrently offered with 36 group sessions of strengthening exercise training (SB) over 6 months compared to enhanced usual care. MEASUREMENTS: Acceptability defined as satisfaction and attendance to >50% of sessions. Effectiveness determined by changes in mood symptoms (HSCL-25 and GAD-7), functional performance (SPPB), self-reported disability (LLFDI), and disability days (WHODAS 2.0). RESULTS: Around 77.6% of intervention participants attended over half of PM Sessions; 53.4% attended over half of SB sessions. Intent-to-treat analyses at 6 months showed significant intervention effects: improved functioning per SPPB and LLFDI, and lowered mood symptoms per HSCL-25. Intent-to-treat analyses at 12 months showed that effects remained significant for LLFDI and HSCL-25, and disability days (per WHODAS 2.0) significantly decreased 6-month after the intervention. CONCLUSIONS: PMSB offered by paraprofessionals in community-based organizations demonstrates good acceptability and seems to improve functioning, with a compliance-benefit effect showing compliance as an important determinant of the intervention response.

Effect of Mobile Device-Supported Single-Patient Multi-crossover Trials on Treatment of Chronic Musculoskeletal Pain: A Randomized Clinical Trial.

Importance: Individually designed single-patient multi-crossover (n-of-1) trials can facilitate tailoring of treatments directed at various conditions, including chronic musculoskeletal pain (CMSP) but are potentially burdensome, which may limit uptake in research and practice. Objectives: To determine whether patients randomized to participate in an n-of-1 trial supported by a mobile health (mHealth) app would experience less pain and improved global health, adherence, satisfaction, and shared decision making compared with patients assigned to usual care. Design, Setting, and Participants: This randomized clinical trial compared participation in an individualized, mHealth-supported n-of-1 trial vs usual care. The participating 215 patients had CMSP for at least 6 weeks, had a smartphone or tablet with a data plan, were enrolled in northern California from July 2014 through July 2016, and were followed for up to 1 year by 48 clinicians in academic, community, Veterans Affairs, and military settings. Interventions: Intervention patients met with their clinicians and used a desktop interface to select treatments and trial parameters for an n-of-1 trial comparing 2 pain-management regimens. The mHealth app provided reminders to take designated treatments on assigned days and to upload responses to daily questions on pain and treatment-associated adverse effects. Control patients received care as usual. Main Outcomes and Measures: The primary outcome was change in the PROMIS (Patient-Reported Outcomes Measurement Information System) pain-related interference 8-item short-form scale (full scale range, 41-78) from baseline to 6 months. Secondary outcomes included patient-reported pain intensity, overall health, analgesic adherence, trust in clinician, satisfaction with care, medication-related shared decision making, and, for the n-of-1 group only, participant engagement and experience. Results: Among 215 patients (108 randomized to the n-of-1 intervention and 107 to control), 102 (47%) were women, and the mean (SD) age was 55.5 (11.1) years. At the 6-month follow-up, pain interference was reduced in both groups, though there was no difference between the intervention and control groups (-1.36 points; 95% CI, -2.91 to 0.19 points; P = .09). There were no advantages in secondary outcomes for intervention patients vs control patients except for higher medication-related shared decision making at 6 months (between-group difference, 11.9 points; 95% CI, 2.6-21.2 points; P = .01). Among patients assigned to the n-of-1 group, 88% (n = 86) affirmed that the mHealth app could help people like them manage their pain. Conclusions and Relevance: In this population of patients with CMSP, mHealth-supported n-of-1 trials were feasible and associated with a satisfactory user experience, but n-of-1 trial participation did not significantly improve pain interference at 6 months vs usual care. Trial Registration: ClinicalTrials.gov identifier: NCT02116621.

Patient preferences for personalized (N-of-1) trials: a conjoint analysis.

OBJECTIVE: Despite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials. STUDY DESIGN AND SETTING: We recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model. RESULTS: Overall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P < 0.001) and with less vs. more time commitment/day (0.16 [0.07], P < 0.015) but did not hold preferences for the other six attributes. In subgroup analyses, participants ≥65 years, white, and with income ≤$50,000 were more averse to costs than their counterparts (P all <0.05). CONCLUSION: To optimize dissemination, Personalized Trial designers should seek to minimize out-of-pocket costs and time burden of self-monitoring. They should also consider adaptive designs that can accommodate subgroup differences in design preferences.

An Overview of Research and Evaluation Designs for Dissemination and Implementation.

The wide variety of dissemination and implementation designs now being used to evaluate and improve health systems and outcomes warrants review of the scope, features, and limitations of these designs. This article is one product of a design workgroup that was formed in 2013 by the National Institutes of Health to address dissemination and implementation research, and whose members represented diverse methodologic backgrounds, content focus areas, and health sectors. These experts integrated their collective knowledge on dissemination and implementation designs with searches of published evaluations strategies. This article emphasizes randomized and nonrandomized designs for the traditional translational research continuum or pipeline, which builds on existing efficacy and effectiveness trials to examine how one or more evidence-based clinical/prevention interventions are adopted, scaled up, and sustained in community or service delivery systems. We also mention other designs, including hybrid designs that combine effectiveness and implementation research, quality improvement designs for local knowledge, and designs that use simulation modeling.

Effectiveness of Collaborative Care for Depression in Public-Sector Primary Care Clinics Serving Latinos.

OBJECTIVE: Quality improvement interventions for depression care have been shown to be effective for improving quality of care and depression outcomes in settings with primarily insured patients. The aim of this study was to determine the impact of a collaborative care intervention for depression that was tailored for low-income Latino patients seen in public-sector clinics. METHODS: A total of 400 depressed patients from three public-sector primary care clinics were enrolled in a randomized controlled trial of a tailored collaborative care intervention versus enhanced usual care. Social workers without previous mental health experience served as depression care specialists for the intervention patients (N=196). Depending on patient preference, they delivered a cognitive-behavioral therapy (CBT) intervention or facilitated antidepressant medication given by primary care providers or both. In enhanced usual care, patients (N=204) received a pamphlet about depression, a letter for their primary care provider stating that they had a positive depression screen, and a list of local mental health resources. Intent-to-treat analyses examined clinical and process-of-care outcomes at 16 weeks. RESULTS: Compared with patients in the enhanced usual care group, patients in the intervention group had significantly improved depression, quality of life, and satisfaction outcomes (p<.001 for all). Intervention patients also had significantly improved quality-of-care indicators, including the proportion of patients receiving either psychotherapy or antidepressant medication (77% versus 21%, p<.001). CONCLUSIONS: Collaborative care for depression can greatly improve care and outcomes in public-sector clinics. Social workers without prior mental health experience can effectively provide CBT and manage depression care.

Measuring Maladaptive Cognitions in Complicated Grief: Introducing the Typical Beliefs Questionnaire.

OBJECTIVES: Maladaptive cognitions related to loss are thought to contribute to development of complicated grief and are crucial to address in treatment, but tools available to assess them are limited. This paper introduces the Typical Beliefs Questionnaire (TBQ), a 25-item self-report instrument to assess cognitions that interfere with adaptation to loss. DESIGN: Study participants completed an assessment battery during their initial evaluation and again after completing treatment at 20 weeks. Test-retest reliability was assessed on a subsample of the participants who did not show change in complicated grief severity after the first 4 weeks of treatment. To examine latent structure of the TBQ, an exploratory factor analysis (EFA) was performed. SETTING: Academic medical centers in Boston, New York, Pittsburgh, and San Diego from 2010-2014. PARTICIPANTS: 394 bereaved adults who met criteria for complicated grief. MEASUREMENTS: The TBQ along with assessments of complicated grief symptoms and related avoidance, depression symptoms, functional impairment, and perceived social support. RESULTS: The TBQ exhibited good internal consistency (α = 0.82) and test-retest reliability (N = 105; intraclass correlation coefficient = 0.74). EFA indicated a five-factor structure: "Protesting the Death," "Negative Thoughts About the World," "Needing the Person," "Less Grief is Wrong" and "Grieving Too Much." The total score and all factors showed sensitivity to change with treatment. CONCLUSIONS: This new tool allows a clinician to quickly and reliably ascertain presence of specific maladaptive cognitions related to complicated grief, and subsequently, to use the information to aid a diagnostic assessment, to structure the treatment, and to measure treatment outcomes.

No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals.

BACKGROUND: When subgroup analyses are not correctly analyzed and reported, incorrect conclusions may be drawn, and inappropriate treatments provided. Despite the increased recognition of the importance of subgroup analysis, little information exists regarding the prevalence, appropriateness, and study characteristics that influence subgroup analysis. The objective of this study is to determine (1) if the use of subgroup analyses and multivariable risk indices has increased, (2) whether statistical methodology has improved over time, and (3) which study characteristics predict subgroup analysis. METHODS: We randomly selected randomized controlled trials (RCTs) from five high-impact general medical journals during three time periods. Data from these articles were abstracted in duplicate using standard forms and a standard protocol. Subgroup analysis was defined as reporting any subgroup effect. Appropriate methods for subgroup analysis included a formal test for heterogeneity or interaction across treatment-by-covariate groups. We used logistic regression to determine the variables significantly associated with any subgroup analysis or, among RCTs reporting subgroup analyses, using appropriate methodology. RESULTS: The final sample of 416 articles reported 437 RCTs, of which 270 (62 %) reported subgroup analysis. Among these, 185 (69 %) used appropriate methods to conduct such analyses. Subgroup analysis was reported in 62, 55, and 67 % of the articles from 2007, 2010, and 2013, respectively. The percentage using appropriate methods decreased over the three time points from 77 % in 2007 to 63 % in 2013 (p < 0.05). Significant predictors of reporting subgroup analysis included industry funding (OR 1.94 (95 % CI 1.17, 3.21)), sample size (OR 1.98 per quintile (1.64, 2.40), and a significant primary outcome (OR 0.55 (0.33, 0.92)). The use of appropriate methods to conduct subgroup analysis decreased by year (OR 0.88 (0.76, 1.00)) and was less common with industry funding (OR 0.35 (0.18, 0.70)). Only 33 (18 %) of the RCTs examined subgroup effects using a multivariable risk index. CONCLUSIONS: While we found no significant increase in the reporting of subgroup analysis over time, our results show a significant decrease in the reporting of subgroup analyses using appropriate methods during recent years. Industry-sponsored trials may more commonly report subgroup analyses, but without utilizing appropriate methods. Suboptimal reporting of subgroup effects may impact optimal physician-patient decision-making.

Optimizing Treatment of Complicated Grief: A Randomized Clinical Trial.

IMPORTANCE: To our knowledge, this is the first placebo-controlled randomized clinical trial to evaluate the efficacy of antidepressant pharmacotherapy, with and without complicated grief psychotherapy, in the treatment of complicated grief. OBJECTIVE: To confirm the efficacy of a targeted complicated grief treatment (CGT), determine whether citalopram (CIT) enhances CGT outcome, and examine CIT efficacy without CGT. DESIGN, SETTING, AND PARTICIPANTS: Included in the study were 395 bereaved adults who met criteria for CG recruited from March 2010 to September 2014 from academic medical centers in Boston, Massachusetts; New York, New York; Pittsburgh, Pennsylvania; and San Diego, California. Co-occurring substance abuse, psychosis, mania, and cognitive impairment were exclusionary. Study participants were randomized using site-specific permuted blocks stratified by major depression into groups prescribed CIT (n = 101), placebo (PLA; n = 99), CGT with CIT (n = 99), and CGT with PLA (n = 96). Independent evaluators conducted monthly assessments for 20 weeks. Response rates were compared under the intention-to-treat principle, including all randomized participants in a logistic regression with inverse probability weighting. INTERVENTIONS: All participants received protocolized pharmacotherapy optimized by flexible dosing, psychoeducation, grief monitoring, and encouragement to engage in activities. Half were also randomized to receive manualized CGT in 16 concurrent weekly sessions. MAIN OUTCOMES AND MEASURES: Complicated grief-anchored Clinical Global Impression scale measurments every 4 weeks. Response was measured as a rating of "much improved" or "very much improved." RESULTS: Of the 395 study participants, 308 (78.0%) were female and 325 (82.3%) were white. Participants' response to CGT with PLA vs PLA (82.5% vs 54.8%; relative risk [RR], 1.51; 95% CI, 1.16-1.95; P = .002; number needed to treat [NNT], 3.6) suggested the efficacy of CGT, and the addition of CIT did not significantly improve CGT outcome (CGT with CIT vs CGT with PLA: 83.7% vs 82.5%; RR, 1.01; 95% CI, 0.88-1.17; P = .84; NNT, 84). However, depressive symptoms decreased significantly more when CIT was added to treatment (CGT with CIT vs CGT with PLA: model-based adjusted mean [standard error] difference, -2.06 [1.00]; 95% CI, -4.02 to -0.11; P = .04). By contrast, adding CGT improved CIT outcome (CIT vs CGT with CIT: 69.3% vs 83.7%; RR, 1.21; 95% CI, 1.00-1.46; P = .05; NNT, 6.9). Last, participant response to CIT was not significantly different from PLA at week 12 (45.9% vs 37.9%; RR, 1.21; 95% CI, 0.82-1.81; P = .35; NNT, 12.4) or at week 20 (69.3% vs 54.8%; RR, 1.26; 95% CI, 0.95-1.68; P = .11; NNT, 6.9). Rates of suicidal ideation diminished to a substantially greater extent among participants receiving CGT than among those who did not. CONCLUSIONS AND RELEVANCE: Complicated grief treatment is the treatment of choice for CG, and the addition of CIT optimizes the treatment of co-occurring depressive symptoms. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01179568.

Reporting quality of N-of-1 trials published between 1985 and 2013: a systematic review.

OBJECTIVES: To evaluate the quality of reporting of single-patient (N-of-1) trials published in the medical literature based on the CONSORT Extension for N-of-1 Trials (CENT) statement and to examine factors that influence reporting quality in these trials. STUDY DESIGN AND SETTING: Through a search of 10 electronic databases, we identified N-of-1 trials in clinical medicine published between January 1, 1985, and December 31, 2013. Two reviewers screened articles for eligibility and independently extracted data. Quality assessment was performed using the CENT statement. Discrepancies were resolved by consensus. RESULTS: We identified 112 eligible N-of-1 trials published in 87 journals and involving a total of 2,278 patients. Overall, kappa agreement between the two evaluators for compliance with CENT criteria was 0.80 (95% confidence interval: 0.79, 0.82). Trials assessed pharmacology and therapeutics (87%), behavior (11%), or diagnosis (2%). Although 87% of articles described the trial design (including the planned number of subjects and length of treatment period), the median percentage of specific CENT elements reported in the Methods was 41% (range, 16-87%), and the median percentage in the Results was 38% (range, 32-93%). First authors were predominantly from North America (46%), Europe (29%), and Australia (17%). Quality of reporting was higher in articles published in journals with relatively high-impact factors (P = 0.004). CONCLUSION: The quality of reporting of published N-of-1 trials is variable and in need of improvement. Because the CENT guidelines were not published until near the end of the period of this review, these results represent a baseline from which improvement may be expected in the future.

Trials of Intervention Principles: Evaluation Methods for Evolving Behavioral Intervention Technologies.

In recent years, there has been increasing discussion of the limitations of traditional randomized controlled trial (RCT) methodologies for the evaluation of eHealth and mHealth interventions, and in particular, the requirement that these interventions be locked down during evaluation. Locking down these interventions locks in defects and eliminates the opportunities for quality improvement and adaptation to the changing technological environment, often leading to validation of tools that are outdated by the time that trial results are published. Furthermore, because behavioral intervention technologies change frequently during real-world deployment, even if a tested intervention were deployed in the real world, its shelf life would be limited. We argue that RCTs will have greater scientific and public health value if they focus on the evaluation of intervention principles (rather than a specific locked-down version of the intervention), allowing for ongoing quality improvement modifications to the behavioral intervention technology based on the core intervention principles, while continuously improving the functionality and maintaining technological currency. This paper is an initial proposal of a framework and methodology for the conduct of trials of intervention principles (TIPs) aimed at minimizing the risks of in-trial changes to intervention technologies and maximizing the potential for knowledge acquisition. The focus on evaluation of intervention principles using clinical and usage outcomes has the potential to provide more generalizable and durable information than trials focused on a single intervention technology.

THE STRUCTURED CLINICAL INTERVIEW FOR COMPLICATED GRIEF: RELIABILITY, VALIDITY, AND EXPLORATORY FACTOR ANALYSIS.

BACKGROUND: Complicated grief (CG) has been recently included in the DSM-5, under the term "persistent complex bereavement disorder," as a condition requiring further study. To our knowledge, no psychometric data on any structured clinical interview for CG (SCI-CG) is available to date. In this manuscript, we introduce the SCI-CG, a 31-item "SCID-like" clinician-administered instrument to assess the presence of CG symptoms. METHODS: Participants were 281 treatment-seeking adults with CG (77.9% [n = 219] women, mean age = 52.4, standard deviation [SD] = 17.8) who were assessed with the SCI-CG and measures of depression, posttraumatic stress, anxiety, functional impairment. RESULTS: The SCI-CG exhibited satisfactory internal consistency (α = .78), good test-retest reliability (interclass correlation [ICC] 0.68, 95% CI [0.60-0.75]), and excellent interrater reliability (ICC = 0.95, 95% CI [0.89-0.98]). Exploratory factor analyses revealed that a five-factor structure, explaining 50.3% of the total variance, was the best fit for the data. CONCLUSIONS: The clinician-rated SCI-CG demonstrates good internal consistency, reliability, and convergent validity in treatment-seeking individuals with CG and therefore can be a useful tool to assess CG. Although diagnostic criteria for CG have yet to be adequately validated, the SCI-CG may facilitate this process. The SCI-CG can now be used as a validated instrument in research and clinical practice.