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Background: Increasing interest has centered on the psychotherapeutic working alliance as a means of understanding clinical change in digital mental health interventions in recent years. However, little is understood about how and to what extent a digital mental health program can have an impact on the working alliance and clinical outcomes in a blended (therapist plus digital program) cognitive behavioral therapy (bCBT) intervention for depression. Objective: This study aimed to test the difference in working alliance scores between bCBT and treatment as usual (TAU), examine the association between working alliance and depression severity scores in both arms, and test for an interaction between system usability and working alliance with regard to the association between working alliance and depression scores in bCBT at 3 -month assessments. Methods: We conducted a secondary data analysis of the E -COMPARED (European Comparative Effectiveness Research on Blended Depression Treatment versus Treatment -as -usual) trial, which compared bCBT with TAU across 9 European countries. Data were collected in primary care and specialized services between April 2015 and December 2017. Eligible participants aged 18 years or older and diagnosed with major depressive disorder were randomized to either bCBT (n=476) or TAU (n=467). bCBT consisted of 6-20 sessions of bCBT (involving face-to-face sessions with a therapist and an internet-based program). TAU consisted of usual care for depression. The main outcomes were scores of the working alliance (Working Alliance Inventory -Short Revised-Client [WAI-SR-C]) and depressive symptoms (Patient Health Questionnaire -9 [PHQ-9]) at 3 months after randomization. Other variables included system usability scores (System Usability Scale -Client [SUS -C]) at 3 months and baseline demographic information. Data from baseline and 3 -month assessments were analyzed using linear regression models that adjusted for a set of baseline variables. Result

BACKGROUND: Various factors modulate circulating testosterone in men, affecting interpretation of testosterone measurements. PURPOSE: To clarify factors associated with variations in sex hormone concentrations. DATA SOURCES: Systematic literature searches (to July 2019). STUDY SELECTION: Prospective cohort studies of community-dwelling men with total testosterone measured using mass spectrometry. DATA EXTRACTION: Individual participant data (IPD) (9 studies; n = 21 074) and aggregate data (2 studies; n = 4075). Sociodemographic, lifestyle, and health factors and concentrations of total testosterone, sex hormone-binding globulin (SHBG), luteinizing hormone (LH), dihydrotestosterone, and estradiol were extracted. DATA SYNTHESIS: Two-stage random-effects IPD meta-analyses found a nonlinear association of testosterone with age, with negligible change among men aged 17 to 70 years (change per SD increase about the midpoint, -0.27 nmol/L [-7.8 ng/dL] [CI, -0.71 to 0.18 nmol/L {-20.5 to 5.2 ng/dL}]) and decreasing testosterone levels with age for men older than 70 years (-1.55 nmol/L [-44.7 ng/dL] [CI, -2.05 to -1.06 nmol/L {-59.1 to -30.6 ng/dL}]). Testosterone was inversely associated with body mass index (BMI) (change per SD increase, -2.42 nmol/L [-69.7 ng/dL] [CI, -2.70 to -2.13 nmol/L {-77.8 to -61.4 ng/dL}]). Testosterone concentrations were lower for men who were married (mean difference, -0.57 nmol/L [-16.4 ng/dL] [CI, -0.89 to -0.26 nmol/L {-25.6 to -7.5 ng/dL}]); undertook at most 75 minutes of vigorous physical activity per week (-0.51 nmol/L [-14.7 ng/dL] [CI, -0.90 to -0.13 nmol/L {-25.9 to -3.7 ng/dL}]); were former smokers (-0.34 nmol/L [-9.8 ng/dL] [CI, -0.55 to -0.12 nmol/L {-15.9 to -3.5 ng/dL}]); or had hypertension (-0.53 nmol/L [-15.3 ng/dL] [CI, -0.82 to -0.24 nmol/L {-23.6 to -6.9 ng/dL}]), cardiovascular disease (-0.35 nmol/L [-10.1 ng/dL] [CI, -0.55 to -0.15 nmol/L {-15.9 to -4.3 ng/dL}]), cancer (-1.39 nmol/L [-40.1 ng/dL] [CI, -1.79 to

Adolescents living with HIV are subject to multiple co-morbidities, including growth retardation and immunodeficiency. We describe growth and CD4 evolution during adolescence using data from the Collaborative Initiative for Paediatric HIV Education and Research (CIPHER) global project. Data were collected between 1994 and 2015 from 11 CIPHER networks worldwide. Adolescents with perinatally acquired HIV infection (APH) who initiated antiretroviral therapy (ART) before age 10 years, with at least one height or CD4 count measurement while aged 10-17 years, were included. Growth was measured using height-for-age Z-scores (HAZ, stunting if <-2 SD, WHO growth charts). Linear mixed-effects models were used to study the evolution of each outcome between ages 10 and 17. For growth, sex-specific models with fractional polynomials were used to model non-linear relationships for age at ART initiation, HAZ at age 10 and time, defined as current age from 10 to 17 years of age. A total of 20,939 and 19,557 APH were included for the growth and CD4 analyses, respectively. Half were females, two-thirds lived in East and Southern Africa, and median age at ART initiation ranged from <3 years in North America and Europe to >7 years in sub-Saharan African regions. At age 10, stunting ranged from 6% in North America and Europe to 39% in the Asia-Pacific; 19% overall had CD4 counts <500 cells/mm 3. Across adolescence, higher HAZ was observed in females and among those in high-income countries. APH with stunting at age 10 and those with late ART initiation (after age 5) had the largest HAZ gains during adolescence, but these gains were insufficient to catch-up with non-stunted, early ART-treated adolescents. From age 10 to 16 years, mean CD4 counts declined from 768 to 607 cells/mm 3. This decline was observed across all regions, in males and females. Growth patterns during adolescence differed substantially by sex and region, while CD4 patterns were similar, with an observed CD4 decline th

Introduction: Adolescents living with HIV are subject to multiple co-morbidities, including growth retardation and immunodeficiency. We describe growth and CD4 evolution during adolescence using data from the Collaborative Initiative for Paediatric HIV Education and Research (CIPHER) global project. Methods: Data were collected between 1994 and 2015 from 11 CIPHER networks worldwide. Adolescents with perinatally acquired HIV infection (APH) who initiated antiretroviral therapy (ART) before age 10 years, with at least one height or CD4 count measurement while aged 10–17 years, were included. Growth was measured using height-for-age Z-scores (HAZ, stunting if <-2 SD, WHO growth charts). Linear mixed-effects models were used to study the evolution of each outcome between ages 10 and 17. For growth, sex-specific models with fractional polynomials were used to model non-linear relationships for age at ART initiation, HAZ at age 10 and time, defined as current age from 10 to 17 years of age. Results: A total of 20,939 and 19,557 APH were included for the growth and CD4 analyses, respectively. Half were females, two-thirds lived in East and Southern Africa, and median age at ART initiation ranged from <3 years in North America and Europe to >7 years in sub-Saharan African regions. At age 10, stunting ranged from 6% in North America and Europe to 39% in the Asia-Pacific; 19% overall had CD4 counts <500 cells/mm3. Across adolescence, higher HAZ was observed in females and among those in high-income countries. APH with stunting at age 10 and those with late ART initiation (after age 5) had the largest HAZ gains during adolescence, but these gains were insufficient to catch-up with non-stunted, early ART-treated adolescents. From age 10 to 16 years, mean CD4 counts declined from 768 to 607 cells/mm3. This decline was observed across all regions, in males and females. Conclusions: Growth patterns during adolescence differed substantially by

Background Trials to identify the minimal effective treatment duration are needed in different therapeutic areas, including bacterial infections, tuberculosis and hepatitis C. However, standard non-inferiority designs have several limitations, including arbitrariness of non-inferiority margins, choice of research arms and very large sample sizes. Methods We recast the problem of finding an appropriate non-inferior treatment duration in terms of modelling the entire duration-response curve within a pre-specified range. We propose a multi-arm randomised trial design, allocating patients to different treatment durations. We use fractional polynomials and spline-based methods to flexibly model the duration-response curve. We call this a 'Durations design'. We compare different methods in terms of a scaled version of the area between true and estimated prediction curves. We evaluate sensitivity to key design parameters, including sample size, number and position of arms. Results A total sample size of ~ 500 patients divided into a moderate number of equidistant arms (5-7) is sufficient to estimate the duration-response curve within a 5% error margin in 95% of the simulations. Fractional polynomials provide similar or better results than spline-based methods in most scenarios. Conclusion Our proposed practical randomised trial 'Durations design' shows promising performance in the estimation of the duration-response curve; subject to a pending careful investigation of its inferential properties, it provides a potential alternative to standard non-inferiority designs, avoiding many of their limitations, and yet being fairly robust to different possible duration-response curves. The trial outcome is the whole duration-response curve, which may be used by clinicians and policymakers to make informed decisions, facilitating a move away from a forced binary hypothesis testing paradigm.

The risk of tuberculosis (TB) is variable among individuals with latent Mycobacterium tuberculosis infection (LTBI), but validated estimates of personalized risk are lacking. In pooled data from 18 systematically identified cohort studies from 20 countries, including 80,468 individuals tested for LTBI, 5-year cumulative incident TB risk among people with untreated LTBI was 15.6% (95% confidence interval (CI), 8.0-29.2%) among child contacts, 4.8% (95% CI, 3.0-7.7%) among adult contacts, 5.0% (95% CI, 1.6-14.5%) among migrants and 4.8% (95% CI, 1.5-14.3%) among immunocompromised groups. We confirmed highly variable estimates within risk groups, necessitating an individualized approach to risk stratification. Therefore, we developed a personalized risk predictor for incident TB (PERISKOPE-TB) that combines a quantitative measure of T cell sensitization and clinical covariates. Internal-external cross-validation of the model demonstrated a random effects meta-analysis C-statistic of 0.88 (95% CI, 0.82-0.93) for incident TB. In decision curve analysis, the model demonstrated clinical utility for targeting preventative treatment, compared to treating all, or no, people with LTBI. We challenge the current crude approach to TB risk estimation among people with LTBI in favor of our evidence-based and patient-centered method, in settings aiming for pre-elimination worldwide.

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data.