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Immortal time may arise in survival analyses when individuals are assigned to treatment strategies based on post-eligibility information or selected based on post-assignment eligibility criteria. Selection based on eligibility criteria applied after treatment assignment results in immortal time when the analysis starts the follow-up at assignment. Misclassification of assignment to treatment strategies based on treatment received after eligibility results in immortal time when the treatment strategies are not distinguishable at the start of follow-up. Target trial emulation prevents the introduction of immortal time by explicitly specifying eligibility and assignment to the treatment strategies, and by synchronizing them at the start of follow-up. We summarize analytic approaches that prevent immortal time when longitudinal data are available to emulate the target trial from the time of treatment assignment. The term "immortal time bias" suggests that the source of the bias is the immortal time, but it is selection or misclassification that generates the immortal time, leading to bias. [ABSTRACT FROM AUTHOR]

Hospitalized patients with COVID-19 are at high risk for thrombosis and systemic inflammation. Anticoagulation can improve outcomes, but trials have varied in the doses used. This systematic review and prospective meta-analysis includes and analyzes randomized trials registered by September 2024. The analysis compares therapeutic-, prophylactic-, and intermediate-dose anticoagulation with heparins on 28-day mortality, major bleeding, and other clinical outcomes. Background: Reported results of clinical trials assessing higher-dose anticoagulation in patients hospitalized for COVID-19 have been inconsistent. Purpose: To estimate the association of higher- versus lower-dose anticoagulation with clinical outcomes. Data Sources: Randomized trials were identified from the World Health Organization's International Clinical Trials Registry Platform and ClinicalTrials.gov with no restriction by trial status or language. Study Selection: Eligible randomized trials assigned patients hospitalized for COVID-19 to higher- versus lower-dose anticoagulation. Data Extraction: 20 eligible trials provided data in a prospectively agreed format. Two further studies were included based on published data. The primary outcome was all-cause mortality 28 days after randomization. Secondary outcomes were progression to invasive mechanical ventilation or death, thromboembolic events, and major bleeding. Data Synthesis: Therapeutic- compared with prophylactic-dose anticoagulation with heparins reduced 28-day mortality (OR, 0.77 [95% CI, 0.64 to 0.93]; I 2  = 29%; 11 trials, 6297 patients, of whom 5456 required low or no oxygen at randomization). The ORs for 28-day mortality were 1.21 (CI, 0.93 to 1.58; I 2  = 0%) for therapeutic-dose compared with intermediate-dose anticoagulation (6 trials, 1803 patients, 843 receiving noninvasive ventilation at randomization) and 0.95 (CI, 0.76 to 1.19; I 2  = 0%; 10 trials, 3897 patients, 2935 receiving no or low oxygen at randomization) for intermediate- versus prophylactic-dose anticoagulation. Treatment effects appeared broadly consistent across predefined patient subgroups, although some analyses were limited in power. Higher- compared with lower-dose anticoagulation was associated with fewer thromboembolic events, but a greater risk for major bleeding. Conclusion: Therapeutic-dose compared with prophylactic-dose anticoagulation reduced 28-day mortality. Mortality was similar for intermediate-dose compared with prophylactic-dose anticoagulation and higher for therapeutic-dose compared with intermediate-dose anticoagulation, although this comparison was not estimated precisely. Primary Funding Source: No direct funding. (PROSPERO: CRD42020213461) [ABSTRACT FROM AUTHOR]

Network meta‐analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In many areas of research, interventions are often complex, made up of multiple components or features. This makes it difficult to define a common set of interventions on which to perform the analysis. One approach to this problem is component network meta‐analysis (CNMA) which uses a meta‐regression framework to define each intervention as a subset of components whose individual effects combine additively. In this article, we are motivated by a systematic review of complex interventions to prevent obesity in children. Due to considerable heterogeneity across the trials, these interventions cannot be expressed as a subset of components but instead are coded against a framework of characteristic features. To analyse these data, we develop a bespoke CNMA‐inspired model that allows us to identify the most important features of interventions. We define a meta‐regression model with covariates on three levels: intervention, study, and follow‐up time, as well as flexible interaction terms. By specifying different regression structures for trials with and without a control arm, we relax the assumption from previous CNMA models that a control arm is the absence of intervention components. Furthermore, we derive a correlation structure that accounts for trials with multiple intervention arms and multiple follow‐up times. Although, our model was developed for the specifics of the obesity data set, it has wider applicability to any set of complex interventions that can be coded according to a set of shared features. [ABSTRACT FROM AUTHOR]

Background and aims: Chronic non‐cancer pain (CNCP) is one of the most common causes of disability globally. Opioid prescribing to treat CNCP remains widespread, despite limited evidence of long‐term clinical benefit and evidence of harm such as problematic pharmaceutical opioid use (POU) and overdose. The study aimed to measure the prevalence of POU in CNCP patients treated with opioid analgesics. Method: A comprehensive systematic literature review and meta‐analysis was undertaken using MEDLINE, Embase and PsycINFO databases from inception to 27 January 2021. We included studies from all settings with participants aged ≥ 12 with non‐cancer pain of ≥ 3 months duration, treated with opioid analgesics. We excluded case–control studies, as they cannot be used to generate prevalence estimates. POU was defined using four categories: dependence and opioid use disorder (D&OUD), signs and symptoms of D&OUD (S&S), aberrant behaviour (AB) and at risk of D&OUD. We used a random‐effects multi‐level meta‐analytical model. We evaluated inconsistency using the I2 statistic and explored heterogeneity using subgroup analyses and meta‐regressions. Results: A total of 148 studies were included with > 4.3 million participants; 1% of studies were classified as high risk of bias. The pooled prevalence was 9.3% [95% confidence interval (CI) = 5.7–14.8%; I2 = 99.9%] for D&OUD, 29.6% (95% CI = 22.1–38.3%, I2 = 99.3%) for S&S and 22% (95% CI = 17.4–27.3%, I2 = 99.8%) for AB. The prevalence of those at risk of D&OUD was 12.4% (95% CI = 4.3–30.7%, I2 = 99.6%). Prevalence was affected by study setting, study design and diagnostic tool. Due to the high heterogeneity, the findings should be interpreted with caution. Conclusions: Problematic pharmaceutical opioid use appears to be common in chronic pain patients treated with opioid analgesics, with nearly one in 10 experiencing dependence and opioid use disorder, one in three showing signs and symptoms of dependence and opioid use disorder and one in five showing aberrant behaviour. [ABSTRACT FROM AUTHOR]

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta‐analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. The first is an analytical method based on the mathematical definitions of z‐scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual‐level relationships. We verify and contrast our methods using simulation studies and trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non‐convergence. [ABSTRACT FROM AUTHOR]

Background: Previous research has suggested that statistical power is suboptimal in many biomedical disciplines, but it is unclear whether power is better in trials for particular interventions, disorders, or outcome types. We therefore performed a detailed examination of power in trials of psychotherapy, pharmacotherapy, and complementary and alternative medicine (CAM) for mood, anxiety, and psychotic disorders. Methods: We extracted data from the Cochrane Database of Systematic Reviews (Mental Health). We focused on continuous efficacy outcomes and estimated power to detect predetermined effect sizes (standardized mean difference [SMD] = 0.20–0.80, primary SMD = 0.40) and meta-analytic effect sizes (ESMA). We performed meta-regression to estimate the influence of including underpowered studies in meta-analyses. Results: We included 256 reviews with 10 686 meta-analyses and 47 384 studies. Statistical power for continuous efficacy outcomes was very low across intervention and disorder types (overall median [IQR] power for SMD = 0.40: 0.32 [0.19–0.54]; for ESMA: 0.23 [0.09–0.58]), only reaching conventionally acceptable levels (80%) for SMD = 0.80. Median power to detect the ESMA was higher in treatment-as-usual (TAU)/waitlist-controlled (0.49–0.63) or placebo-controlled (0.12–0.38) trials than in trials comparing active treatments (0.07–0.13). Adequately-powered studies produced smaller effect sizes than underpowered studies (B = −0.06, p ⩽ 0.001). Conclusions: Power to detect both predetermined and meta-analytic effect sizes in psychiatric trials was low across all interventions and disorders examined. Consistent with the presence of reporting bias, underpowered studies produced larger effect sizes than adequately-powered studies. These results emphasize the need to increase sample sizes and to reduce reporting bias against studies reporting null results to improve the reliability of the published literature. [ABSTRACT FROM AUTHOR]