Tobacco smoking clusters in households affected by tuberculosis in an individual participant data meta-analysis of national tuberculosis prevalence surveys: Time for household-wide interventions?

Tuberculosis (TB) and non-communicable diseases (NCD) share predisposing risk factors. TB-associated NCD might cluster within households affected with TB requiring shared prevention and care strategies. We conducted an individual participant data meta-analysis of national TB prevalence surveys to determine whether NCD cluster in members of households with TB. We identified eligible surveys that reported at least one NCD or NCD risk factor through the archive maintained by the World Health Organization and searching in Medline and Embase from 1 January 2000 to 10 August 2021, which was updated on 23 March 2023. We compared the prevalence of NCD and their risk factors between people who do not have TB living in households with at least one person with TB (members of households with TB), and members of households without TB. We included 16 surveys (n = 740,815) from Asia and Africa. In a multivariable model adjusted for age and gender, the odds of smoking was higher among members of households with TB (adjusted odds ratio (aOR) 1.23; 95% CI: 1.11-1.38), compared with members of households without TB. The analysis did not find a significant difference in the prevalence of alcohol drinking, diabetes, hypertension, or BMI between members of households with and without TB. Studies evaluating household-wide interventions for smoking to reduce its dual impact on TB and NCD may be warranted. Systematically screening for NCD using objective diagnostic methods is needed to understand the actual burden of NCD and inform comprehensive interventions.

How to check a simulation study.

Simulation studies are powerful tools in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings in which answers are already known. They should be coded in stages, with data-generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates surprisingly powerful tools. Failed estimation and outlying estimates should be identified and dealt with by changing data-generating mechanisms or coding realistic hybrid analysis procedures. Finally, we give a series of ideas that have been useful to us in the past for checking unexpected results. Following our advice may help to prevent errors and to improve the quality of published simulation studies.

Treatment for radiographically active, sputum culture-negative pulmonary tuberculosis: A systematic review and meta-analysis.

BACKGROUND: People with radiographic evidence for pulmonary tuberculosis (TB), but negative sputum cultures, have increased risk of developing culture-positive TB. Recent expansion of X-ray screening is leading to increased identification of this group. We set out to synthesise the evidence for treatment to prevent progression to culture-positive disease. METHODS: We conducted a systematic review and meta-analysis. We searched for prospective trials evaluating the efficacy of TB regimens against placebo, observation, or alternative regimens, for the treatment of adults and children with radiographic evidence of TB but culture-negative respiratory samples. Databases were searched up to 18 Oct 2022. Study quality was assessed using ROB 2·0 and ROBINS-I. The primary outcome was progression to culture-positive TB. Meta-analysis with a random effects model was conducted to estimate pooled efficacy. This study was registered with PROSPERO (CRD42021248486). FINDINGS: We included 13 trials (32,568 individuals) conducted between 1955 and 2018. Radiographic and bacteriological criteria for inclusion varied. 19·1% to 57·9% of participants with active x-ray changes and no treatment progressed to culture-positive disease. Progression was reduced with any treatment (6 studies, risk ratio [RR] 0·27, 95%CI 0·13-0·56), although multi-drug TB treatment (RR 0·11, 95%CI 0·05-0·23) was significantly more effective than isoniazid treatment (RR 0·63, 95%CI 0·35-1·13) (p = 0·0002). INTERPRETATION: Multi-drug regimens were associated with significantly reduced risk of progression to TB disease for individuals with radiographically apparent, but culture-negative TB. However, most studies were old, conducted prior to the HIV epidemic and with outdated regimens. New clinical trials are required to identify the optimal treatment approach.

What is the optimal duration, dose and frequency for anti-PD1 therapy of non-small cell lung cancer?

Over the past decade, immune checkpoint inhibitors (ICIs) have transformed the management of multiple malignancies including lung cancer. However, the optimal use of these agents in terms of duration, dose and administration frequency remains unknown. Focusing on anti-PD1 agents nivolumab and pembrolizumab in the context of non-small cell lung cancer, we argue that several lines of evidence suggest current administration regimens of these drugs may result in overtreatment with potentially important implications for cost, quality of life and toxicity. This review summarizes evidence for the scope to optimize anti-PD1 regimens, the limitations of existing data and potential approaches to solve these problems including with a novel multi-arm clinical trial design implemented in the recently opened REFINE-Lung study.

Association of diabetes, smoking, and alcohol use with subclinical-to-symptomatic spectrum of tuberculosis in 16 countries: an individual participant data meta-analysis of national tuberculosis prevalence surveys.

Background: Non-communicable diseases (NCDs) and NCD risk factors, such as smoking, increase the risk for tuberculosis (TB). Data are scarce on the risk of prevalent TB associated with these factors in the context of population-wide systematic screening and on the association between NCDs and NCD risk factors with different manifestations of TB, where ∼50% being asymptomatic but bacteriologically positive (subclinical). We did an individual participant data (IPD) meta-analysis of national and sub-national TB prevalence surveys to synthesise the evidence on the risk of symptomatic and subclinical TB in people with NCDs or risk factors, which could help countries to plan screening activities. Methods: In this systematic review and IPD meta-analysis, we identified eligible prevalence surveys in low-income and middle-income countries that reported at least one NCD (e.g., diabetes) or NCD risk factor (e.g., smoking, alcohol use) through the archive maintained by the World Health Organization and by searching in Medline and Embase from January 1, 2000 to August 10, 2021. The search was updated on March 23, 2023. We performed a one-stage meta-analysis using multivariable multinomial models. We estimated the proportion of and the odds ratio for subclinical and symptomatic TB compared to people without TB for current smoking, alcohol use, and self-reported diabetes, adjusted for age and gender. Subclinical TB was defined as microbiologically confirmed TB without symptoms of current cough, fever, night sweats, or weight loss and symptomatic TB with at least one of these symptoms. We assessed heterogeneity using forest plots and I2 statistic. Missing variables were imputed through multi-level multiple imputation. This study is registered with PROSPERO (CRD42021272679). Findings: We obtained IPD from 16 national surveys out of 21 national and five sub-national surveys identified (five in Asia and 11 in Africa, N = 740,815). Across surveys, 15.1%-56.7% of TB were subclinical (median: 38.1%). In the multivariable model, current smoking was associated with both subclinical (OR 1.67, 95% CI 1.27-2.40) and symptomatic TB (OR 1.49, 95% CI 1.34-1.66). Self-reported diabetes was associated with symptomatic TB (OR 1.67, 95% CI 1.17-2.40) but not with subclinical TB (OR 0.92, 95% CI 0.55-1.55). For alcohol drinking ≥ twice per week vs no alcohol drinking, the estimates were imprecise (OR 1.59, 95% CI 0.70-3.62) for subclinical TB and OR 1.43, 95% CI 0.59-3.46 for symptomatic TB). For the association between current smoking and symptomatic TB, I2 was high (76.5% (95% CI 62.0-85.4), while the direction of the point estimates was consistent except for three surveys with wide CIs. Interpretation: Our findings suggest that current smokers are more likely to have both symptomatic and subclinical TB. These individuals can, therefore, be prioritised for intensified screening, such as the use of chest X-ray in the context of community-based screening. People with self-reported diabetes are also more likely to have symptomatic TB, but the association is unclear for subclinical TB. Funding: None.

The Smooth Away From Expected (SAFE) non-inferiority frontier: theory and implementation with an application to the D3 trial.

BACKGROUND: In a non-inferiority trial, the choice of margin depends on the expected control event risk. If the true risk differs from expected, power and interpretability of results can be affected. A non-inferiority frontier pre-specifies an appropriate non-inferiority margin for each value of control event risk. D3 is a non-inferiority trial comparing two treatment regimens in children living with HIV, designed assuming a control event risk of 12%, a non-inferiority margin of 10%, 80% power and a significance level (α) of 0.025. We consider approaches to choosing and implementing a frontier for this already funded trial, where changing the sample size substantially would be difficult. METHODS: In D3, we fix the non-inferiority margin at 10%, 8% and 5% for control event risks of ≥9%, 5% and 1%, respectively. We propose four frontiers which fit these fixed points, including a Smooth Away From Expected (SAFE) frontier. Analysis approaches considered are as follows: using the pre-specified significance level (α=0.025); always using a reduced significance level (to achieve α≤0.025 across control event risks); reducing significance levels only when the control event risk differs significantly from expected (control event risk <9%); and using a likelihood ratio test. We compare power and type 1 error for SAFE with other frontiers. RESULTS: Changing the significance level only when the control event risk is <9% achieves approximately nominal (<3%) type I error rate and maintains reasonable power for control event risks between 1 and 15%. The likelihood ratio test method performs similarly, but the results are more complex to present. Other analysis methods lead to either inflated type 1 error or badly reduced power. The SAFE frontier gives more interpretable results with low control event risks than other frontiers (i.e. it uses more reasonable non-inferiority margins). Other frontiers do not achieve power close (i.e. within 1%) to SAFE across the range of likely control event risks while controlling type I error. CONCLUSIONS: The SAFE non-inferiority frontier will be used in D3, and the non-inferiority margin and significance level will be modified if the control event risk is lower than expected. This ensures results will remain interpretable if design assumptions are incorrect, while achieving similar power. A similar approach could be considered for other non-inferiority trials where the control event risk is uncertain.

Factors Associated With Circulating Sex Hormones in Men : Individual Participant Data Meta-analyses.

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 -0.99 nmol/L {-51.6 to -28.5 ng/dL}]), or diabetes (-1.43 nmol/L [-41.2 ng/dL] [CI, -1.65 to -1.22 nmol/L {-47.6 to -35.2 ng/dL}]). Sex hormone-binding globulin was directly associated with age and inversely associated with BMI. Luteinizing hormone was directly associated with age in men older than 70 years. LIMITATION: Cross-sectional analysis, heterogeneity between studies and in timing of blood sampling, and imputation for missing data. CONCLUSION: Multiple factors are associated with variation in male testosterone, SHBG, and LH concentrations. Reduced testosterone and increased LH concentrations may indicate impaired testicular function after age 70 years. Interpretation of individual testosterone measurements should account particularly for age older than 70 years, obesity, diabetes, and cancer. PRIMARY FUNDING SOURCE: Medical Research Future Fund, Government of Western Australia, and Lawley Pharmaceuticals. (PROSPERO: CRD42019139668).

A comparison of different population-level summary measures for randomised trials with time-to-event outcomes, with a focus on non-inferiority trials.

BACKGROUND: The population-level summary measure is a key component of the estimand for clinical trials with time-to-event outcomes. This is particularly the case for non-inferiority trials, because different summary measures imply different null hypotheses. Most trials are designed using the hazard ratio as summary measure, but recent studies suggested that the difference in restricted mean survival time might be more powerful, at least in certain situations. In a recent letter, we conjectured that differences between summary measures can be explained using the concept of the non-inferiority frontier and that for a fair simulation comparison of summary measures, the same analysis methods, making the same assumptions, should be used to estimate different summary measures. The aim of this article is to make such a comparison between three commonly used summary measures: hazard ratio, difference in restricted mean survival time and difference in survival at a fixed time point. In addition, we aim to investigate the impact of using an analysis method that assumes proportional hazards on the operating characteristics of a trial designed with any of the three summary measures. METHODS: We conduct a simulation study in the proportional hazards setting. We estimate difference in restricted mean survival time and difference in survival non-parametrically, without assuming proportional hazards. We also estimate all three measures parametrically, using flexible survival regression, under the proportional hazards assumption. RESULTS: Comparing the hazard ratio assuming proportional hazards with the other summary measures not assuming proportional hazards, relative performance varies substantially depending on the specific scenario. Fixing the summary measure, assuming proportional hazards always leads to substantial power gains compared to using non-parametric methods. Fixing the modelling approach to flexible parametric regression assuming proportional hazards, difference in restricted mean survival time is most often the most powerful summary measure among those considered. CONCLUSION: When the hazards are likely to be approximately proportional, reflecting this in the analysis can lead to large gains in power for difference in restricted mean survival time and difference in survival. The choice of summary measure for a non-inferiority trial with time-to-event outcomes should be made on clinical grounds; when any of the three summary measures discussed here is equally justifiable, difference in restricted mean survival time is most often associated with the most powerful test, on the condition that it is estimated under proportional hazards.

REFINE-Lung implements a novel multi-arm randomised trial design to address possible immunotherapy overtreatment.

Increasing evidence suggests that some immunotherapy dosing regimens for patients with advanced cancer could result in overtreatment. Given the high costs of these agents, and important implications for quality of life and toxicity, new approaches are needed to identify and reduce unnecessary treatment. Conventional two-arm non-inferiority designs are inefficient in this context because they require large numbers of patients to explore a single alternative to the standard of care. Here, we discuss the potential problem of overtreatment with anti-PD-1 directed agents in general and introduce REFINE-Lung (NCT05085028), a UK multicentre phase 3 study of reduced frequency pembrolizumab in advanced non-small-cell lung cancer. REFINE-Lung uses a novel multi-arm multi-stage response over continuous interventions (MAMS-ROCI) design to determine the optimal dose frequency of pembrolizumab. Along with a similarly designed basket study of patients with renal cancer and melanoma, REFINE-Lung and the MAMS-ROCI design could contribute to practice-changing advances in patient care and form a template for future immunotherapy optimisation studies across cancer types and indications. This new trial design is applicable to many new or existing agents for which optimisation of dose, frequency, or duration of therapy is desirable.

Practical approaches to Bayesian sample size determination in non-inferiority trials with binary outcomes.

Bayesian analysis of a non-inferiority trial is advantageous in allowing direct probability statements to be made about the relative treatment difference rather than relying on an arbitrary and often poorly justified non-inferiority margin. When the primary analysis will be Bayesian, a Bayesian approach to sample size determination will often be appropriate for consistency with the analysis. We demonstrate three Bayesian approaches to choosing sample size for non-inferiority trials with binary outcomes and review their advantages and disadvantages. First, we present a predictive power approach for determining sample size using the probability that the trial will produce a convincing result in the final analysis. Next, we determine sample size by considering the expected posterior probability of non-inferiority in the trial. Finally, we demonstrate a precision-based approach. We apply these methods to a non-inferiority trial in antiretroviral therapy for treatment of HIV-infected children. A predictive power approach would be most accessible in practical settings, because it is analogous to the standard frequentist approach. Sample sizes are larger than with frequentist calculations unless an informative analysis prior is specified, because appropriate allowance is made for uncertainty in the assumed design parameters, ignored in frequentist calculations. An expected posterior probability approach will lead to a smaller sample size and is appropriate when the focus is on estimating posterior probability rather than on testing. A precision-based approach would be useful when sample size is restricted by limits on recruitment or costs, but it would be difficult to decide on sample size using this approach alone.

Predictive performance of interferon-gamma release assays and the tuberculin skin test for incident tuberculosis: an individual participant data meta-analysis.

Background: Evidence on the comparative performance of purified protein derivative tuberculin skin tests (TST) and interferon-gamma release assays (IGRA) for predicting incident active tuberculosis (TB) remains conflicting. We conducted an individual participant data meta-analysis to directly compare the predictive performance for incident TB disease between TST and IGRA to inform policy. Methods: We searched Medline and Embase from 1 January 2002 to 4 September 2020, and studies that were included in previous systematic reviews. We included prospective longitudinal studies in which participants received both TST and IGRA and estimated performance as hazard ratios (HR) for the development of all diagnoses of TB in participants with dichotomised positive test results compared to negative results, using different thresholds of positivity for TST. Secondary analyses included an evaluation of the impact of background TB incidence. We also estimated the sensitivity and specificity for predicting TB. We explored heterogeneity through pre-defined sub-group analyses (e.g. country-level TB incidence). Publication bias was assessed using funnel plots and Egger's test. This review is registered with PROSPERO, CRD42020205667. Findings: We obtained data from 13 studies out of 40 that were considered eligible (N = 32,034 participants: 36% from countries with TB incidence rate ≥100 per 100,000 population). All reported data on TST and QuantiFERON Gold in-Tube (QFT-GIT). The point estimate for the TST was highest with higher cut-offs for positivity and particularly when stratified by bacillus Calmette-Guérin vaccine (BCG) status (15 mm if BCG vaccinated and 5 mm if not [TST5/15 mm]) at 2.88 (95% CI 1.69-4.90). The pooled HR for QFT-GIT was higher than for TST at 4.15 (95% CI 1.97-8.75). The difference was large in countries with TB incidence rate <100 per 100,000 population (HR 10.38, 95% CI 4.17-25.87 for QFT-GIT VS. HR 5.36, 95% CI 3.82-7.51 for TST5/15 mm) but much of this difference was driven by a single study (HR 5.13, 95% CI 3.58-7.35 for TST5/15 mm VS. 7.18, 95% CI 4.48-11.51 for QFT-GIT, when excluding the study, in which all 19 TB cases had positive QFT-GIT results). The comparative performance was similar in the higher burden countries (HR 1.61, 95% CI 1.23-2.10 for QFT-GIT VS. HR 1.72, 95% CI 0.98-3.01 for TST5/15 mm). The predictive performance of both tests was higher in countries with TB incidence rate <100 per 100,000 population. In the lower TB incidence countries, the specificity of TST (76% for TST5/15 mm) and QFT-GIT (74%) for predicting active TB approached the minimum World Health Organization target (≥75%), but the sensitivity was below the target of ≥75% (63% for TST5/15 mm and 65% for QFT-GIT). The absolute differences in positive and negative predictive values between TST15 mm and QFT-GIT were small (positive predictive values 2.74% VS. 2.46%; negative predictive values 99.42% VS. 99.52% in low-incidence countries). Egger's test did not show evidence of publication bias (0.74 for TST15 mm and p = 0.68 for QFT-GIT). Interpretation: IGRA appears to have higher predictive performance than the TST in low TB incidence countries, but the difference was driven by a single study. Any advantage in clinical performance may be small, given the numerically similar positive and negative predictive values. Both IGRA and TST had lower performance in countries with high TB incidence. Test choice should be contextual and made considering operational and likely clinical impact of test results. Funding: YH, IA, and MXR were supported by the National Institute for Health and Care Research (NIHR), United Kingdom (RP-PG-0217-20009). MQ was supported by the Medical Research Council [MC_UU_00004/07].

REFINE (REduced Frequency ImmuNE checkpoint inhibition in cancers): A multi-arm phase II basket trial testing reduced intensity immunotherapy across different cancers.

BACKGROUND: Immune checkpoint inhibitors (ICI) have revolutionised treating advanced cancers. ICI are administered intravenously every 2-6 weeks for up to 2 years, until cancer progression/unacceptable toxicity. Physiological efficacy is observed at lower doses than those used as standard of care (SOC). Pharmacodynamic studies indicate sustained target occupancy, despite a pharmacological half-life of 2-3 weeks. Reducing frequency of administration may be possible without compromising outcomes. The REFINE trial aims to limit individual patient exposure to ICI whilst maintaining efficacy, with potential benefits in quality of life and reduced drug treatment/attendance costs. METHODS/DESIGN: REFINE is a randomised phase II, multi-arm, multi-stage (MAMS) adaptive basket trial investigating extended interval administration of ICIs. Eligible patients are those responding to conventionally dosed ICI at 12 weeks. In stage I, patients (n = 160 per tumour-specific cohort) will be randomly allocated (1:1) to receive maintenance ICI at SOC vs extended dose interval. REFINE is currently recruiting UK patients with locally advanced or metastatic renal cell carcinoma (RCC) who have tolerated and responded to initial nivolumab/ipilimumab, randomised to receive maintenance nivolumab SOC (480 mg 4 weekly) vs extended interval (480 mg 8 weekly). Additional tumour cohorts are planned. Subject to satisfactory outcomes (progression-free survival) stage II will investigate up to 5 different treatment intervals. Secondary outcome measures include overall survival, quality-of-life, treatment-related toxicity, mean incremental pathway costs and quality-adjusted life-years per patient. REFINE is funded by the Jon Moulton Charity Trust and Medical Research Council, sponsored by University College London (UCL), and coordinated by the MRC CTU at UCL. Trial Registration ISRCTN79455488. NCT04913025 EUDRACT #: 2021-002060-47. CTA 31330/0008/001-0001; MREC approval: 21/LO/0593. REFINE Protocol version 4.0.

Substantive model compatible multilevel multiple imputation: A joint modeling approach.

BACKGROUND: Substantive model compatible multiple imputation (SMC-MI) is a relatively novel imputation method that is particularly useful when the analyst's model includes interactions, non-linearities, and/or partially observed random slope variables. METHODS: Here we thoroughly investigate a SMC-MI strategy based on joint modeling of the covariates of the analysis model. We provide code to apply the proposed strategy and we perform an extensive simulation work to test it in various circumstances. We explore the impact on the results of various factors, including whether the missing data are at the individual or cluster level, whether there are non-linearities and whether the imputation model is correctly specified. Finally, we apply the imputation methods to the motivating example data. RESULTS: SMC-JM appears to be superior to standard JM imputation, particularly in presence of large variation in random slopes, non-linearities, and interactions. Results seem to be robust to slight mis-specification of the imputation model for the covariates. When imputing level 2 data, enough clusters have to be observed in order to obtain unbiased estimates of the level 2 parameters. CONCLUSIONS: SMC-JM is preferable to standard JM imputation in presence of complexities in the analysis model of interest, such as non-linearities or random slopes.

Handling missing data in a composite outcome with partially observed components: simulation study based on clustered paediatric routine data.

Composite scores are useful in providing insights and trends about complex and multidimensional quality of care processes. However, missing data in subcomponents may hinder the overall reliability of a composite measure. In this study, strategies for handling missing data in Paediatric Admission Quality of Care (PAQC) score, an ordinal composite outcome, were explored through a simulation study. Specifically, the implications of the conventional method employed in addressing missing PAQC score subcomponents, consisting of scoring missing PAQC score components with a zero, and a multiple imputation (MI)-based strategy, were assessed. The latent normal joint modelling MI approach was used for the latter. Across simulation scenarios, MI of missing PAQC score elements at item level produced minimally biased estimates compared to the conventional method. Moreover, regression coefficients were more prone to bias compared to standards errors. Magnitude of bias was dependent on the proportion of missingness and the missing data generating mechanism. Therefore, incomplete composite outcome subcomponents should be handled carefully to alleviate potential for biased estimates and misleading inferences. Further research on other strategies of imputing at the component and composite outcome level and imputing compatibly with the substantive model in this setting, is needed.

Growth and CD4 patterns of adolescents living with perinatally acquired HIV worldwide, a CIPHER cohort collaboration analysis.

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 sex and region, while CD4 patterns were similar, with an observed CD4 decline that needs further investigation. Early diagnosis and timely initiation of treatment in early childhood to prevent growth retardation and immunodeficiency are critical to improving APH growth and CD4 outcomes by the time they reach adulthood.

Choosing and changing the analysis scale in non-inferiority trials with a binary outcome.

BACKGROUND: The size of the margin strongly influences the required sample size in non-inferiority and equivalence trials. What is sometimes ignored, however, is that for trials with binary outcomes, the scale of the margin - risk difference, risk ratio or odds ratio - also has a large impact on power and thus on sample size requirement. When considering several scales at the design stage of a trial, these sample size consequences should be taken into account. Sometimes, changing the scale may be needed at a later stage of a trial, for example, when the event proportion in the control arm turns out different from expected. Also after completion of a trial, a switch to another scale is sometimes made, for example, when using a regression model in a secondary analysis or when combining study results in a meta-analysis that requires unifying scales. The exact consequences of such switches are currently unknown. METHODS AND RESULTS: This article first outlines sample size consequences for different choices of analysis scale at the design stage of a trial. We add a new result on sample size requirement comparing the risk difference scale with the risk ratio scale. Then, we study two different approaches to changing the analysis scale after the trial has commenced: (1) mapping the original non-inferiority margin using the event proportion in the control arm that was anticipated at the design stage or (2) mapping the original non-inferiority margin using the observed event proportion in the control arm. We use simulations to illustrate consequences on type I and type II error rates. Methods are illustrated on the INES trial, a non-inferiority trial that compared single birth rates in subfertile couples after different fertility treatments. Our results demonstrate large differences in required sample size when choosing between risk difference, risk ratio and odds ratio scales at the design stage of non-inferiority trials. In some cases, the sample size requirement is twice as large on one scale compared with another. Changing the scale after commencing the trial using anticipated proportions mainly impacts type II error rate, whereas switching using observed proportions is not advised due to not maintaining type I error rate. Differences were more pronounced with larger margins. CONCLUSIONS: Trialists should be aware that the analysis scale can have large impact on type I and type II error rates in non-inferiority trials.

Excess Mortality by Multimorbidity, Socioeconomic, and Healthcare Factors, amongst Patients Diagnosed with Diffuse Large B-Cell or Follicular Lymphoma in England.

(1) Background: Socioeconomic inequalities of survival in patients with lymphoma persist, which may be explained by patients' comorbidities. We aimed to assess the association between comorbidities and the survival of patients diagnosed with diffuse large B-cell (DLBCL) or follicular lymphoma (FL) in England accounting for other socio-demographic characteristics. (2) Methods: Population-based cancer registry data were linked to Hospital Episode Statistics. We used a flexible multilevel excess hazard model to estimate excess mortality and net survival by patient's comorbidity status, adjusted for sociodemographic, economic, and healthcare factors, and accounting for the patient's area of residence. We used the latent normal joint modelling multiple imputation approach for missing data. (3) Results: Overall, 15,516 and 29,898 patients were diagnosed with FL and DLBCL in England between 2005 and 2013, respectively. Amongst DLBCL and FL patients, respectively, those in the most deprived areas showed 1.22 (95% confidence interval (CI): 1.18-1.27) and 1.45 (95% CI: 1.30-1.62) times higher excess mortality hazard compared to those in the least deprived areas, adjusted for comorbidity status, age at diagnosis, sex, ethnicity, and route to diagnosis. (4) Conclusions: Deprivation is consistently associated with poorer survival among patients diagnosed with DLBCL or FL, after adjusting for co/multimorbidities. Comorbidities and multimorbidities need to be considered when planning public health interventions targeting haematological malignancies in England.

Investigating the inequalities in route to diagnosis amongst patients with diffuse large B-cell or follicular lymphoma in England.

INTRODUCTION: Diagnostic delay is associated with lower chances of cancer survival. Underlying comorbidities are known to affect the timely diagnosis of cancer. Diffuse large B-cell (DLBCL) and follicular lymphomas (FL) are primarily diagnosed amongst older patients, who are more likely to have comorbidities. Characteristics of clinical commissioning groups (CCG) are also known to impact diagnostic delay. We assess the association between comorbidities and diagnostic delay amongst patients with DLBCL or FL in England during 2005-2013. METHODS: Multivariable generalised linear mixed-effect models were used to assess the main association. Empirical Bayes estimates of the random effects were used to explore between-cluster variation. The latent normal joint modelling multiple imputation approach was used to account for partially observed variables. RESULTS: We included 30,078 and 15,551 patients diagnosed with DLBCL or FL, respectively. Amongst patients from the same CCG, having multimorbidity was strongly associated with the emergency route to diagnosis (DLBCL: odds ratio 1.56, CI 1.40-1.73; FL: odds ratio 1.80, CI 1.45-2.23). Amongst DLBCL patients, the diagnostic delay was possibly correlated with CCGs that had higher population densities. CONCLUSIONS: Underlying comorbidity is associated with diagnostic delay amongst patients with DLBCL or FL. Results suggest a possible correlation between CCGs with higher population densities and diagnostic delay of aggressive lymphomas.