Confidence distributions for treatment effects in clinical trials: Posteriors without priors.

An attractive feature of using a Bayesian analysis for a clinical trial is that knowledge and uncertainty about the treatment effect is summarized in a posterior probability distribution. Researchers often find probability statements about treatment effects highly intuitive and the fact that this is not accommodated in frequentist inference is a disadvantage. At the same time, the requirement to specify a prior distribution in order to obtain a posterior distribution is sometimes an artificial process that may introduce subjectivity or complexity into the analysis. This paper considers a compromise involving confidence distributions, which are probability distributions that summarize uncertainty about the treatment effect without the need for a prior distribution and in a way that is fully compatible with frequentist inference. The concept of a confidence distribution provides a posterior-like probability distribution that is distinct from, but exists in tandem with, the relative frequency interpretation of probability used in frequentist inference. Although they have been discussed for decades, confidence distributions are not well known among clinical trial statisticians and the goal of this paper is to discuss their use in analyzing treatment effects from randomized trials. As well as providing an introduction to confidence distributions, some illustrative examples relevant to clinical trials are presented, along with various case studies based on real clinical trials. It is recommended that trial statisticians consider presenting confidence distributions for treatment effects when reporting analyses of clinical trials.

Validity and Efficiency of Progression-Free Survival-2 as a Surrogate End Point for Overall Survival in Advanced Cancer Randomized Trials.

PURPOSE: Progression-free survival (PFS)-2, defined as the time from randomization to progression on second-line therapy, is potentially a more reliable surrogate than PFS for overall survival (OS), but will require longer follow-up and a larger sample size. We sought to compare the validity and efficiency, defined as proportional increase in follow-up time and sample size, of PFS-2 to PFS. METHODS: We performed an electronic search to identify randomized trials of advanced solid tumors reporting PFS, PFS-2, and OS as prespecified end points. Only studies that had protocols that defined measurement of PFS-2 and follow-up for patients after first disease progression were included. We compared correlations in the relative treatment effect for OS with PFS and PFS-2. We reconstructed individual patient data from survival curves to estimate time to statistical significance (TSS) of the relative treatment effect. We further computed the sample size (person-year [PY] follow-up) required to reach statistical significance. RESULTS: Across the 42 analysis units and 21,255 patients, the correlation of the relative treatment effect between OS and PFS-2, r, was 0.70 (95% CI, 0.41 to 0.80) and r = 0.46 (95% CI, 0.26 to 0.74) for OS and PFS. The median differences in TSS between OS with PFS, OS with PFS-2, and PFS with PFS-2 were 16.59 (95% CI, 4.48 to not reached [NR]), 10.0 (95% CI, 2.2 to NR), and 4.31 (95% CI, 2.92 to 13.13) months, respectively. The median difference in PYs required to reach statistical significance for PFS-2 over PFS was 156 (95% CI, 82 to 500) PYs, equivalent to an estimated median 12.7% increase in PYs. CONCLUSION: PFS-2 offers improved correlation with OS than PFS with a modest increase in follow-up time and sample size. PFS-2 should be considered as a primary end point in future trials of advanced cancers.

A randomized trial of expedited intra-arrest transfer versus more extended on-scene resuscitation for refractory out of hospital cardiac arrest: Rationale and design of the EVIDENCE trial.

BACKGROUND: Refractory Out of Hospital Cardiac Arrest (r-OHCA) is common and the benefit versus harm of intra-arrest transport of patients to hospital is not clear. OBJECTIVE: To assess the rate of survival to hospital discharge in adult patients with r-OHCA, initial rhythm pulseless ventricular tachycardia (VT)/ventricular fibrillation (VF) or Pulseless Electrical Activity (PEA) treated with 1 of 2 locally accepted standards of care:1 expedited transport from scene; or2 ongoing advanced life support (ALS) resuscitation on-scene. HYPOTHESIS: We hypothesize that expedited transport from scene in r-OHCA improves survival with favorable neurological status/outcome. METHODS/DESIGN: Phase III, multi-center, partially blinded, prospective, intention-to-treat, safety and efficacy clinical trial with contemporaneous registry of patient ineligible for the clinical trial. Eligible patients for inclusion are adults with witnessed r-OHCA; estimated age 18 to 70, assumed medical cause with immediate bystander cardiopulmonary resuscitation (CPR); initial rhythm of VF/pulseless VT, or PEA; no return of spontaneous circulation following 3 shocks and/or 15 minutes of professional on-scene resuscitation; with mechanical CPR available. Two hundred patients will be randomized in a 1:1 ratio to either expedited transport from scene or ongoing ALS at the scene of cardiac arrest. SETTING: Two urban regions in NSW Australia. OUTCOMES: Primary: survival to hospital discharge with cerebral performance category (CPC) 1 or 2. Secondary: safety, survival, prognostic factors, use of ECMO supported CPR and functional assessment at hospital discharge and 4 weeks and 6 months, quality of life, healthcare use and cost-effectiveness. CONCLUSIONS: The EVIDENCE trial will determine the potential risks and benefits of an expedited transport from scene of cardiac arrest.

Transparent reporting of adaptive clinical trials using concurrently randomised cohorts.

Adaptive clinical trials have designs that evolve over time because of changes to treatments or changes to the chance that participants will receive these treatments. These changes might introduce confounding that biases crude comparisons of the treatment arms and makes the results from standard reporting methods difficult to interpret for adaptive trials. To deal with this shortcoming, a reporting framework for adaptive trials was developed based on concurrently randomised cohort reporting. A concurrently randomised cohort is a subgroup of participants who all had the same treatments available and the same chance of receiving these treatments. The reporting of pre-randomisation characteristics and post-randomisation outcomes for each concurrently randomised cohort in the study is recommended. This approach provides a transparent and unbiased display of the degree of baseline balance and the randomised treatment comparisons for adaptive trials. The key concepts, terminology, and recommendations underlying concurrently randomised cohort reporting are presented, and its routine use in adaptive trial reporting is advocated.

To guide future practice, perinatal trials should be much larger, simpler and less fragile with close to 100% ascertainment of mortality and other key outcomes.

The Australian Placental Transfusion Study (APTS) randomised 1,634 fetuses to delayed (≥60 s) versus immediate (≤10 s) clamping of the umbilical cord. Systematic reviews with meta-analyses, including this and similar trials, show that delaying clamping in preterm infants reduces mortality and need for blood transfusions. Amongst 1,531 infants in APTS followed up at two years, aiming to delay clamping for 60 s or more reduced the relative risk of the primary composite outcome of death or disability by 17% (p = 0.01). However, this result is fragile because nominal statistical significance (p < 0.05) would be abolished by only 2 patients switching from a non-event to an event, and the primary composite outcome was missing in 112 patients (7%). To achieve more robust evidence, any future trials should emulate the large, simple trials co-ordinated from Oxford which reliably identified moderate, incremental improvements in mortality in tens of thousands of participants, with <1% missing data. Those who fund, regulate, and conduct trials that aim to change practice should repay the trust of those who consent to participate by doing everything possible to minimise missing data for key outcomes.

Mediation analysis of the testosterone treatment effect to prevent type 2 diabetes in the Testosterone for Prevention of Type 2 Diabetes Mellitus trial.

OBJECTIVE: To determine if testosterone treatment effect on glycaemia is mediated through changes in total fat mass, abdominal fat mass, skeletal muscle mass, non-dominant hand-grip, oestradiol (E2), and sex hormone-binding globulin (SHBG). DESIGN: Mediation analysis of a randomised placebo-controlled trial of testosterone. METHODS: Six Australian tertiary care centres recruited 1007 males, aged 50-74 years, with waist circumference ≥95 cm, serum total testosterone ≤14 nmol/L (immunoassay), and either impaired glucose tolerance or newly diagnosed type 2 diabetes on an oral glucose tolerance test (OGTT). Participants were enrolled in a lifestyle programme and randomised 1:1 to 3 monthly injections of 1000 mg testosterone undecanoate or placebo for 2 years. Complete data were available for 709 participants (70%). Mediation analyses for the primary outcomes of type 2 diabetes at 2 years (OGTT ≥ 11.1 mmol/L and change in 2-h glucose from baseline), incorporating potential mediators: changes in fat mass, % abdominal fat, skeletal muscle mass, non-dominant hand-grip strength, E2, and SHBG, were performed. RESULTS: For type 2 diabetes at 2 years, the unadjusted OR for treatment was 0.53 (95% CI:.35-.79), which became 0.48 (95% CI:.30-.76) after adjustment for covariates. Including potential mediators attenuated the treatment effect (OR 0.77; 95% CI:.44-1.35; direct effect) with 65% mediated. Only fat mass remained prognostic in the full model (OR: 1.23; 95% CI: 1.09-1.39; P < .001). CONCLUSION: At least part of the testosterone treatment effect was found to be mediated by changes in fat mass, abdominal fat, skeletal muscle mass, grip strength, SHBG, and E2, but predominantly by changes in fat mass.

Linear mixed models for investigating effect modification in subgroup meta-analysis.

Subgroup meta-analysis can be used for comparing treatment effects between subgroups using information from multiple trials. If the effect of treatment is differential depending on subgroup, the results could enable personalization of the treatment. We propose using linear mixed models for estimating treatment effect modification in aggregate data meta-analysis. The linear mixed models capture existing subgroup meta-analysis methods while allowing for additional features such as flexibility in modeling heterogeneity, handling studies with missing subgroups and more. Reviews and simulation studies of the best suited models for estimating possible differential effect of treatment depending on subgroups have been studied mostly within individual participant data meta-analysis. While individual participant data meta-analysis in general is recommended over aggregate data meta-analysis, conducting an aggregate data subgroup meta-analysis could be valuable for exploring treatment effect modifiers before committing to an individual participant data subgroup meta-analysis. Additionally, using solely individual participant data for subgroup meta-analysis requires collecting sufficient individual participant data which may not always be possible. In this article, we compared existing methods with linear mixed models for aggregate data subgroup meta-analysis under a broad selection of scenarios using simulation and two case studies. Both the case studies and simulation studies presented here demonstrate the advantages of the linear mixed model approach in aggregate data subgroup meta-analysis.

Importance of sequential methods in meta-analysis: implications for postoperative mortality, delirium, and stroke management.

Trial sequential analysis is an adaptation of frequentist sequential methods that can be used to improve inferences from meta-analysis. Trial sequential analysis can help preserve type I and type II error rates at desired levels for analyses conducted before the required information size. Through three case studies recently published in the British Journal of Anaesthesia, we show how trial sequential analysis can inform the interpretation of meta-analyses. Limitations of trial sequential analysis, which also include those of the meta-analysis to which it is applied, must be carefully considered alongside its benefits.

A Randomized Trial of Nafamostat for Covid-19.

BACKGROUND: Nafamostat mesylate is a potent in vitro antiviral agent that inhibits the host transmembrane protease serine 2 enzyme used by severe acute respiratory syndrome coronavirus 2 for cell entry. METHODS: This open-label, pragmatic, randomized clinical trial in Australia, New Zealand, and Nepal included noncritically ill hospitalized patients with coronavirus disease 2019 (Covid-19). Participants were randomly assigned to usual care or usual care plus nafamostat. The primary end point was death (any cause) or receipt of new invasive or noninvasive ventilation or vasopressor support within 28 days after randomization. Analysis was with a Bayesian logistic model in which an adjusted odds ratio <1.0 indicates improved outcomes with nafamostat. Enrollment was closed due to falling numbers of eligible patients. RESULTS: We screened 647 patients in 21 hospitals (15 in Australia, 4 in New Zealand, and 2 in Nepal) and enrolled 160 participants from May 2021 to August 2022. In the intention-to-treat population, the primary end point occurred in 8 (11%) of 73 patients with usual care and 4 (5%) of 82 with nafamostat. The median adjusted odds ratio for the primary end point for nafamostat was 0.40 (95% credible interval, 0.12 to 1.34) with a posterior probability of effectiveness (adjusted odds ratio <1.0) of 93%. For usual care compared with nafamostat, hyperkalemia occurred in 1 (1%) of 67 and 7 (9%) of 78 participants, respectively, and clinically relevant bleeding occurred in 1 (1%) of 73 and 7 (8%) of 82 participants. CONCLUSIONS: Among hospitalized patients with Covid-19, there was a 93% posterior probability that nafamostat reduced the odds of death or organ support. Prespecified stopping criteria were not met, precluding definitive conclusions. Hyperkalemia and bleeding were more common with nafamostat. (Funded by ASCOT and others; ClinicalTrials.gov number, NCT04483960.)

Estimation of the treatment effect following a clinical trial that stopped early for benefit.

When a clinical trial stops early for benefit, the maximum likelihood estimate (MLE) of the treatment effect may be subject to overestimation bias. Several authors have proposed adjusting for this bias using the conditional MLE, which is obtained by conditioning on early stopping. However, this approach has a fundamental problem in that the adjusted estimate may not be in the direction of benefit, even though the study has stopped early due to benefit. In this paper, we address this problem by embedding both the MLE and the conditional MLE within a broader class of penalised likelihood estimates, and choosing a member of the class that is a favourable compromise between the two. This penalised MLE, and its associated confidence interval, always lie in the direction of benefit when the study stops early for benefit. We study its properties using both simulations and analyses of the ENZAMET trial in metastatic prostate cancer. Conditional on stopping early for benefit, the method is found to have good unbiasedness and coverage properties, along with very favourable efficiency at earlier interim analyses. We recommend the penalised MLE as a supplementary analysis to a conventional primary analysis when a clinical trial stops early for benefit.

Analysis of adaptive platform trials using a network approach.

BACKGROUND: Adaptive platform trials allow randomized controlled comparisons of multiple treatments using a common infrastructure and the flexibility to adapt key design features during the study. Nonetheless, they have been criticized due to the potential for time trends in the underlying risk level of the population. Such time trends lead to confounding between design features and risk level, which may introduce bias favoring one or more treatments. This is particularly true when experimental treatments are not all randomized during the same time period as the control, leading to the potential for bias from non-concurrent controls. METHODS: Two analysis methods addressing this bias are stratification and adjustment. Stratification uses only comparisons between treatment cohorts randomized during identical time periods and does not use non-concurrent randomizations. Adjustment uses a modeled analysis including time period adjustment, allowing all data to be used, even from periods without concurrent randomization. We show that these competing approaches may be embedded in a common framework using network meta-analysis principles. We interpret the stages between adaptations in a platform trial as separate fixed design trials. This allows platform trials to be viewed as networks of direct randomized comparisons and indirect non-randomized comparisons. Network meta-analysis methodology can be re-purposed to aggregate the total information from a platform trial and to transparently decompose this total information into direct randomized evidence and indirect non-randomized evidence. This allows sensitivity to indirect information to be assessed and the two analysis methods to be clearly compared. RESULTS: Simulations of platform trials were analyzed using a network approach implemented in the netmeta package in R. The results demonstrated bias of unadjusted methods in the presence of time trends in risk level. Adjustment and stratification were both unbiased when direct evidence and indirect evidence were consistent. Network tests of inconsistency may be used to diagnose inconsistency when it exists. In an illustrative network analysis of one of the treatment comparisons from the STAMPEDE platform trial in metastatic prostate cancer, indirect comparisons using non-concurrent controls were inconsistent with the information from direct randomized comparisons. This supports the primary analysis approach of STAMPEDE, which used only direct randomized comparisons. CONCLUSION: Network meta-analysis provides a natural methodology for analyzing the network of direct and indirect treatment comparisons from a platform trial. Such analyses provide transparent separation of direct and indirect evidence, allowing assessment of the impact of non-concurrent controls. We recommend time-stratified analysis of concurrently controlled comparisons for primary analyses, with time-adjusted analyses incorporating non-concurrent controls reserved for secondary analyses. However, regardless of which methodology is used, a network analysis provides a useful supplement to the primary analysis.

Distributional regression in clinical trials: treatment effects on parameters other than the mean.

BACKGROUND: The classical linear model is widely used in the analysis of clinical trials with continuous outcomes. However, required model assumptions are frequently not met, resulting in estimates of treatment effect that can be inefficient and biased. In addition, traditional models assess treatment effect only on the mean response, and not on other aspects of the response, such as the variance. Distributional regression modelling overcomes these limitations. The purpose of this paper is to demonstrate its usefulness for the analysis of clinical trials, and superior performance to that of traditional models. METHODS: Distributional regression models are demonstrated, and contrasted with normal linear models, on data from the LIPID randomized controlled trial, which compared the effects of pravastatin with placebo in patients with coronary heart disease. Systolic blood pressure (SBP) and the biomarker midregional pro-adrenomedullin (MR-proADM) were analysed. Treatment effect was estimated in models that used response distributions more appropriate than the normal (Box-Cox-t and Johnson's Su for MR-proADM and SBP, respectively), applied censoring below the detection limit of MR-proADM, estimated treatment effect on distributional parameters other than the mean, and included random effects for longitudinal observations. A simulation study was conducted to compare the performance of distributional regression models with normal linear regression, under conditions mimicking the LIPID study. The R package gamlss (Generalized Additive Models for Location, Scale and Shape), which implements maximum likelihood estimation for distributional regression modelling, was used throughout. RESULTS: In all cases the distributional regression models fit the data well, in contrast to poor fits obtained for traditional models; for MR-proADM a small but significant treatment effect on the mean was detected by the distributional regression model and not the normal model; and for SBP a beneficial treatment effect on the variance was demonstrated. In the simulation study distributional models strongly outperformed normal models when the response variable was non-normal and heterogeneous; and there was no disadvantage introduced by the use of distributional regression modelling when the response satisfied the normal linear model assumptions. CONCLUSIONS: Distributional regression models are a rich framework, largely untapped in the clinical trials world. We have demonstrated a sample of the capabilities of these models for the analysis of trials. If interest lies in accurate estimation of treatment effect on the mean, or other distributional features such as variance, the use of distributional regression modelling will yield superior estimates to traditional normal models, and is strongly recommended. TRIAL REGISTRATION: The LIPID trial was retrospectively registered on ANZCTR on 27/04/2016, registration number ACTRN12616000535471 .

Circulating Cystatin C Is an Independent Risk Marker for Cardiovascular Outcomes, Development of Renal Impairment, and Long-Term Mortality in Patients With Stable Coronary Heart Disease: The LIPID Study.

Background Elevated plasma cystatin C levels reflect reduced renal function and increased cardiovascular risk. Less is known about whether the increased risk persists long-term or is independent of renal function and other important biomarkers. Methods and Results Cystatin C and other biomarkers were measured at baseline (in 7863 patients) and 1 year later (in 6106 patients) in participants in the LIPID (Long-Term Intervention with Pravastatin in Ischemic Disease) study, who had a previous acute coronary syndrome. Outcomes were ascertained during the study (median follow-up, 6 years) and long-term (median follow-up, 16 years). Glomerular filtration rate (GFR) was estimated using Chronic Kidney Disease Epidemiology Collaboration equations (first GFR-creatinine, then GFR-creatinine-cystatin C). Over 6 years, in fully adjusted multivariable time-to-event models, with respect to the primary end point of coronary heart disease mortality or nonfatal myocardial infarction, for comparison of Quartile 4 versus 1 of baseline cystatin C, the hazard ratio was 1.37 (95% CI, 1.07-1.74; P=0.01), and for major cardiovascular events was 1.47 (95% CI, 1.19-1.82; P<0.001). Over 16 years, the association of baseline cystatin C with coronary heart disease, cardiovascular, and all-cause mortality persisted (each P<0.001) and remained significant after adjustment for estimated GFR-creatinine-cystatin C. Cystatin C also predicted the development of chronic kidney disease for 6 years (odds ratio, 6.61; 95% CI, 4.28-10.20) independently of estimated GFR-creatinine and other risk factors. However, this association was no longer significant after adjustment for estimated GFR-creatinine-cystatin C. Conclusions Cystatin C independently predicted major cardiovascular events, development of chronic kidney disease, and cardiovascular and all-cause mortality. Prediction of long-term mortality was independent of improved estimation of GFR. Registration URL: https://anzctr.org.au; Unique identifier: ACTRN12616000535471.

PD-L1 expression as a prognostic marker in patients treated with chemotherapy for metastatic non-small-cell lung cancer.

Background: In metastatic non-small-cell lung cancer (mNSCLC), PD-L1 expression is associated with benefit from immune checkpoint inhibitor (ICI) therapy. However, the significance of PD-L1 expression in chemotherapy-treated patients is uncertain. Methods: Using the chemotherapy control arm of first-line randomized trials, a meta-analysis of the association between efficacy outcomes and PD-L1 status was performed, stratified by assay due to inter-assay differences. Results: Across 12 trials and 4378 patients, overall survival (OS) was superior in high PD-L1 versus negative tumors and low versus negative according to 22C3/28-8 assays. When classified by SP142 assay, no significant difference in response or survival was seen between PD-L1 groups. Conclusion: In mNSCLC, high PD-L1-expressing tumors are associated with longer OS and improved objective rate when treated with chemotherapy. Inter-assay variability impacts outcome results.

Biomarkers are naturally occurring cancer traits that can predict certain events. PD-L1 expression is a biomarker used in advanced lung cancer to predict benefit from immunotherapy. However, the association between PD-L1expression and chemotherapy is unclear. The authors analyzed data from 14 large clinical trials and found that PD-L1 expression could also be used to define a type of lung cancer that responds better to chemotherapy.

The validity of progression-free survival 2 as a surrogate trial end point for overall survival.

BACKGROUND: Overall survival (OS) is the gold-standard end point for oncology trials. However, the availability of multiple therapeutic options after progression and crossover to receive investigational agents confound and delay OS data maturation. Progression-free survival 2 (PFS-2), defined as the time from randomization to progression on first subsequent therapy, has been proposed as a surrogate for OS. Using a meta-analytic approach, the authors aimed to assess the association between OS and PFS-2 and compare this with progression-free survival 1 (PFS-1) and the objective response rate (ORR). METHODS: An electronic literature search was performed to identify randomized trials of systemic therapies in advanced solid tumors that reported PFS-2 as a prespecified end point. Correlations between OS and PFS-2, OS and PFS-1, and OS and ORR as hazard ratios (HRs) or odds ratios (ORs) were assessed via linear regression weighted by trial size. RESULTS: Thirty-eight trials were included, and they comprised 19,031 patients across 8 tumor types. PFS-2 displayed a moderate correlation with OS (r = 0.67; 95% confidence interval [CI], 0.08-0.69). Conversely, correlations of ORR (r = 0.12; 95% CI, 0.00-0.13) and PFS-1 (r = 0.21; 95% CI, 0.00-0.33) were poor. The findings for PFS-2 were consistent for subgroup analyses by treatment type (immunotherapy vs nonimmunotherapy: r = 0.67 vs 0.67), survival post progression (<12 vs ≥12 months: r = 0.86 vs 0.79), and percentage not receiving subsequent treatment (<50% vs ≥50%: r = 0.70 vs 0.63). CONCLUSIONS: Across diverse tumors and therapies, the treatment effect on PFS-2 correlated moderately with the treatment effect on OS. PFS-2 performed consistently better than PFS-1 and ORR, regardless of postprogression treatment and postprogression survival. PFS-2 should be included as a key trial end point in future randomized trials of solid tumors.

Unpacking the behavioural components and delivery features of early childhood obesity prevention interventions in the TOPCHILD Collaboration: a systematic review and intervention coding protocol.

INTRODUCTION: Little is known about how early (eg, commencing antenatally or in the first 12 months after birth) obesity prevention interventions seek to change behaviour and which components are or are not effective. This study aims to (1) characterise early obesity prevention interventions in terms of target behaviours, delivery features and behaviour change techniques (BCTs), (2) explore similarities and differences in BCTs used to target behaviours and (3) explore effectiveness of intervention components in preventing childhood obesity. METHODS AND ANALYSIS: Annual comprehensive systematic searches will be performed in Epub Ahead of Print/MEDLINE, Embase, Cochrane (CENTRAL), CINAHL, PsycINFO, as well as clinical trial registries. Eligible randomised controlled trials of behavioural interventions to prevent childhood obesity commencing antenatally or in the first year after birth will be invited to join the Transforming Obesity in CHILDren Collaboration. Standard ontologies will be used to code target behaviours, delivery features and BCTs in both published and unpublished intervention materials provided by trialists. Narrative syntheses will be performed to summarise intervention components and compare applied BCTs by types of target behaviours. Exploratory analyses will be undertaken to assess effectiveness of intervention components. ETHICS AND DISSEMINATION: The study has been approved by The University of Sydney Human Research Ethics Committee (project no. 2020/273) and Flinders University Social and Behavioural Research Ethics Committee (project no. HREC CIA2133-1). The study's findings will be disseminated through peer-reviewed publications, conference presentations and targeted communication with key stakeholders. PROSPERO REGISTRATION NUMBER: CRD42020177408.

Transforming Obesity Prevention for CHILDren (TOPCHILD) Collaboration: protocol for a systematic review with individual participant data meta-analysis of behavioural interventions for the prevention of early childhood obesity.

INTRODUCTION: Behavioural interventions in early life appear to show some effect in reducing childhood overweight and obesity. However, uncertainty remains regarding their overall effectiveness, and whether effectiveness differs among key subgroups. These evidence gaps have prompted an increase in very early childhood obesity prevention trials worldwide. Combining the individual participant data (IPD) from these trials will enhance statistical power to determine overall effectiveness and enable examination of individual and trial-level subgroups. We present a protocol for a systematic review with IPD meta-analysis to evaluate the effectiveness of obesity prevention interventions commencing antenatally or in the first year after birth, and to explore whether there are differential effects among key subgroups. METHODS AND ANALYSIS: Systematic searches of Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo and trial registries for all ongoing and completed randomised controlled trials evaluating behavioural interventions for the prevention of early childhood obesity have been completed up to March 2021 and will be updated annually to include additional trials. Eligible trialists will be asked to share their IPD; if unavailable, aggregate data will be used where possible. An IPD meta-analysis and a nested prospective meta-analysis will be performed using methodologies recommended by the Cochrane Collaboration. The primary outcome will be body mass index z-score at age 24±6 months using WHO Growth Standards, and effect differences will be explored among prespecified individual and trial-level subgroups. Secondary outcomes include other child weight-related measures, infant feeding, dietary intake, physical activity, sedentary behaviours, sleep, parenting measures and adverse events. ETHICS AND DISSEMINATION: Approved by The University of Sydney Human Research Ethics Committee (2020/273) and Flinders University Social and Behavioural Research Ethics Committee (HREC CIA2133-1). Results will be relevant to clinicians, child health services, researchers, policy-makers and families, and will be disseminated via publications, presentations and media releases. PROSPERO REGISTRATION NUMBER: CRD42020177408.

Pre- and on-treatment lactate dehydrogenase as a prognostic and predictive biomarker in advanced non-small cell lung cancer.

BACKGROUND: The survival outcomes of patients with advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) are variable. This study investigated whether pre- and on-treatment lactate dehydrogenase (LDH) could better prognosticate and select patients for ICI therapy. METHODS: Using data from the POPLAR and OAK trials of atezolizumab versus docetaxel in previously treated advanced NSCLC, the authors assessed the prognostic and predictive value of pretreatment LDH (less than or equal to vs greater than the upper limit of normal). They further examined changes in on-treatment LDH by performing landmark analyses and estimated overall survival (OS) distributions according to the LDH level stratified by the response category (complete response [CR]/partial response [PR] vs stable disease [SD]). They repeated pretreatment analyses in subgroups defined by the programmed death ligand 1 (PD-L1) status. RESULTS: This study included 1327 patients with available pretreatment LDH. Elevated pretreatment LDH was associated with an adverse prognosis regardless of treatment (hazard ratio [HR] for atezolizumab OS, 1.49; P = .0001; HR for docetaxel OS, 1.30; P = .004; P for treatment by LDH interaction = .28). Findings for elevated pretreatment LDH were similar for patients with positive PD-L1 expression treated with atezolizumab. Persistently elevated on-treatment LDH was associated with a 1.3- to 2.8-fold increased risk of death at weeks 6, 12, 18, and 24 regardless of treatment. Elevated LDH at 6 weeks was associated with significantly shorter OS regardless of radiological response (HR for CR/PR, 2.10; P = .04; HR for SD, 1.50; P < .01), with similar findings observed at 12 weeks. CONCLUSIONS: In previously treated advanced NSCLC, elevated pretreatment LDH is an independent adverse prognostic marker. There is no evidence that pretreatment LDH predicts ICI benefit. Persistently elevated on-treatment LDH is associated with worse OS despite radiologic response.

Incorporating circulating tumor DNA detection to radiographic assessment for treatment response in advanced EGFR-mutant lung cancer.

PURPOSE: Response Evaluation Criteria in Solid Tumors (RECIST) has limitations but remains the conventional approach for tumor assessments. We explored whether circulating tumor DNA (ctDNA) can be incorporated into RECIST to provide a more robust measure of tumor response in advanced EGFR-mutant NSCLC. PATIENTS AND METHODS: In FASTACT-2, patients with advanced NSCLC received platinum/gemcitabine intercalated with erlotinib or placebo. EGFR mutation (tumor and plasma ctDNA) was detected using cobas v2. Patients selected for this hypothesis-generating analysis had EGFR mutations (on either tumor or plasma) at baseline and evaluable week 8 plasma EGFR. Week 8 ctDNA and radiologic response status were correlated with survival using landmark cox regression analyses. RESULTS: Of the original 451 patients, 86 (19.1%) were eligible for this analysis. 73% (n = 63) had detectable ctDNA at baseline. At week 8, 40% (n = 34) had radiologic partial response (PR), 60% (n = 52) had stable disease (SD); 80% (n = 69) had a ctDNA response (undetectable ctDNA). In patients who had initial PR and undetectable ctDNA, 93% (28/30) had ongoing PR subsequently at week 16. The median duration of response was 14.9 months. In patients with SD and undetectable ctDNA at week 8, 28% had radiological PR at week 16. Amongst those with PR at week 8, survival outcomes for those with undetectable vs detectable ctDNA were not statistically significant (PFS HR 0.49, 95%CI 0.16-1.48, p = 0.21; OS HR 0.39, 95%CI 0.13-1.19, p = 0.10). Amongst those with SD at week 8, there was significantly longer survival for those with undetectable vs detectable ctDNA (PFS HR 0.27, 95% CI 0.13-0.57, p < 0.0001; OS HR 0.40, 95% CI 0.20-0.80, p = 0.009). CONCLUSION: In patients with SD, undetectable ctDNA at week 8 correlated with survival improvement. Both radiologic and ctDNA responses are prognostic of PFS. Incorporation of ctDNA with RECIST may improve tumor response assessment in EGFR-mutant NSCLC.

Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring.

This paper considers the problem of semi-parametric proportional hazards model fitting where observed survival times contain event times and also interval, left and right censoring times. Although this is not a new topic, many existing methods suffer from poor computational performance. In this paper, we adopt a more versatile penalized likelihood method to estimate the baseline hazard and the regression coefficients simultaneously. The baseline hazard is approximated using basis functions such as M-splines. A penalty is introduced to regularize the baseline hazard estimate and also to ease dependence of the estimates on the knots of the basis functions. We propose a Newton-MI (multiplicative iterative) algorithm to fit this model. We also present novel asymptotic properties of our estimates, allowing for the possibility that some parameters of the approximate baseline hazard may lie on the parameter space boundary. Comparisons of our method against other similar approaches are made through an intensive simulation study. Results demonstrate that our method is very stable and encounters virtually no numerical issues. A real data application involving melanoma recurrence is presented and an R package 'survivalMPL' implementing the method is available on R CRAN.