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1.
Stat Med ; 43(6): 1271-1289, 2024 Mar 15.
Artículo en Inglés | MEDLINE | ID: mdl-38205556

RESUMEN

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.


Asunto(s)
Modelos Estadísticos , Humanos , Teorema de Bayes , Probabilidad , Incertidumbre
2.
Cancer ; 128(8): 1574-1583, 2022 04 15.
Artículo en Inglés | MEDLINE | ID: mdl-35090047

RESUMEN

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.


Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Biomarcadores , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Humanos , L-Lactato Deshidrogenasa , Neoplasias Pulmonares/tratamiento farmacológico , Pronóstico
3.
Cancer ; 128(7): 1449-1457, 2022 04 01.
Artículo en Inglés | MEDLINE | ID: mdl-34985773

RESUMEN

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.


Asunto(s)
Neoplasias , Biomarcadores , Supervivencia sin Enfermedad , Humanos , Inmunoterapia , Supervivencia sin Progresión , Modelos de Riesgos Proporcionales
4.
BMC Med Res Methodol ; 22(1): 56, 2022 02 27.
Artículo en Inglés | MEDLINE | ID: mdl-35220944

RESUMEN

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 .


Asunto(s)
Interpretación Estadística de Datos , Biomarcadores , Ensayos Clínicos como Asunto , Humanos
5.
Clin Trials ; 19(5): 479-489, 2022 10.
Artículo en Inglés | MEDLINE | ID: mdl-35993542

RESUMEN

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.


Asunto(s)
Proyectos de Investigación , Sesgo , Humanos , Masculino , Metaanálisis en Red , Ensayos Clínicos Controlados Aleatorios como Asunto
6.
BMC Med Res Methodol ; 21(1): 126, 2021 06 21.
Artículo en Inglés | MEDLINE | ID: mdl-34154563

RESUMEN

BACKGROUND: Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution. METHODS: Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions. RESULTS: It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 - 3.5) to 40.0% at age 95 years (CI: 36.6 - 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 - 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 - 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns. CONCLUSIONS: Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.


Asunto(s)
COVID-19 , Factores de Edad , Anciano , Anciano de 80 o más Años , Brotes de Enfermedades , Humanos , Persona de Mediana Edad , SARS-CoV-2 , Victoria/epidemiología
7.
J Pediatr ; 204: 301-304.e2, 2019 01.
Artículo en Inglés | MEDLINE | ID: mdl-30314661

RESUMEN

Infants in the Australian and UK Benefits of Oxygen Saturation Targeting-II trials treated using revised oximeters spent more time within their planned pulse oximeter saturation target ranges than infants treated using the original oximeters (P < .001). This may explain the larger mortality difference seen with revised oximeters. If so, average treatment effects from the Neonatal Oxygen Prospective Meta-analysis trials may be underestimates.


Asunto(s)
Mortalidad Infantil , Oximetría/métodos , Oxígeno/sangre , Australia , Calibración , Humanos , Lactante , Recién Nacido , Oximetría/instrumentación , Reino Unido
8.
Biom J ; 59(4): 636-657, 2017 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-27704593

RESUMEN

Randomized clinical trials comparing several treatments to a common control are often reported in the medical literature. For example, multiple experimental treatments may be compared with placebo, or in combination therapy trials, a combination therapy may be compared with each of its constituent monotherapies. Such trials are typically designed using a balanced approach in which equal numbers of individuals are randomized to each arm, however, this can result in an inefficient use of resources. We provide a unified framework and new theoretical results for optimal design of such single-control multiple-comparator studies. We consider variance optimal designs based on D-, A-, and E-optimality criteria, using a general model that allows for heteroscedasticity and a range of effect measures that include both continuous and binary outcomes. We demonstrate the sensitivity of these designs to the type of optimality criterion by showing that the optimal allocation ratios are systematically ordered according to the optimality criterion. Given this sensitivity to the optimality criterion, we argue that power optimality is a more suitable approach when designing clinical trials where testing is the objective. Weighted variance optimal designs are also discussed, which, like power optimal designs, allow the treatment difference to play a major role in determining allocation ratios. We illustrate our methods using two real clinical trial examples taken from the medical literature. Some recommendations on the use of optimal designs in single-control multiple-comparator trials are also provided.


Asunto(s)
Ensayos Clínicos como Asunto/métodos , Modelos Estadísticos , Proyectos de Investigación , Humanos
9.
Stat Med ; 35(18): 3166-78, 2016 08 15.
Artículo en Inglés | MEDLINE | ID: mdl-27073156

RESUMEN

Rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios. When the data are event counts observed during an exposure period, adjusted rate differences may be estimated using an identity-link Poisson generalised linear model, also known as additive Poisson regression. A problem with this approach is that the assumption of equality of mean and variance rarely holds in real data, which often show overdispersion. An additive negative binomial model is the natural alternative to account for this; however, standard model-fitting methods are often unable to cope with the constrained parameter space arising from the non-negativity restrictions of the additive model. In this paper, we propose a novel solution to this problem using a variant of the expectation-conditional maximisation-either algorithm. Our method provides a reliable way to fit an additive negative binomial regression model and also permits flexible generalisations using semi-parametric regression functions. We illustrate the method using a placebo-controlled clinical trial of fenofibrate treatment in patients with type II diabetes, where the outcome is the number of laser therapy courses administered to treat diabetic retinopathy. An R package is available that implements the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.


Asunto(s)
Bioestadística , Análisis de Regresión , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Humanos , Modelos Lineales , Modelos Estadísticos
11.
Biostatistics ; 13(1): 179-92, 2012 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-21914729

RESUMEN

Relative risks (RRs) are generally considered preferable to odds ratios in prospective studies. However, unlike logistic regression for odds ratios, the standard log-binomial model for RR regression does not respect the natural parameter constraints and is therefore often subject to numerical instability. In this paper, we develop a reliable and flexible method for fitting log-binomial models. We use an Expectation-Maximization (EM) algorithm where the multiplicative event probability is viewed as the joint probability for a collection of latent binary outcomes. This gives a simple iterative scheme that provides stable convergence to the maximum likelihood estimate. In addition to reliability, the method offers some flexible generalizations, including models with unspecified isotonic regression functions. We examine the method's performance using simulations and data analyses of the age-specific RR of mortality following heart attack. These analyses demonstrate the potential for numerical instability in RR regression and show how this can be overcome using the proposed approach. Source code to implement the method in R is provided as supplementary material available at Biostatistics online.


Asunto(s)
Análisis de Regresión , Riesgo , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Bioestadística , Humanos , Funciones de Verosimilitud , Modelos Lineales , Persona de Mediana Edad , Modelos Estadísticos , Infarto del Miocardio/tratamiento farmacológico , Infarto del Miocardio/mortalidad
12.
Stat Med ; 32(28): 4859-74, 2013 Dec 10.
Artículo en Inglés | MEDLINE | ID: mdl-23824994

RESUMEN

Clinical trials that stop early for benefit have a treatment difference that overestimates the true effect. The consequences of this fact have been extensively debated in the literature. Some researchers argue that early stopping, or truncation, is an important source of bias in treatment effect estimates, particularly when truncated studies are incorporated into meta-analyses. Such claims are bound to lead some systematic reviewers to consider excluding truncated studies from evidence synthesis. We therefore investigated the implications of this strategy by examining the properties of sequentially monitored studies conditional on reaching the final analysis. As well as estimation bias, we studied information bias measured by the difference between standard measures of statistical information, such as sample size, and the actual information based on the conditional sampling distribution. We found that excluding truncated studies leads to underestimation of treatment effects and overestimation of information. Importantly, the information bias increases with the estimation bias, meaning that greater estimation bias is accompanied by greater overweighting in a meta-analysis. Simulations of meta-analyses confirmed that the bias from excluding truncated studies can be substantial. In contrast, when meta-analyses included truncated studies, treatment effect estimates were essentially unbiased. Previous analyses comparing treatment effects in truncated and non-truncated studies are shown not to be indicative of bias in truncated studies. We conclude that early stopping of clinical trials is not a substantive source of bias in meta-analyses and recommend that all studies, both truncated and non-truncated, be included in evidence synthesis.


Asunto(s)
Sesgo , Ensayos Clínicos Controlados Aleatorios como Asunto/métodos , Proyectos de Investigación/normas , Resultado del Tratamiento , Simulación por Computador , Humanos , Ensayos Clínicos Controlados Aleatorios como Asunto/normas
13.
Stat Methods Med Res ; 32(5): 994-1009, 2023 05.
Artículo en Inglés | MEDLINE | ID: mdl-36924263

RESUMEN

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.


Asunto(s)
Modelos Lineales , Humanos , Simulación por Computador
14.
BMJ Med ; 2(1): e000497, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-37736079

RESUMEN

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.

15.
Eur J Endocrinol ; 188(7): 613-620, 2023 Jul 10.
Artículo en Inglés | MEDLINE | ID: mdl-37406250

RESUMEN

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.


Asunto(s)
Diabetes Mellitus Tipo 2 , Masculino , Humanos , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Diabetes Mellitus Tipo 2/prevención & control , Análisis de Mediación , Australia , Testosterona/uso terapéutico , Prueba de Tolerancia a la Glucosa , Globulina de Unión a Hormona Sexual/análisis
16.
NEJM Evid ; 2(11): EVIDoa2300132, 2023 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-38320527

RESUMEN

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.)


Asunto(s)
COVID-19 , Humanos , SARS-CoV-2 , Guanidinas/farmacología , Benzamidinas
17.
Stat Methods Med Res ; 31(12): 2456-2469, 2022 12.
Artículo en Inglés | MEDLINE | ID: mdl-36065593

RESUMEN

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.


Asunto(s)
Ensayos Clínicos como Asunto , Proyectos de Investigación , Sesgo , Funciones de Verosimilitud
18.
Int J Biostat ; 18(2): 553-575, 2022 11 01.
Artículo en Inglés | MEDLINE | ID: mdl-34714982

RESUMEN

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.


Asunto(s)
Algoritmos , Proyectos de Investigación , Modelos de Riesgos Proporcionales , Funciones de Verosimilitud , Simulación por Computador
19.
BMJ Open ; 12(1): e048165, 2022 Jan 20.
Artículo en Inglés | MEDLINE | ID: mdl-35058255

RESUMEN

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.


Asunto(s)
Obesidad Infantil , Terapia Conductista/métodos , Niño , Preescolar , Humanos , Obesidad Infantil/prevención & control , Revisiones Sistemáticas como Asunto
20.
BMJ Open ; 12(1): e048166, 2022 Jan 20.
Artículo en Inglés | MEDLINE | ID: mdl-35058256

RESUMEN

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.


Asunto(s)
Obesidad Infantil , Terapia Conductista , Índice de Masa Corporal , Niño , Preescolar , Ejercicio Físico , Humanos , Lactante , Metaanálisis como Asunto , Obesidad Infantil/prevención & control , Estudios Prospectivos , Revisiones Sistemáticas como Asunto
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