Abstract
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.
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.
This analysis of 1327 patients with advanced non–small cell lung cancer from the POPLAR and OAK randomized controlled trials has found that lactate dehydrogenase (LDH) is a useful pre‐ and on‐treatment prognostic marker that can assist clinicians in counselling patients undergoing second‐ or later‐line atezolizumab or docetaxel. However, the findings fail to support the use of LDH as a predictive biomarker for immune checkpoint inhibitor therapy and reinforce the importance of rigorous validation of promising predictive biomarkers using randomized data.
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.
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 S
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.
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.
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.
The LIPID trial was retrospectively registered on ANZCTR on 27/04/2016, registration number ACTRN12616000535471 .
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.
Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysis of binary outcomes. However, Fisher scoring, which is the standard method for ...fitting GLMs in statistical software, may have difficulties in converging to the maximum likelihood estimate due to implicit parameter constraints. logbin is an R package that implements several algorithms for fitting relative risk regression models, allowing stable maximum likelihood estimation while ensuring the required parameter constraints are obeyed. We describe the logbin package and examine its stability and speed for different computational algorithms. We also describe how the package may be used to include flexible semi-parametric terms in relative risk regression models.
Back-projection is an epidemiological analysis method that was developed to estimate HIV incidence using surveillance data on AIDS diagnoses. It was used extensively during the 1990s for this purpose ...as well as in other epidemiological contexts. Surveillance data on COVID-19 diagnoses can be analysed by the method of back-projection using information about the probability distribution of the time between infection and diagnosis, which is primarily determined by the incubation period. This paper demonstrates the value of such analyses using daily diagnoses from Australia. It is shown how back-projection can be used to assess the pattern of COVID-19 infection incidence over time and to assess the impact of control measures by investigating their temporal association with changes in incidence patterns. For Australia, these analyses reveal that peak infection incidence coincided with the introduction of border closures and social distancing restrictions, while the introduction of subsequent social distancing measures coincided with a continuing decline in incidence to very low levels. These associations were not directly discernible from the daily diagnosis counts, which continued to increase after the first stage of control measures. It is estimated that a one week delay in peak incidence would have led to a fivefold increase in total infections. Furthermore, at the height of the outbreak, half to three-quarters of all infections remained undiagnosed. Automated data analytics of routinely collected surveillance data are a valuable monitoring tool for the COVID-19 pandemic and may be useful for calibrating transmission dynamics models.
Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only ...large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses. CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between -1024 and -250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images.
Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM).
The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models' poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.
•Two mathematical models for diagnosis of pulmonary thromboembolism (PTE) in dogs were generated.•D-dimer measurements are only useful as screening assay prior to a gold standard ...test.•Thromboelastography (TEG) G values were not significantly different between dogs with and without PTE.•Computed tomography pulmonary angiography (CTPA) appears to be safe and its use is supported as gold standard for diagnosis of PTE in dogs.
There is no evidence-based diagnostic approach for diagnosis of pulmonary thromboembolism (PTE) in dogs. Many dogs with diseases that predispose to thrombosis are hypercoagulable when assessed with thromboelastography (TEG), but no direct link has been established. The aims of this study were: (1) to investigate if diseased dogs with PTE, diagnosed by computed tomography pulmonary angiography (CTPA), had evidence of hypercoagulability by TEG; (2) to characterise haemostatic and inflammatory changes in dogs with PTE; (3) to construct models for prediction of PTE based on combinations of haemostatic and inflammatory variables; and (4) to evaluate the performance of D-dimer measurement for prediction of PTE. Twenty-five dogs were included in this prospective observational study (PTE: n=6; non-PTE: n=19). Clot strength G values did not differ between the PTE and non-PTE groups in tissue factor (TF) or kaolin-activated TEG analyses. Haemostatic and inflammatory variables did not differ between the two groups. Linear discriminant analysis generated a model for prediction of PTE with a sensitivity and specificity of 100% when TF results were used as TEG data, and a model with sensitivity of 83% and specificity of 100% when kaolin results were used as TEG data. Receiver operating characteristic analysis of D-dimer levels showed that a value of >0.3mg/L yielded a sensitivity of 100% and a specificity of 71.4%. In conclusion, the study supports CTPA as method for diagnosing canine PTE, but shows that TEG alone cannot identify dogs with PTE. Models for prediction of PTE were generated, but require further validation.
Abstract Background In the setting of metastatic colorectal cancer (CRC), anti-EGFR antibodies are not currently recommended for individuals with KRAS mutant tumours. This is based on subgroup ...analyses of individual clinical trials rather than a formal synthesis of evidence for KRAS status as a predictive biomarker, while newer trials report no benefit for anti-EGFR antibodies irrespective of KRAS status. This study systematically reviewed the evidence for KRAS mutation status as a treatment effect modifier of response to anti-EGFR antibodies and the influence of partner chemotherapy. Methods Medline (1966–2010), EMBASE and American and European oncology meeting abstracts were searched for randomised controlled trials reporting the influence of KRAS status on effectiveness of anti-EGFR antibodies in metastatic CRC. The treatment efficacy was summarised by KRAS status using hazard ratios (HR) for progression-free survival (PFS) and risk differences (RD) for objective response. For each study, a measure of effect modification was calculated, and aggregated using random effects meta-analysis to assess the interaction between KRAS and treatment effect. Findings Eleven studies (8924 patients) were selected from 198 reports. Two studies assessed anti-EGFR antibodies as monotherapy and nine their use with chemotherapy. KRAS status was reported in 7555 cases. In subgroup analysis, the progression HR for KRAS wild patients assigned to anti-EGFR antibodies was 0.80 (4436 patients 95%CI: 0.64, 0.99) and for mutant cases 1.11 (3119 patients, 95%CI: 0.97, 1.27). A significant treatment effect interaction between KRAS status and addition of anti-EGFR antibodies to standard treatment was found for PFS (ratio of HRs 0.71, 95%CI: 0.57, 0.90 p = 0.005) and response rate difference (difference in RDs 15%, 95%CI: 8, 22%, p < 0.001). There was no evidence that the extent of effect modification differed between chemotherapeutic partners for both PFS ( p = 0.3) and response rate ( p = 0.6). Interpretation KRAS mutation status is a treatment effect modifier for anti-EGFR antibodies in metastatic CRC. Further evidence is needed to determine whether this is true for all chemotherapy partners and all clinical circumstances.
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