scholarly journals Statistical tests for heterogeneity of clusters and composite endpoints

2021 ◽  
Author(s):  
Anthony J Webster
Keyword(s):  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Statistical Tests ◽  
Meta Analysis ◽  
Composite Endpoint ◽  
International Classification Of Diseases ◽  
Model Composite ◽  
Multi Stage ◽  
Composite Endpoints

Clinical trials and epidemiological cohort studies often group similar diseases together into a composite endpoint, to increase statistical power. A common example is to use a 3-digit code from the International Classification of Diseases (ICD), to represent a collection of several 4-digit coded diseases. More recently, data-driven studies are using associations with risk factors to cluster diseases, leading this article to reconsider the assumptions needed to study a composite endpoint of several potentially distinct diseases. An important assumption is that the (possibly multivariate) associations are the same for all diseases in a composite endpoint (not heterogeneous). Therefore, multivariate measures of heterogeneity from meta analysis are considered, including multi-variate versions of the I2 statistic and Cochran's Q statistic. Whereas meta-analysis offers tools to test heterogeneity of clustering studies, clustering models suggest an alternative heterogeneity test, of whether data are better described by one, or more, clusters of elements with the same mean. The assumptions needed to model composite endpoints with a proportional hazards model are also considered. It is found that the model can fail if one or more diseases in the composite endpoint have different associations. Tests of the proportional hazards assumption can help identify when this occurs. It is emphasised that in multi-stage diseases such as cancer, some germline genetic variants can strongly modify the baseline hazard function and cannot be adjusted for, but must instead be used to stratify the data.

Multi-Zone Proportional Hazard Model for a Multi-Stage Degradation Process

2013 ◽  
Author(s):  
Lin Li ◽  
Zeyi Sun ◽  
Xinwei Xu ◽  
Kaifu Zhang
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Monitoring Data ◽  
Status Change ◽  
Multi Stage ◽  
And Performance

Conditional-based maintenance (CBM) decision-making is of high interests in recent years due to its better performance on cost efficiency compared to other traditional policies. One of the most respected methods based on condition-monitoring data for maintenance decision-making is Proportional Hazards Model (PHM). It utilizes condition-monitoring data as covariates and identifies their effects on the lifetime of a component. Conventional modeling process of PHM only treats the degradation process as a whole lifecycle. In this paper, the PHM is advanced to describe a multi-zone degradation system considering the fact that the lifecycle of a machine can be divided into several different degradation stages. The methods to estimate reliability and performance prognostics are developed based on the proposed multi-zone PHM to predict the remaining time that the machine stays at the current stage before transferring into the next stage and the remaining useful life (RUL). The results illustrate that the multi-zone PHM effectively monitors the equipment status change and leads to a more accurate RUL prediction compared with traditional PHM.

The win ratio approach for composite endpoints: practical guidance based on previous experience

2020 ◽  
Vol 41 (46) ◽  
pp. 4391-4399 ◽  
Author(s):  
Björn Redfors ◽  
John Gregson ◽  
Aaron Crowley ◽  
Thomas McAndrew ◽  
Ori Ben-Yehuda ◽  
...  
Keyword(s):  
Statistical Power ◽  
Fatal Outcome ◽  
Composite Endpoint ◽  
Exercise Tests ◽  
Time To Death ◽  
Ratio Approach ◽  
Practical Guidance ◽  
Composite Endpoints ◽  
Available Information

Abstract The win ratio was introduced in 2012 as a new method for examining composite endpoints and has since been widely adopted in cardiovascular (CV) trials. Improving upon conventional methods for analysing composite endpoints, the win ratio accounts for relative priorities of the components and allows the components to be different types of outcomes. For example, the win ratio can combine the time to death with the number of occurrences of a non-fatal outcome such as CV-related hospitalizations (CVHs) in a single hierarchical composite endpoint. The win ratio can provide greater statistical power to detect and quantify a treatment difference by using all available information contained in the component outcomes. The win ratio can also incorporate quantitative outcomes such as exercise tests or quality-of-life scores. There is a need for more practical guidance on how best to design trials using the win ratio approach. This manuscript provides an overview of the principles behind the win ratio and provides insights into how to implement the win ratio in CV trial design and reporting, including how to determine trial size.

Testing for qualitative heterogeneity: An application to composite endpoints in survival analysis

2017 ◽  
Vol 28 (1) ◽  
pp. 151-169
Author(s):  
Abderrahim Oulhaj ◽  
Anouar El Ghouch ◽  
Rury R Holman
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Statistical Power ◽  
Controlled Trial ◽  
Composite Endpoint ◽  
Testing Procedure ◽  
Parameters Estimation ◽  
Type I ◽  
Data Set ◽  
Composite Endpoints

Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection–union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer’s disease.

FGFR3 mutation as a prognostic indicator in patients with urothelial carcinoma: A systematic review and meta-analysis.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 411-411
Author(s):  
Sidra Khalid ◽  
Bassam Mohammed Basulaiman ◽  
Jeff Emack ◽  
Christopher M. Booth ◽  
David Hernandez-Barajas ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Statistical Tests ◽  
Meta Analysis ◽  
Random Effect ◽  
Composite Endpoint ◽  
Systemic Review ◽  
Urothelial Carcinomas ◽  
Fgfr3 Mutation ◽  
Muscle Invasive ◽  
Fgfr3 Mutations

411 Background: Mutations in the fibroblast growth factor receptor-3 (FGFR3) have been implicated in urothelial tumorigenesis. The role of FGFR3 inhibitors in urothelial carcinoma is being explored in clinical trials. Here we explore the association between FGFR3 mutations and survival in urothelial carcinoma. Methods: We conducted a systemic review of electronic databases to identify studies published 1985-2018. Studies were included if they described the associated between FGFR3 mutations and outcomes of non-muscle invasive (NMI) and muscle invasive (MI) urothelial carcinomas. We used a composite endpoint of progression-free and recurrence-free survival (PRFS). Analysis was performed in Revman software. Hazard ratios (HR) and the 95% confidence intervals (CI) were obtained and entered; and then weighted and pooled in a meta-analysis with random effect modelling. The statistical tests were two sided. Results: Twelve retrospective and prospective studies comprising a total of 2162 patients were included. Analysis was done for two groups. The first group, included 1651 patients with NMI urothelial carcinomas; 886 (53.6%) of these had FGFR3 mutation. Compared to FGFR3 wild type, FGFR3 mutation did not influence PRFS (HR = 1.01, CI = 0.79-1.29, p = 0.95); I2 42%. In the second analysis, 511 patients with NMI and MI urothelial carcinomas were evaluated; 30% (n = 151) of which had FGFR3 mutation. In this group, FGFR3 mutation was not associated with PRFS (HR = 1.46, CI = 0.45-4.71, p = 0.53); I2 90%. Conclusions: Our meta-analysis does not show an association between FGFR3 mutation status and PRFS in urothelial carcinoma. [Table: see text]

scholarly journals Characterisation, identification, clustering, and classification of disease

2020 ◽  
Author(s):  
A.J. Webster ◽  
K. Gaitskell ◽  
I. Turnbull ◽  
B.J. Cairns ◽  
R. Clarke
Keyword(s):  
Risk Factors ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Disease Risk ◽  
Neurological Diseases ◽  
Disease Incidence ◽  
Disease Onset ◽  
Laboratory Findings ◽  
Clustering And Classification

Data-driven classifications are improving statistical power and refining prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases. Studies have used molecular information, age of disease incidence, and sequences of disease onset (“disease trajectories”). Here we consider whether easily measured risk factors such as height and BMI can usefully characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for study on the basis of clinical and epidemiological criteria, and a conventional proportional hazards model was used to estimate associations with 12 established risk factors. Comparing men and women, several diseases had strongly sex-dependent associations of disease risk with BMI. Despite this, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. This included 10 diseases presently classified as “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified”. Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases, provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.

scholarly journals 761Characterisation and clustering of diseases by their association with well-known risk factors

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Anthony Webster ◽  
Kezia Gaitskell ◽  
Iain Turnbull ◽  
Ben Cairns ◽  
Robert Clarke
Keyword(s):  
Risk Factors ◽  
Multiple Testing ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Neurological Diseases ◽  
Disease Onset ◽  
Significant Risk ◽  
Multivariate Statistical ◽  
Age Related

Abstract Background Data-driven classifications are improving statistical power, refining prognoses, and improving our understanding of autoimmune, respiratory, infectious, and neurological diseases. Classifications have used molecular information, age of incidence, and sequences of disease onset (“disease trajectories”). Here we consider whether associations with easily-measured established risk factors such as height and BMI can usefully characterise disease. Methods UK Biobank data and their linked hospital episode statistics were used to study 172 common age-related diseases. A proportional hazards model was used to estimate associations with potential risk-factors and to adjust for well-known confounders. Diseases were compared and hierarchically clustered using novel but rigorous multivariate statistical methods. Results For diseases affecting both sexes, over 38% can be uniquely identified by their associations with risk factors. Equivalent diseases often clustered adjacently. After an FDR multiple-testing adjustment, roughly 5% have statistically significant differences. Similar remarks applied to several symptoms of unknown cause. Many clustered diseases are associated with a shared, known pathogenesis, others suggest likely but unconfirmed causes. Conclusions Risk factors for disease can be surprisingly precise and can be used to cluster diseases in a meaningful way. Risk factors for men and women may differ for some diseases. Several symptoms of unknown cause have disease-specific, statistically significant risk factors. Key messages Big datasets and modern statistics are providing new insights into the relationships between diseases and their associations with risk-factors. Diseases can be identified and clustered by their associations with well-known risk factors.

A joint frailty-copula model between tumour progression and death for meta-analysis

2015 ◽  
Vol 26 (6) ◽  
pp. 2649-2666 ◽  
Author(s):  
Takeshi Emura ◽  
Masahiro Nakatochi ◽  
Kenta Murotani ◽  
Virginie Rondeau
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Likelihood Function ◽  
Tumour Progression ◽  
Meta Analysis ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Dependent Censoring ◽  
Recurrent Event ◽  
Recurrent Event Data

Dependent censoring often arises in biomedical studies when time to tumour progression (e.g., relapse of cancer) is censored by an informative terminal event (e.g., death). For meta-analysis combining existing studies, a joint survival model between tumour progression and death has been considered under semicompeting risks, which induces dependence through the study-specific frailty. Our paper here utilizes copulas to generalize the joint frailty model by introducing additional source of dependence arising from intra-subject association between tumour progression and death. The practical value of the new model is particularly evident for meta-analyses in which only a few covariates are consistently measured across studies and hence there exist residual dependence. The covariate effects are formulated through the Cox proportional hazards model, and the baseline hazards are nonparametrically modeled on a basis of splines. The estimator is then obtained by maximizing a penalized log-likelihood function. We also show that the present methodologies are easily modified for the competing risks or recurrent event data, and are generalized to accommodate left-truncation. Simulations are performed to examine the performance of the proposed estimator. The method is applied to a meta-analysis for assessing a recently suggested biomarker CXCL12 for survival in ovarian cancer patients. We implement our proposed methods in R joint.Cox package.

Factors Influencing Hospitalisation of Infants for Recurrent Gastroenteritis in Western Australia

2003 ◽  
Vol 42 (03) ◽  
pp. 251-254 ◽  
Author(s):  
K. Wang ◽  
K. K. W. Yau ◽  
A. H. Lee
Keyword(s):  
Prognostic Factors ◽  
Western Australia ◽  
Linked Data ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Discharge Rate ◽  
Discharge Summary ◽  
Older Children ◽  
Factors Influencing

Summary Objective: To determine factors affecting length of hospitalisation of infants for recurrent gastroenteritis using linked data records from the Western Australia heath information system. Methods: A seven-year retrospective cohort study was undertaken on all infants born in Western Australia in 1995 who were admitted for gastroenteritis during their first year of life (n = 519). Linked hospitalisation records were retrieved to derive the outcome measure and other demographic variables for the cohort. Unlike previous studies that focused mainly on a single episode of gastroenteritis, the durations of successive hospitalisations were analysed using a proportional hazards model with correlated frailty to determine the prognostic factors influencing recurrent gastroenteritis. Results: Older children experienced a shorter stay with an increased discharge rate of 1.9% for each month increase in admission age. An additional comorbidity recorded in the hospital discharge summary slowed the adjusted discharge rate by 46.5%. Aboriginal infants were readmitted to hospital more frequently, and had an adjusted hazard ratio of 0.253, implying a much higher risk of prolonged hospitalisation compared to non-Aborigines. Conclusions: The use of linked hospitalisation records has the advantage of providing access to hospital-based population information in the context of medical informatics. The analysis of linked data has enabled the assessment of prognostic factors influencing length of hospitalisations for recurrent gastroenteritis with high statistical power.

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