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  1. Article ; Online: Heterogeneity in meta-analysis: a comprehensive overview.

    Stogiannis, Dimitris / Siannis, Fotios / Androulakis, Emmanouil

    The international journal of biostatistics

    2023  

    Abstract: In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any ... ...

    Abstract In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another. This yields a considerable amount of variability, any kind of which may be termed heterogeneity. To this end, reports of meta-analyses commonly present a statistical test of heterogeneity when attempting to establish whether the included studies are indeed similar in terms of the reported output or not. We intend to provide an overview of the topic, discuss the potential sources of heterogeneity commonly met in the literature and provide useful guidelines on how to address this issue and to detect heterogeneity. Moreover, we review the recent developments in the Bayesian approach along with the various graphical tools and statistical software that are currently available to the analyst. In addition, we discuss sensitivity analysis issues and other approaches of understanding the causes of heterogeneity. Finally, we explore heterogeneity in meta-analysis for time to event data in a nutshell, pointing out its unique characteristics.
    Language English
    Publishing date 2023-03-27
    Publishing country Germany
    Document type Journal Article ; Review
    ISSN 1557-4679
    ISSN (online) 1557-4679
    DOI 10.1515/ijb-2022-0070
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  2. Article ; Online: Sensitivity analysis for multiple right censoring processes: investigating mortality in psoriatic arthritis.

    Siannis, Fotios

    Statistics in medicine

    2011  Volume 30, Issue 4, Page(s) 356–367

    Abstract: In a mortality study in psoriatic arthritis (PsA), censored observations are generated from the fact that patients fail to attend their scheduled appointments at the clinic. As a result, more than one types of right-censored observations are available. ... ...

    Abstract In a mortality study in psoriatic arthritis (PsA), censored observations are generated from the fact that patients fail to attend their scheduled appointments at the clinic. As a result, more than one types of right-censored observations are available. In survival analysis, the treatment of censored observations remains a concern. The assumption of ignorable censoring, although in many cases justified, is an important assumption made often for convenience rather than any other reason. In this paper we discuss a semi-parametric model for the analysis of survival data, where sensitivity analysis on quantities of interest can be performed when small levels of association between the failure and the censoring processes are assumed. Extension of the model allows for the presence of more than one censoring processes, where one may be characterized as ignorable and the other not. This model will be used to analyze the PsA mortality data, where a sensitivity analysis on parameters can be done under the assumption of non-ignorable censoring. Sensitivity analysis will also be performed in the presence of two censoring processes, one of which will be classified as non-ignorable.
    MeSH term(s) Age of Onset ; Arthritis, Psoriatic/mortality ; Computer Simulation/statistics & numerical data ; Humans ; Models, Statistical ; Survival Analysis
    Language English
    Publishing date 2011-02-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.4117
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  3. Article: Applications of a parametric model for informative censoring.

    Siannis, Fotios

    Biometrics

    2004  Volume 60, Issue 3, Page(s) 704–714

    Abstract: In this article, we explore the use of a parametric model (for analyzing survival data) which is defined to allow sensitivity analysis for the presence of informative censoring. The dependence between the failure and the censoring processes is expressed ... ...

    Abstract In this article, we explore the use of a parametric model (for analyzing survival data) which is defined to allow sensitivity analysis for the presence of informative censoring. The dependence between the failure and the censoring processes is expressed through a parameter delta and a general bias function B(t, theta). We calculate the expectation of the potential bias due to informative censoring, which is an overall measure of how misleading our results might be if censoring is actually nonignorable. Bounds are also calculated for quantities of interest, e.g., parameter of the distribution of the failure process, which do not depend on the choice of the bias function for fixed delta. An application that relates to systematic lupus erythematosus data illustrates how additional information can result in reducing the uncertainty on estimates of the location parameter. Sensitivity analysis on a relative risk parameter is also explored.
    MeSH term(s) Bias ; Biometry ; Follow-Up Studies ; Humans ; Lupus Erythematosus, Systemic/mortality ; Models, Statistical ; Risk ; Sensitivity and Specificity ; Survival Analysis
    Language English
    Publishing date 2004-09
    Publishing country United States
    Document type Comparative Study ; Journal Article
    ZDB-ID 213543-7
    ISSN 0006-341X ; 0099-4987
    ISSN 0006-341X ; 0099-4987
    DOI 10.1111/j.0006-341X.2004.00220.x
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  4. Article: Applications of a Parametric Model for Informative Censoring

    Siannis, Fotios

    Biometrics. 2004 Sept., v. 60, no. 3

    2004  

    Abstract: In this article, we explore the use of a parametric model (for analyzing survival data) which is defined to allow sensitivity analysis for the presence of informative censoring. The dependence between the failure and the censoring processes is expressed ... ...

    Abstract In this article, we explore the use of a parametric model (for analyzing survival data) which is defined to allow sensitivity analysis for the presence of informative censoring. The dependence between the failure and the censoring processes is expressed through a parameter δ and a general bias function B(t, θ). We calculate the expectation of the potential bias due to informative censoring, which is an overall measure of how misleading our results might be if censoring is actually nonignorable. Bounds are also calculated for quantities of interest, e.g., parameter of the distribution of the failure process, which do not depend on the choice of the bias function for fixed δ. An application that relates to systematic lupus erythematosus data illustrates how additional information can result in reducing the uncertainty on estimates of the location parameter. Sensitivity analysis on a relative risk parameter is also explored.
    Keywords biometry ; lupus erythematosus ; models ; relative risk ; uncertainty
    Language English
    Dates of publication 2004-09
    Size p. 704-714.
    Publishing place Blackwell Publishing
    Document type Article
    ZDB-ID 213543-7
    ISSN 0099-4987 ; 0006-341X
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1111/j.0006-341X.2004.00220.x
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  5. Article ; Online: Modelling competing risks data with missing cause of failure.

    Bakoyannis, Giorgos / Siannis, Fotios / Touloumi, Giota

    Statistics in medicine

    2010  Volume 29, Issue 30, Page(s) 3172–3185

    Abstract: When competing risks data arise, information on the actual cause of failure for some subjects might be missing. Therefore, a cause-specific proportional hazards model together with multiple imputation (MI) methods have been used to analyze such data. ... ...

    Abstract When competing risks data arise, information on the actual cause of failure for some subjects might be missing. Therefore, a cause-specific proportional hazards model together with multiple imputation (MI) methods have been used to analyze such data. Modelling the cumulative incidence function is also of interest, and thus we investigate the proportional subdistribution hazards model (Fine and Gray model) together with MI methods as a modelling approach for competing risks data with missing cause of failure. Possible strategies for analyzing such data include the complete case analysis as well as an analysis where the missing causes are classified as an additional failure type. These approaches, however, may produce misleading results in clinical settings. In the present work we investigate the bias of the parameter estimates when fitting the Fine and Gray model in the above modelling approaches. We also apply the MI method and evaluate its comparative performance under various missing data scenarios. Results from simulation experiments showed that there is substantial bias in the estimates when fitting the Fine and Gray model with naive techniques for missing data, under missing at random cause of failure. Compared to those techniques the MI-based method gave estimates with much smaller biases and coverage probabilities of 95 per cent confidence intervals closer to the nominal level. All three methods were also applied on real data modelling time to AIDS or non-AIDS cause of death in HIV-1 infected individuals.
    MeSH term(s) Acquired Immunodeficiency Syndrome/mortality ; Adult ; Bias ; Computer Simulation ; Female ; HIV Infections/drug therapy ; HIV-1/drug effects ; Humans ; Incidence ; Male ; Proportional Hazards Models ; Risk Assessment/methods
    Language English
    Publishing date 2010-12-30
    Publishing country England
    Document type Comparative Study ; Journal Article
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.4133
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  6. Article ; Online: A semi-competing risks model for data with interval-censoring and informative observation: an application to the MRC cognitive function and ageing study.

    Barrett, Jessica K / Siannis, Fotios / Farewell, Vern T

    Statistics in medicine

    2010  Volume 30, Issue 1, Page(s) 1–10

    Abstract: ... an extension of that used by Siannis et al. (Statist. Med. 2007; 26:426–442) to model semi-competing risks data ...

    Abstract Semi-competing risks data occur frequently in medical research when interest is in simultaneous modelling of two or more processes, one of which may censor the others. We consider the analysis of semi-competing risks data in the presence of interval-censoring and informative loss-to-followup. The work is motivated by a data set from the MRC UK Cognitive Function and Ageing Study, which we use to model two processes, cognitive impairment and death. Analysis is carried out using a multi-state model, which is an extension of that used by Siannis et al. (Statist. Med. 2007; 26:426–442) to model semi-competing risks data with exact transition times, to data which is interval-censored. Model parameters are estimated using maximum likelihood. The role of a sensitivity parameter k, which influences the nature of informative censoring, is explored.
    MeSH term(s) Age Factors ; Aged ; Aged, 80 and over ; Cognition Disorders ; Data Interpretation, Statistical ; Female ; Humans ; Longitudinal Studies ; Male ; Middle Aged ; Proportional Hazards Models ; Risk Assessment/methods ; United Kingdom
    Language English
    Publishing date 2010-11-05
    Publishing country England
    Document type Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.4071
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  7. Article ; Online: One-stage parametric meta-analysis of time-to-event outcomes.

    Siannis, F / Barrett, J K / Farewell, V T / Tierney, J F

    Statistics in medicine

    2010  Volume 29, Issue 29, Page(s) 3030–3045

    Abstract: Methodology for the meta-analysis of individual patient data with survival end-points is proposed. Motivated by questions about the reliance on hazard ratios as summary measures of treatment effects, a parametric approach is considered and percentile ... ...

    Abstract Methodology for the meta-analysis of individual patient data with survival end-points is proposed. Motivated by questions about the reliance on hazard ratios as summary measures of treatment effects, a parametric approach is considered and percentile ratios are introduced as an alternative to hazard ratios. The generalized log-gamma model, which includes many common time-to-event distributions as special cases, is discussed in detail. Likelihood inference for percentile ratios is outlined. The proposed methodology is used for a meta-analysis of glioma data that was one of the studies which motivated this work. A simulation study exploring the validity of the proposed methodology is available electronically.
    MeSH term(s) Algorithms ; Computer Simulation ; Glioma/drug therapy ; Glioma/mortality ; Glioma/therapy ; Humans ; Likelihood Functions ; Logistic Models ; Meta-Analysis as Topic ; Models, Statistical ; Proportional Hazards Models ; Randomized Controlled Trials as Topic ; Regression Analysis ; Statistical Distributions ; Survival Rate ; Treatment Outcome
    Language English
    Publishing date 2010-10-20
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.4086
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  8. Article: A multi-state model for joint modelling of terminal and non-terminal events with application to Whitehall II.

    Siannis, F / Farewell, V T / Head, J

    Statistics in medicine

    2005  Volume 26, Issue 2, Page(s) 426–442

    Abstract: ... epidemiological study of British civil servants. Both fatal (F) and non-fatal (NF) CHD events are of interest and ... while essentially complete information is available on F events, the observation of NF events is subject ... modelling of F and NF events. Two model-based assumptions ensure identifiability of the model and ...

    Abstract Serious coronary heart disease (CHD) is a primary outcome in the Whitehall II study, a large epidemiological study of British civil servants. Both fatal (F) and non-fatal (NF) CHD events are of interest and while essentially complete information is available on F events, the observation of NF events is subject to potentially informative censoring. A multi-state model with an unobserved state is introduced for the joint modelling of F and NF events. Two model-based assumptions ensure identifiability of the model and a parameter is introduced to allow sensitivity analyses concerning the assumption linked to informative censoring. Weibull transition rates, which include dependence on explanatory variables, are used in the analysis of Whitehall II data with a particular focus on the relationship between civil service grade and CHD events.
    MeSH term(s) Adult ; Age Factors ; Coronary Disease/epidemiology ; Coronary Disease/mortality ; Employment ; Female ; Humans ; Male ; Markov Chains ; Middle Aged ; Models, Biological ; Models, Statistical ; Sex Factors ; United Kingdom/epidemiology
    Language English
    Publishing date 2005-10-11
    Publishing country England
    Document type Comparative Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, P.H.S.
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.2342
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  9. Article ; Online: Two-stage meta-analysis of survival data from individual participants using percentile ratios.

    Barrett, Jessica K / Farewell, Vern T / Siannis, Fotios / Tierney, Jayne / Higgins, Julian P T

    Statistics in medicine

    2012  Volume 31, Issue 30, Page(s) 4296–4308

    Abstract: ... on the hazard ratio as a measure of treatment effect. Recently, Siannis et al. (2010, Statistics in Medicine 29 ...

    Abstract Methods for individual participant data meta-analysis of survival outcomes commonly focus on the hazard ratio as a measure of treatment effect. Recently, Siannis et al. (2010, Statistics in Medicine 29:3030-3045) proposed the use of percentile ratios as an alternative to hazard ratios. We describe a novel two-stage method for the meta-analysis of percentile ratios that avoids distributional assumptions at the study level.
    MeSH term(s) Analysis of Variance ; Computer Simulation ; Glioma/surgery ; Humans ; Kaplan-Meier Estimate ; Logistic Models ; Meta-Analysis as Topic ; Odds Ratio ; Postoperative Care/methods ; Postoperative Care/statistics & numerical data ; Probability ; Proportional Hazards Models ; Survival Analysis ; Treatment Outcome
    Language English
    Publishing date 2012-07-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.5516
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  10. Article: Sensitivity analysis for informative censoring in parametric survival models.

    Siannis, Fotios / Copas, John / Lu, Guobing

    Biostatistics (Oxford, England)

    2005  Volume 6, Issue 1, Page(s) 77–91

    Abstract: Most statistical methods for censored survival data assume there is no dependence between the lifetime and censoring mechanisms, an assumption which is often doubtful in practice. In this paper we study a parametric model which allows for dependence in ... ...

    Abstract Most statistical methods for censored survival data assume there is no dependence between the lifetime and censoring mechanisms, an assumption which is often doubtful in practice. In this paper we study a parametric model which allows for dependence in terms of a parameter delta and a bias function B(t, theta). We propose a sensitivity analysis on the estimate of the parameter of interest for small values of delta. This parameter measures the dependence between the lifetime and the censoring mechanisms. Its size can be interpreted in terms of a correlation coefficient between the two mechanisms. A medical example suggests that even a small degree of dependence between the failure and censoring processes can have a noticeable effect on the analysis.
    MeSH term(s) Data Interpretation, Statistical ; Humans ; Multiple Myeloma/mortality ; Proportional Hazards Models ; Sensitivity and Specificity ; Survival Analysis
    Language English
    Publishing date 2005-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2031500-4
    ISSN 1465-4644
    ISSN 1465-4644
    DOI 10.1093/biostatistics/kxh019
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