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REVIEW 3 major objections 4 minor 40 references

Decline of COPD exacerbations in clinical trials over two decades -- a systematic review and meta-regression

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Placebo exacerbation rates in COPD trials declined by 6.7% per year

desk verdict Worth engaging: a clinically meaningful trend with real soft spots, including an unsupported 'independence' claim, a copy-paste error, and an identifiability issue the paper never addresses. read the letter →

arxiv 1908.06340 v1 pith:SIWYAOQX submitted 2019-08-17 stat.AP

Stefan Andreas , Christian Röver , Judith Heinz , Sebastian Straube , Henrik Watz , Tim Friede This is my paper · ORCID
classification stat.AP MSC 62P1062F15
keywords COPDexacerbationsmeta-regressionplacebogroupsBayesiannegativebinomialmodeltemporaltrendsclinicaltrialdesignrandomizedcontrolledtrials
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the frequency of COPD exacerbations—sudden worsening of symptoms that drive hospitalizations—has been changing in the placebo groups of clinical trials. Analyzing 55 randomized trials spanning about two decades, it claims that the annual exacerbation rate fell by roughly 6.7% per year, a halving per decade, and that this decline persisted after adjusting for known prognostic factors such as lung function and symptom scores. The authors argue the trend is clinically relevant and that trial designers and meta-analysts cannot assume stable event rates. If true, comparisons between older and newer trials need recalibration, and part of the benefit attributed to new treatments may reflect improving background care.

What carries the argument

The central object is a Bayesian negative binomial meta-regression. The negative binomial distribution models each patient's exacerbation count with an annual rate and an overdispersion parameter; the model places study-specific random effects on log rate and log overdispersion and regresses log rate on publication year and covariates. Its key work is to convert heterogeneous aggregate reporting into a coherent estimate of the annual rate and its trend. Prior distributions are assigned to intercept, slope, and heterogeneity parameters, and inference is carried out by Markov chain Monte Carlo.

What would settle it

Recompute the meta-regression using only the 14 studies that report both a total exacerbation count and the number of exacerbation-free patients; if the estimated annual decline drops to near zero or changes sign, the headline trend is an artifact of the model's reconstructed rates.

Watch

Extended reading notes

Core claim

The central discovery is a temporal decline in COPD exacerbation rates among placebo-treated patients: a 6.7% reduction per year (95% credible interval 4.4 to 9.0%), equivalent to 50% per decade, based on 55 placebo groups with 14,065 patients and 10,491 patient-years. The trend is estimated from a Bayesian negative binomial meta-regression that jointly models different reporting formats, including direct rates, total event counts, event-free proportions, and combinations of these. The decline remains similar in sensitivity analyses restricted to true placebo arms and to arms allowing inhaled corticosteroids, and after adjustment for baseline FEV1 and SGRQ score, which themselves changed over time in the direction of milder trial populations.

Load-bearing premise

The estimate depends on the model's assumption that studies reporting only the proportion of patients without an exacerbation can be used to infer the underlying event rate; if that assumption is wrong, the 6.7% annual decline could be an artifact.

Editorial extensions

If this is right

  • Sample-size calculations for new COPD exacerbation trials should incorporate a falling baseline rate or use adaptive designs, otherwise studies risk being underpowered.
  • Treatment effects from older and newer trials cannot be compared directly; a 50% per decade background decline complicates network meta-analyses that span many years.
  • The observed trend is consistent with a substantial contribution of adjunct or background care, such as vaccination, comorbidity treatment, and lifestyle changes, to exacerbation reduction.
  • Reporting standards for exacerbation outcomes should be harmonized, because the analysis could only proceed by statistically reconstructing rates from sparse summary formats.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the decline reflects improving background care rather than trial selection, contemporary real-world COPD cohorts should also show falling exacerbation rates; linking trial placebo rates to administrative health data would test this.
  • The same Bayesian evidence-synthesis approach could be applied to other chronic diseases with count endpoints and heterogeneous reporting, such as asthma attacks or heart failure hospitalizations, to detect background trends.
  • Since only two measured baseline characteristics changed over time and adjustment only slightly altered the trend, unmeasured confounders such as smoking cessation rates, influenza vaccination coverage, or air quality deserve direct examination.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper presents a systematic review and Bayesian meta-regression of placebo groups from 55 randomized controlled trials of COPD, spanning roughly two decades. The authors develop a Bayesian negative binomial model that combines heterogeneous reporting formats (rates with standard errors, total counts, event-free proportions) and estimate a 6.7% annual decline in exacerbation rates (95% credible interval 4.4–9.0; pB < 0.001), with sensitivity analyses for true-placebo versus ICS-placebo subgroups and adjustment for SGRQ and FEV1. The paper concludes that the decline is clinically relevant and independent of important prognostic factors, with implications for trial design and network meta-analysis. Data and protocol are provided as supplementary files.

Significance. If the estimate is correct, the paper documents a large and clinically important time trend that affects power calculations, interpretation of historical trials, and comparability in network meta-analyses. The study is strengthened by a pre-registered protocol (PROSPERO), a well-documented Bayesian model, and the availability of the extracted data. However, the credibility of the headline estimate depends critically on the model's ability to recover rates from sparse summary data, and the claim of independence from prognostic factors is not fully supported by the fully adjusted analysis. The sensitivity analyses contain apparent reporting errors that need correction before the robustness claims can be evaluated.

major comments (3)
  1. [Data analysis, 'The model is primarily used to fit a time trend...' paragraph; Results, 'Temporal trends in…] For the 29 studies reporting only the proportion of exacerbation-free patients, the likelihood is essentially the zero probability p0 = (1 + φ δ λ)^(−1/φ), so the rate λ_i and overdispersion φ_i are not separately identifiable from each study. Identification is achieved through the shared log-normal random-effects distribution of φ_i, whose mean μφ is assumed constant over time. If overdispersion in these studies changed with calendar time—for example because exacerbation definitions, event ascertainment, or patient selection changed—the model would incorrectly attribute the trend in zero-proportion studies to λ_i, biasing the estimated annual decline. The paper reports no diagnostic or sensitivity analysis for a temporal trend in φ_i. Please report (i) a model allowing μφ to depend linearly on publication year and (ii) a re-analysis restricted to the 26 studies that report counts or rates, to assess robustness of the 6.7% annual decline.
  2. [Abstract and Conclusions vs. Table 4, row 'Adjusting for SGRQ and FEV1 (25 studies)'] The abstract and conclusions state that the decline is 'independent of important prognostic factors,' but the fully adjusted model in Table 4 gives β1 = −0.045 (95% CI −0.102 to 0.008; pB = 0.094), a credible interval that includes zero. This is the model that simultaneously adjusts for the two prognostic factors that changed significantly over time (SGRQ and FEV1), and it no longer provides statistical support for a decline. The text acknowledges wider CIs but the overall conclusion overstates the strength of the evidence. Please temper the claim, report the adjusted estimate more prominently, and discuss explicitly that adjustment in the reduced subset weakens the evidence for an independent trend.
  3. [Table 3 (True placebos vs. ICS-placebos)] The rows for 'True placebos' and 'ICS-placebos' in Table 3 are identical in every entry (same intercept, slope, heterogeneity, and overdispersion estimates), yet the Results text reports different annual declines for the two subgroups: 7.5% (95% CI 2.6–12.4) for true placebos and 6.7% (95% CI 3.7–9.6) for ICS-placebos. This is an internal inconsistency in a key sensitivity analysis and prevents verification of the subgroup claims. Please correct the table and check that the underlying computational results were not mis-transcribed.
minor comments (4)
  1. [Figure 4 caption] The caption states that the overall model gives an annual reduction of 6.3% (95% CI 3.9–8.7), while the text, abstract, and Table 2 report 6.7% (95% CI 4.4–9.0); please reconcile this discrepancy.
  2. [Reference 21] Reference 21 appears to combine the JAGS/rjags software manual with the FDA adaptive-design guidance under a single entry; the citation should be split and corrected, since the text cites it for both the software and the FDA guidance.
  3. [Figure 3 caption and Results] Figure 3 caption states baseline SGRQ is available for 27 studies, while the text and Table 4 say 28 studies; please clarify the correct number.
  4. [Typos throughout] Please fix several typographical errors: 'exercerbation' in the Background, 'oberved' in the Discussion, 'calender' in the Figure 4 caption, and the use of 'Pb' instead of 'pB' in Table 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the temporal trend is estimated from the extracted data, not derived from its inputs by construction.

full rationale

The paper is a meta-regression of placebo-group exacerbation rates from 55 RCTs. The headline estimate of a 6.7% annual decline is the posterior median of the slope parameter β1 in the model log(λ_i) ~ Normal(β0 + β1 x, σλ^2), fitted to the extracted study data; it is not a quantity defined in terms of the outcome, and no fitted value is relabeled as a prediction. The Bayesian negative binomial model is cited to the authors' own previous methods paper [14], but the current paper states the model equations and priors in detail, applies the model to new data, and does not invoke a uniqueness theorem or an ansatz as evidence. The adjusted analyses with SGRQ and FEV1 are separate regressions with additional covariates; they do not reduce to the unadjusted fit by construction. The concern that 29 zero-proportion-only studies make rate and overdispersion weakly identifiable under the assumed random-effects distribution is a legitimate robustness or identifiability criticism of the statistical model, but it is not circularity: the paper does not define the trend to be equal to an input or to a self-citation. No self-definitional, fitted-input-as-prediction, or self-citation-chain reduction is present.

Assumptions & free parameters 5 free parameters · 7 assumptions · 0 invented entities

The central estimate rests on a Bayesian negative binomial meta-regression model with several fitting choices and distributional assumptions. The paper introduces no new entities, but the model parameters and priors constitute the main modeling assumptions. Publication year as a time covariate and the handling of sparse data are the most consequential choices.

free parameters (5)
  • Rate slope beta1 (publication year) = -0.070 on log scale (95% CI -0.095 to -0.045)
    The central regression coefficient estimating the annual change in log exacerbation rate; it is fitted to the 55-study dataset and is the direct basis for the 6.7% per year decline claim.
  • Rate intercept beta0 = 0.434
    Fitted baseline log exacerbation rate in the reference year (2000), used to anchor the regression line.
  • Rate heterogeneity sigma_lambda = 0.409
    Random-effect standard deviation for study-specific log rates, fitted to accommodate between-study variability beyond the covariates.
  • Overdispersion mean mu_phi = -0.092
    Mean log overdispersion parameter in the negative binomial model, fitted from data on event counts and event-free patients.
  • Overdispersion heterogeneity sigma_phi = 0.709
    Standard deviation of study-specific log overdispersion, fitted to allow between-study differences in extra-Poisson variation.
assumptions (7)
  • domain assumption Exacerbation counts follow a negative binomial distribution with study-specific rate and overdispersion.
    Standard in COPD trial analysis (refs 13, 17, 18). The model generalizes the Poisson distribution to account for patient heterogeneity, but the choice of the negative binomial is an assumption about the data-generating process.
  • domain assumption The temporal trend is linear on the log-rate scale over publication year.
    The model assumes log(lambda_i) = beta0 + beta1 * year. If the true time trend is nonlinear, the reported slope is a weighted average and may misrepresent the decline at specific periods.
  • domain assumption Study-specific log rates and log overdispersions are normally distributed with common variances.
    The random-effects model borrows strength across studies, which is essential for studies with sparse data. The normality assumption is not empirically tested.
  • ad hoc to paper Weakly informative priors are appropriate: beta1 ~ Normal(0, 10^2), beta0 ~ Uniform(log(0.001), log(1000)), sigma_lambda ~ half-Normal(1), etc.
    The priors are chosen by the authors and are not derived from data. They may influence posterior intervals, especially in the small subgroup of 21 true-placebo studies.
  • domain assumption Studies are exchangeable after adjusting for covariates.
    Differences in regions, run-in periods, rescue medication policies, and exacerbation definitions are not modeled explicitly beyond random effects. This is a standard but strong assumption in meta-analysis.
  • domain assumption Publication year is a valid proxy for the calendar time of trial conduct.
    The covariate is publication year, not the actual trial conduct period. If publication delays or indexing practices changed over time, the estimated slope could be biased.
  • domain assumption Moderate-to-severe exacerbations are comparable across studies.
    When studies report multiple severity categories, moderate-to-severe events are used. The authors argue this minimizes definition differences, but no quantitative harmonization is applied.

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Cite this review

Pith. "Pith review of Decline of COPD exacerbations in clinical trials over two decades -- a systematic review and meta-regression." pith.science (2026). https://pith.science/paper/SIWYAOQX

@misc{pith2026190806340,
  author       = {Pith},
  title        = {Pith review of: Decline of COPD exacerbations in clinical trials over two decades -- a systematic review and meta-regression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SIWYAOQX}},
  note         = {Machine review of arXiv:1908.06340}
}
abstract

BACKGROUND: An important goal of chronic obstructive pulmonary disease (COPD) treatment is to reduce the frequency of exacerbations. Some observations suggest a decline in exacerbation rates in clinical trials over time. A more systematic understanding would help to improve the design and interpretation of COPD trials. METHODS: We performed a systematic review and meta-regression of the placebo groups in published randomized controlled trials reporting exacerbations as an outcome. A Bayesian negative binomial model was developed to accommodate results that are reported in different formats; results are reported with credible intervals (CI) and posterior tail probabilities ($p_B$). RESULTS: Of 1114 studies identified by our search, 55 were ultimately included. Exacerbation rates decreased by 6.7% (95% CI (4.4, 9.0); $p_B$ < 0.001) per year, or 50% (95% CI (36, 61)) per decade. Adjusting for available study and baseline characteristics such as forced expiratory volume in 1 s (FEV1) did not alter the observed trend considerably. Two subsets of studies, one using a true placebo group and the other allowing inhaled corticosteroids in the "placebo" group, also yielded consistent results. CONCLUSIONS: In conclusion, this meta-regression indicates that the rate of COPD exacerbations decreased over the past two decades to a clinically relevant extent independent of important prognostic factors. This suggests that care is needed in the design of new trials or when comparing results from older trials with more recent ones. Also a considerable effect of adjunct therapy on COPD exacerbations can be assumed.

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Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages

  1. [1]

    Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report

    Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, Celli BR, Chen R, Decramer M, Fabbri LM, Frith P, Halpin DM, Lopez Varela MV, Nishimura M, Roche N, Rodriguez-Roisin R, Sin DD, Singh D, Stockley R, Vestbo J, Wedzicha JA, Agusti A. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017...

    work page 2017

  2. [2]

    Fluticasone furoate and vilanterol and survival in chronic obstructive pulmonary disease with heightened cardiovascular risk (SUMMIT): a double-blind randomised controlled trial

    Vestbo J, Anderson JA, Brook RD, Calverley PM, Celli BR, Crim C, Martinez F, Yates J, Newby DE. Fluticasone furoate and vilanterol and survival in chronic obstructive pulmonary disease with heightened cardiovascular risk (SUMMIT): a double-blind randomised controlled trial. Lancet. 2016; 387(10030):1817– 26

    work page 2016

  3. [3]

    Indacaterol-Glycopyrronium versus Salmeterol-Fluticasone for COPD

    Wedzicha JA, Banerji D, Chapman KR, Vestbo J, Roche N, Ayers RT, Thach C, Fogel R, Patalano F, Vogelmeier CF. Indacaterol-Glycopyrronium versus Salmeterol-Fluticasone for COPD. N Engl J Med. 2016;374(23):2222 – 34

    work page 2016

  4. [4]

    Once-daily single-inhaler triple versus dual therapy in patients with COPD

    Lipson DA, Barnhart F, Brealey N, Brooks J, Criner GJ, Day NC, Dransfield M T ,H a l p i nD M G ,H a nM K ,J o n e sC E ,K i l b r i d eS ,L a n g eP ,L o m a sD A , Martinez FJ, Singh D, Tabberer M, Wise RA, Pascoe SJ. Once-daily single-inhaler triple versus dual therapy in patients with COPD. N Engl J Med. 2018;378(18):1671 – 80

    work page 2018

  5. [5]

    Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality

    Suissa S, Dell'Aniello S, Ernst P. Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality. Thorax. 2012;67(11):957– 63

    work page 2012

  6. [6]

    Cazzola M, MacNee W, Martinez FJ, Rabe KF, Franciosi LG, Barnes PJ, Brusasco V, Burge PS, Calverley PM, Celli BR, Jones PW, Mahler DA, Make B, Miravitlles M, Page CP, Palange P, Parr D, Pistolesi M, Rennard SI, Rutten-van Molken MP, Stockley R, Sullivan SD, Wedzicha JAW. E. F. outcomes for COPD pharmacological trials: from lung function to biomarkers. Eur...

    work page 2008

  7. [7]

    Wedzicha JA, Decramer M, Ficker JH, Niewoehner DE, Sandstrom T, Taylor AF, D'Andrea P, Arrasate C, Chen H, Banerji D. Analysis of chronic obstructive pulmonary disease exacerbations with the dual bronchodilator QVA149 compared with glycopyrronium and tiotropium (SPARK): a randomised, double-blind, parallel-group study. Lancet Respir Med. 2013; 1(3):199– 209

    work page 2013

  8. [8]

    Tiotropium and olodaterol in the prevention of chronic obstructive pulmonary disease exacerbations (DYNAGITO): a double-blind, randomised, parallel-group, active-controlled trial

    Calverley PMA, Anzueto AR, Carter K, Gronke L, Hallmann C, Jenkins C, Wedzicha J, Rabe KF. Tiotropium and olodaterol in the prevention of chronic obstructive pulmonary disease exacerbations (DYNAGITO): a double-blind, randomised, parallel-group, active-controlled trial. Lancet Respir Med. 2018;6(5):337 – 44

    work page 2018

Show all 40 references
  1. [9]

    The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration

    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Me...

    2009

  2. [10]

    Inhaled drugs to reduce exacerbations in patients with chronic obstructive pulmonary disease: a network meta-analysis

    Puhan MA, Bachmann LM, Kleijnen J, Ter Riet G, Kessels AG. Inhaled drugs to reduce exacerbations in patients with chronic obstructive pulmonary disease: a network meta-analysis. BMC Med. 2009;7:2

    2009

  3. [11]

    Pharmacotherapies for chronic obstructive pulmonary disease: a multiple treatment comparison meta- analysis

    Mills EJ, Druyts E, Ghement I, Puhan MA. Pharmacotherapies for chronic obstructive pulmonary disease: a multiple treatment comparison meta- analysis. Clin Epidemiol. 2011;28(3):107 – 29

    2011

  4. [12]

    Effectiveness versus efficacy trials in COPD: how study design influences outcomes and applicability

    Woodcock A, Boucot I, Leather DA, Crawford J, Collier S, Bakerly ND, Hilton E, Vestbo J. Effectiveness versus efficacy trials in COPD: how study design influences outcomes and applicability. Eur Respir J. 2018;51(2):1701531

    2018

  5. [13]

    Counting, analysing and reporting exacerbations of COPD in randomised controlled trials

    Aaron SD, Fergusson D, Marks GB, Suissa S, Vandemheen KL, Doucette S, Maltais F, Bourbeau JF, Goldstein RS, Balter M, O'Donnell D, Fitzgerald M. Counting, analysing and reporting exacerbations of COPD in randomised controlled trials. Thorax. 2008;63(2):122 – 8

    2008

  6. [14]

    Evidence synthesis for count distributions based on heterogeneous and incomplete aggregated data

    Roever C, Andreas S, Friede T. Evidence synthesis for count distributions based on heterogeneous and incomplete aggregated data. Biom J. 2016;58:170– 85

    2016

  7. [15]

    Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials

    Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, McQuay HJ. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17(1):1 – 12

    1996

  8. [16]

    ISMOD: an all-subsets regression program for generalized linear models

    Lawless JF, Singhal K. ISMOD: an all-subsets regression program for generalized linear models. I. Statistical and computational background. Comput Methods Prog Biomed. 1987;24(2):117 – 24

    1987

  9. [17]

    Statistical analysis of exacerbation rates in COPD: TRISTAN and ISOLDE revisited

    Keene ON, Calverley PM, Jones PW, Vestbo J, Anderson JA. Statistical analysis of exacerbation rates in COPD: TRISTAN and ISOLDE revisited. Eur Respir J. 2008;32(1):17 – 24

    2008

  10. [18]

    Guideline on clinical investigation of chronic obstructive pulmonary disease (COPD)

    EMA/CHMP/483672/2012. Guideline on clinical investigation of chronic obstructive pulmonary disease (COPD). In: (EMA) EMA, ed. EMA, 2012

    2012

  11. [19]

    Impact of frequency of COPD exacerbations on pulmonary function, health status and clinical outcomes

    Anzueto A, Leimer I, Kesten S. Impact of frequency of COPD exacerbations on pulmonary function, health status and clinical outcomes. Int J Chron Obstruct Pulmon Dis. 2009;4:245 – 51

    2009

  12. [20]

    Roflumilast in symptomatic chronic obstructive pulmonary disease: two randomised clinical trials

    Calverley PM, Rabe KF, Goehring UM, Kristiansen S, Fabbri LM, Martinez FJ. Roflumilast in symptomatic chronic obstructive pulmonary disease: two randomised clinical trials. Lancet. 2009;374(9691):685 – 94. 2 1 . P l u m m e rM ,S t u k a l o v ,A . ,D e n w o o d ,M .A d a p t...

    2009

  13. [22]

    Explaining temporal trends in annualised relapse rates in placebo groups of randomised controlled trials in relapsing multiple sclerosis: systematic review and meta-regression

    Steinvorth SM, Roever C, Schneider S, Nicholas R, Straube S, Friede T. Explaining temporal trends in annualised relapse rates in placebo groups of randomised controlled trials in relapsing multiple sclerosis: systematic review and meta-regression. Mult Scler. 2013;19(12):1580 – 6

    2013

  14. [23]

    Minimal clinically important difference--exacerbations of COPD

    Calverley PM. Minimal clinically important difference--exacerbations of COPD. COPD. 2005;2(1):143 – 8

    2005

  15. [24]

    Carrasco-Garrido P

    de Miguel-Diez J, Jimenez-Garcia R, Hernandez-Barrera V, Puente-Maestu L, Rodriguez-Rodriguez P, Lopez de Andres A. Carrasco-Garrido P. Trends in hospital admissions for acute exacerbation of COPD in Spain from 2006 to

    2006

  16. [25]

    Chronic Obstructive Pulmonary Disease and Cardiac Diseases

    Roversi S, Fabbri LM, Sin DD, Hawkins NM, Agusti A. Chronic Obstructive Pulmonary Disease and Cardiac Diseases. An Urgent Need for Integrated Care. Am J Respir Crit Care Med. 2016;194(11):1319 – 36

    2016

  17. [26]

    Historic air pollution exposure and long-term mortality risks in England and Wales: prospective longitudinal cohort study

    Hansell A, Ghosh RE, Blangiardo M, Perkins C, Vienneau D, Goffe K, Briggs D, Gulliver J. Historic air pollution exposure and long-term mortality risks in England and Wales: prospective longitudinal cohort study. Thorax. 2016; 71(4):330– 8

    2016

  18. [27]

    Susceptibility to exacerbation in chronic obstructive pulmonary disease

    Hurst JR, Vestbo J, Anzueto A, Locantore N, Mullerova H, Tal-Singer R, Miller B, Lomas DA, Agusti A, Macnee W, Calverley P, Rennard S, Wouters EF, Wedzicha JA. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med. 2010;363(12):1128 – 38

    2010

  19. [28]

    Characterisation of exacerbation risk and exacerbator phenotypes in the POET-COPD trial

    Beeh KM, Glaab T, Stowasser S, Schmidt H, Fabbri LM, Rabe KF, Vogelmeier CF. Characterisation of exacerbation risk and exacerbator phenotypes in the POET-COPD trial. Respir Res. 2013;29(14):116

    2013

  20. [29]

    Frequency of exacerbations in patients with chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort

    Han MK, Quibrera PM, Carretta EE, Barr RG, Bleecker ER, Bowler RP, Cooper CB, Comellas A, Couper DJ, Curtis JL, Criner G, Dransfield MT, Hansel NN, Hoffman EA, Kanner RE, Krishnan JA, Martinez CH, Pirozzi CB, O'Neal WK, Rennard S, Tashkin DP, Wedzicha JA, Woodruff P, Paine R 3...

    2017

  21. [30]

    Current concepts in targeting chronic obstructive pulmonary disease pharmacotherapy: making progress towards personalised management

    Woodruff PG, Agusti A, Roche N, Singh D, Martinez FJ. Current concepts in targeting chronic obstructive pulmonary disease pharmacotherapy: making progress towards personalised management. Lancet. 2015;385(9979):1789 – 98

    2015

  22. [31]

    Blood eosinophil count and exacerbations in severe chronic obstructive pulmonary disease after withdrawal of inhaled corticosteroids: a post-hoc analysis of the WISDOM trial

    Watz H, Tetzlaff K, Wouters EF, Kirsten A, Magnussen H, Rodriguez- Roisin R, Vogelmeier C, Fabbri LM, Chanez P, Dahl R, Disse B, Finnigan H, Calverley PM. Blood eosinophil count and exacerbations in severe chronic obstructive pulmonary disease after withdrawal of inhaled corti...

    2016

  23. [32]

    Definitions of exacerbations: does it really matter in clinical trials on COPD

    Effing TW, Kerstjens HAM, Monninkhof EM, van der Valk P, Wouters EFM, Postma DS, Zielhuis GA, van der Palen J. Definitions of exacerbations: does it really matter in clinical trials on COPD. Chest. 2009;136(3):918 – 23

    2009

  24. [33]

    Dong YH, Lin HH, Shau WY, Wu YC, Chang CH, Lai MS. Comparative safety of inhaled medications in patients with chronic obstructive pulmonary disease: systematic review and mixed treatment comparison meta-analysis of randomised controlled trials. Thorax. 2013;68(1):48 – 56

    2013

  25. [34]

    Statistical treatment of exacerbations in therapeutic trials of chronic obstructive pulmonary disease

    Suissa S. Statistical treatment of exacerbations in therapeutic trials of chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2006; 173(8):842– 6. Andreas et al. Respiratory Research (2019) 20:186 Page 10 of 11

    2019

  26. [35]

    Trends in annualized relapse rates in relapsing-remitting multiple sclerosis and consequences for clinical trial design

    Nicholas R, Straube S, Schmidli H, Schneider S, Friede T. Trends in annualized relapse rates in relapsing-remitting multiple sclerosis and consequences for clinical trial design. Mult Scler. 2011;17(10):1211 – 7

    2011

  27. [36]

    A pulmonologist's guide to understanding network meta-analysis

    Dobler CC, Wilson ME, Murad MH. A pulmonologist's guide to understanding network meta-analysis. Eur Respir J. 2018;52(1):1800525

    2018

  28. [37]

    Inhaled corticosteroids in patients with stable chronic obstructive pulmonary disease: a systematic review and meta-analysis

    Drummond MB, Dasenbrook EC, Pitz MW, Murphy DJ, Fan E. Inhaled corticosteroids in patients with stable chronic obstructive pulmonary disease: a systematic review and meta-analysis. JAMA. 2008;300(20):2407 – 16

    2008

  29. [38]

    Comparative efficacy of long-acting bronchodilators for COPD: a network meta-analysis

    Cope S, Donohue JF, Jansen JP, Kraemer M, Capkun-Niggli G, Baldwin M, Buckley F, Ellis A, Jones P. Comparative efficacy of long-acting bronchodilators for COPD: a network meta-analysis. Respir Res. 2013;14:100

    2013

  30. [39]

    Long-acting inhaled therapy (beta-agonists, anticholinergics and steroids) for COPD: a network meta-analysis

    Kew KM, Dias S, Cates CJ. Long-acting inhaled therapy (beta-agonists, anticholinergics and steroids) for COPD: a network meta-analysis. Cochrane Database Syst Rev. 2014;26(3):CD010844

    2014

  31. [40]

    Recommendations for Drug Therapies for Relapsing-Remitting Multiple Sclerosis

    CADTH. Recommendations for Drug Therapies for Relapsing-Remitting Multiple Sclerosis. Canadian Agency for Drugs and Technologies in Health 2013: CADTH Therapeutic Review, Volume 1(Issue 2C). 1-22. Publisher’sN o t e Springer Nature remains neutral with regard to jurisdictional...

    2019

  32. [2010]

    2013;107(5):717 – 23

    Respir Med. 2013;107(5):717 – 23

    2013

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