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Generalised Evidence Synthesis. Keith Abrams, Cosetta Minelli, Nicola Cooper & Alex Sutton Medical Statistics Group Department of Health Sciences, University of Leicester, UK. CHEBS Seminar ‘Focusing on the Key Challenges’ Nov 7, 2003. Outline. Why Generalised Evidence Synthesis? - PowerPoint PPT Presentation

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  • Generalised Evidence Synthesis

    Keith Abrams, Cosetta Minelli, Nicola Cooper & Alex SuttonMedical Statistics GroupDepartment of Health Sciences, University of Leicester, UK

    CHEBS SeminarFocusing on the Key ChallengesNov 7, 2003

  • OutlineWhy Generalised Evidence Synthesis?

    Bias in observational evidence

    Example: Hormone Replacement Therapy (HRT) & Breast Cancer

    Discussion

  • Why Generalised Evidence Synthesis?RCT evidence gold standard for assessing efficacy (internal validity)

    Generalisability of RCT evidence may be difficult (external validity), e.g. CHD & women

    Paucity of RCT evidence, e.g. adverse events

    Difficult to conduct RCTs in some situations, e.g. policy changes

    RCTs have yet to be conducted, but health policy decisions have to be made

    Consider totality of evidence-base (G)ES beyond MA of RCTs

  • Assessment of Bias in Observational Studies - 1

    Empirical evidence relating to potential extent of bias in observational evidence (Deeks et al. 2003)

    Primary studies: Sacks et al. (1982) & Benson et al. (2000)

    Primary & Secondary studies (meta-analyses): Britton et al. (1998) & MacLehose et al. (2000)

    Secondary studies (meta-analyses):Kunz et al. (1998,2000), Concato et al. (2000) & Ioannidis et al. (2001)

  • Assessment of Bias in Observational Studies - 2

    Using a random effects meta-epidemiology model (Sterne et al. 2002)

    Sacks et al. (1982) & Schultz et al. (1995) ~ 30%Ioannidis et al. (2001) ~ 50%MacLehose et al. (2000) ~ 100%

    Deeks et al. (2003) simulation study: comparison of RCTs and historical/concurrent observational studies Empirical assessment of bias results similar to previous meta-epidemiological studiesMethods of case-mix adjustment, regression & propensity scores fail to properly account for bias

  • Approaches to Evidence SynthesisTreat sources separately, possibly ignoring/downweighting some implicitly

    Bayesian approach & treat observational evidence as prior for RCTs & explicit consideration of bias:Power Transform PriorBias Allowance Model

    Generalised Evidence Synthesis

  • Example HRTHRT used for relief of menopausal symptoms

    Prevention of fractures, especially in women with osteoporosis & low bone mineral density

    BUT concerns have been raised over possible increased risk of Breast Cancer

  • HRT & Breast Cancer RCT Evidence before July 2002Source: Torgerson et al. (2002)OR 0.97 95% CI 0.67 to 1.39

  • HRT & Breast Cancer Observational Evidence*Source: Lancet (1997)* Adjusted for possible confoundersAll ObservationalOR 1.18 95% CI 1.10 to 1.26RCTsOR 0.97 95% CI 0.67 to 1.39

  • Use of Observational Evidence in Prior Distribution Case-ControlQuasi RCTsRCTsCohortPriorSynthesisEmpirical EvidenceBias

  • Following Ibrahim & Chen (2000)

    0 1 is degree of downweighting = 0 total discounting = 1 accept at face valueEvaluate for a range of values of

    Power Transform Prior

  • Power Transform Prior Results 1

  • * is unbiased true effect in observational studies is bias associated with observational evidence2 represents a priori beliefs regarding the possible extent of the bias

    Bias Allowance ModelFollowing Spiegelhalter et al. 2003

  • Bias Allowance Model - Results

    Belief/SourceBias2OR95% CrIP(OR>1)Face Value0%01.141.07 to 1.201.00Total Discounting%0.870.30 to 1.600.31Sacks & Schultz30%0.021.080.85 to 1.370.72Ioannidis50%0.081.000.68 to 1.450.50MacLehose100%0.240.940.56 to 1.490.40

  • HRT & Breast Cancer: Evidence July 2002 HERS II (JAMA July 3) [Follow-up of HERS] n = 2321 & 29 Breast Cancers OR 1.08 (95% CI: 0.52 to 2.25)

    WHI (JAMA July 17) [Stopped early] n= 16,608 & 290 Breast Cancers OR 1.28 (95% CI: 1.01 to 1.62)

    HERS II & WHIOR 1.26 (95% CI: 1.01 to 1.58)

    Revised Meta-Analysis of RCTs WHI 68% weight OR 1.20 (95% CI: 0.99 to 1.45)

  • Power Transform Prior Results

  • Generalised Evidence SynthesisModelling RCT & observational (3 types) evidence directly;Hierarchical Models (Prevost et al, 2000;Sutton & Abrams, 2001)Confidence Profiling (Eddy et al, 1990)

    Overcomes whether RCTs should form likelihood & observational studies prior

  • Generalised Evidence SynthesisDecision ModelUtilitiesCosts

  • Hierarchical Model

  • HRT: Hierarchical Model - Results* Ignores study-type

    IndependentHierarchicalOR95% CrIOR95% CrIRCT0.890.39 to 1.521.020.76 to 1.27Cohort0.980.84 to 1.121.010.89 to 1.13CC-P1.060.96 to 1.061.050.97 to 1.15CC-H1.230.94 to 1.551.120.93 to 1.36Overall1.05*0.98 to 1.131.050.87 to 1.24

  • Hierarchical Model - ExtensionsInclusion of empirical assessment of (differential) bias with uncertainty, i.e. distribution

    Bias Constraint, e.g. HRT

  • Discussion 1 Direct vs Indirect use of non-RCT evidenceDirect: intervention effect, e.g. RRIndirect: other model parameters, e.g. correlation between time points

    Allowing for bias/adjusting at study-levelIPD if aggregate patient-level covariates are important, e.g. age, prognostic scoreQuality better instruments for non-RCTs & sensitivity of results to instruments

  • Discussion 2 Subjective prior beliefs regarding relative credibility (bias or relevance) of sources of evidenceElicitation

    Bayesian methods provide A flexible framework to consider inclusion of all evidence, & which is explicit & transparent, BUT Require careful & critical application

  • ReferencesDeeks JJ et al. Evaluating non-randomised intervention studies. HTA 2003;7(27).Eddy DM et al. A Bayesian method for synthesizing evidence. The Confidence Profile Method. IJTAHC 1990;6(1):31-55.Ibrahim JG & Chen MH. Power prior distributions for regression models. Stat. Sci. 2000 15(1):46-60.Prevost TC et al. Hierarchical models in generalised synthesis of evidence: an example based on studies of breast cancer. Stat Med 2000;19:3359-76.Sterne JAC et al. Statistical methods for assessing the influence of study characteristics on treatment effects in meta-epidemiological research. Stat. Med. 2002;21:1513-1524.Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials & Health-care Evaluation. London: Wiley, 2003.Sutton AJ & Abrams KR. Bayesian methods in meta-analysis and evidence synthesis. SMMR 2001;10(4):277-303.

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