Modelling the overdiagnosis of breast cancer due to mammography screening in women aged 40 to 49 in the United Kingdom

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Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance

BACKGROUND: Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective of this study was to assess the performance of a proposed Bayesian approach to adjust for imbalances in patient level covariates when combining evidence from both types of study designs. METHODOLOGY/PRINCIPAL FINDINGS: Simulation techniques, in which the truth is known, were used to generate sets of data for randomised and non-randomised studies. Covariate imbalances between study arms were introduced in the non-randomised studies. The performance of the Bayesian hierarchical model adjusted for imbalances was assessed in terms of bias. The data were also modelled using three other Bayesian approaches for synthesising evidence from randomised and non-randomised studies. The simulations considered six scenarios aimed at assessing the sensitivity of the results to changes in the impact of the imbalances and the relative number and size of studies of each type. For all six scenarios considered, the Bayesian hierarchical model adjusted for differences within studies gave results that were unbiased and closest to the true value compared to the other models. CONCLUSIONS/SIGNIFICANCE: Where informed health care decision making requires the synthesis of evidence from randomised and non-randomised study designs, the proposed hierarchical Bayesian method adjusted for differences in patient characteristics between study arms may facilitate the optimal use of all available evidence leading to unbiased results compared to unadjusted analyses

Symptom lead times in lung and colorectal cancers: What are the benefits of symptom-based approaches to early diagnosis?

This is the final version of the article. Available from Cancer Research UK via the DOI in this record.Background: Individuals with undiagnosed lung and colorectal cancers present with non-specific symptoms in primary care more often than matched controls. Increased access to diagnostic services for patients with symptoms generates more early-stage diagnoses, but the mechanisms for this are only partially understood. Methods: We re-analysed a UK-based case-control study to estimate the Symptom Lead Time (SLT) distribution for a range of potential symptom criteria for investigation. Symptom Lead Time is the time between symptoms caused by cancer and eventual diagnosis, and is analogous to Lead Time in a screening programme. We also estimated the proportion of symptoms in lung and colorectal cancer cases that are actually caused by the cancer. Results: Mean Symptom Lead Times were between 4.1 and 6.0 months, with medians between 2.0 and 3.2 months. Symptom Lead Time did not depend on stage at diagnosis, nor which criteria for investigation are adopted. Depending on the criteria, an estimated 27-48% of symptoms in individuals with as yet undiagnosed lung cancer, and 12-32% with undiagnosed colorectal cancer are not caused by the cancer. Conclusions: In most cancer cases detected by a symptom-based programme, the symptoms are caused by cancer. These cases have a short lead time and benefit relatively little. However, in a significant minority of cases cancer detection is serendipitous. This group experiences the benefits of a standard screening programme, a substantial mean lead time and a higher probability of early-stage diagnosis.This work was supported by the National Institute for Health Research (NIHR) Programme Grants for Applied Research Programme, RP-PG-0608-10045

Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

<p>Abstract</p> <p>Background</p> <p>We propose a simple new method for estimating progression of a chronic disease with multi-state properties by unifying the prevalence pool concept with the Markov process model.</p> <p>Methods</p> <p>Estimation of progression rates in the multi-state model is performed using the E-M algorithm. This approach is applied to data on Type 2 diabetes screening.</p> <p>Results</p> <p>Good convergence of estimations is demonstrated. In contrast to previous Markov models, the major advantage of our proposed method is that integrating the prevalence pool equation (that the numbers entering the prevalence pool is equal to the number leaving it) into the likelihood function not only simplifies the likelihood function but makes estimation of parameters stable.</p> <p>Conclusion</p> <p>This approach may be useful in quantifying the progression of a variety of chronic diseases.</p

The importance of adjusting for potential confounders in Bayesian hierarchical models synthesising evidence from randomised and non-randomised studies: an application comparing treatments for abdominal aortic aneurysms

<p>Abstract</p> <p>Background</p> <p>Informing health care decision making may necessitate the synthesis of evidence from different study designs (e.g., randomised controlled trials, non-randomised/observational studies). Methods for synthesising different types of studies have been proposed, but their routine use requires development of approaches to adjust for potential biases, especially among non-randomised studies. The objective of this study was to extend a published Bayesian hierarchical model to adjust for bias due to confounding in synthesising evidence from studies with different designs.</p> <p>Methods</p> <p>In this new methodological approach, study estimates were adjusted for potential confounders using differences in patient characteristics (e.g., age) between study arms. The new model was applied to synthesise evidence from randomised and non-randomised studies from a published review comparing treatments for abdominal aortic aneurysms. We compared the results of the Bayesian hierarchical model adjusted for differences in study arms with: 1) unadjusted results, 2) results adjusted using aggregate study values and 3) two methods for downweighting the potentially biased non-randomised studies. Sensitivity of the results to alternative prior distributions and the inclusion of additional covariates were also assessed.</p> <p>Results</p> <p>In the base case analysis, the estimated odds ratio was 0.32 (0.13,0.76) for the randomised studies alone and 0.57 (0.41,0.82) for the non-randomised studies alone. The unadjusted result for the two types combined was 0.49 (0.21,0.98). Adjusted for differences between study arms, the estimated odds ratio was 0.37 (0.17,0.77), representing a shift towards the estimate for the randomised studies alone. Adjustment for aggregate values resulted in an estimate of 0.60 (0.28,1.20). The two methods used for downweighting gave odd ratios of 0.43 (0.18,0.89) and 0.35 (0.16,0.76), respectively. Point estimates were robust but credible intervals were wider when using vaguer priors.</p> <p>Conclusions</p> <p>Covariate adjustment using aggregate study values does not account for covariate imbalances between treatment arms and downweighting may not eliminate bias. Adjustment using differences in patient characteristics between arms provides a systematic way of adjusting for bias due to confounding. Within the context of a Bayesian hierarchical model, such an approach could facilitate the use of all available evidence to inform health policy decisions.</p

Assessment of possible impact of a health promotion program in Korea from health risk trends in a longitudinally observed cohort

BACKGROUND: Longitudinally observed cohort data can be utilized to assess the potential for health promotion and healthcare planning by comparing the estimated risk factor trends of non-intervened with that of intervened. The paper seeks (1) to estimate a natural transition (patterns of movement between states) of health risk state from a Korean cohort data using a Markov model, (2) to derive an effective and necessary health promotion strategy for the population, and (3) to project a possible impact of an intervention program on health status. METHODS: The observed transition of health risk states in a Korean employee cohort was utilized to estimate the natural flow of aggregated health risk states from eight health risk measures using Markov chain models. In addition, a reinforced transition was simulated, given that a health promotion program was implemented for the cohort, to project a possible impact on improvement of health status. An intervened risk transition was obtained based on age, gender, and baseline risk state, adjusted to match with the Korean cohort, from a simulated random sample of a US employee population, where a health intervention was in place. RESULTS: The estimated natural flow (non-intervened), following Markov chain order 2, showed a decrease in low risk state by 3.1 percentage points in the Korean population while the simulated reinforced transition (intervened) projected an increase in low risk state by 7.5 percentage points. Estimated transitions of risk states demonstrated the necessity of not only the risk reduction but also low risk maintenance. CONCLUSIONS: The frame work of Markov chain efficiently estimated the trend, and captured the tendency in the natural flow. Given only a minimally intense health promotion program, potential risk reduction and low risk maintenance was projected

Evaluating risk factor assumptions: a simulation-based approach

<p>Abstract</p> <p>Background</p> <p>Microsimulation models are an important tool for estimating the comparative effectiveness of interventions through prediction of individual-level disease outcomes for a hypothetical population. To estimate the effectiveness of interventions targeted toward high risk groups, the mechanism by which risk factors influence the natural history of disease must be specified. We propose a method for evaluating these risk factor assumptions as part of model-building.</p> <p>Methods</p> <p>We used simulation studies to examine the impact of risk factor assumptions on the relative rate (RR) of colorectal cancer (CRC) incidence and mortality for a cohort with a risk factor compared to a cohort without the risk factor using an extension of the CRC-SPIN model for colorectal cancer. We also compared the impact of changing age at initiation of screening colonoscopy for different risk mechanisms.</p> <p>Results</p> <p>Across CRC-specific risk factor mechanisms, the RR of CRC incidence and mortality decreased (towards one) with increasing age. The rate of change in RRs across age groups depended on both the risk factor mechanism and the strength of the risk factor effect. Increased non-CRC mortality attenuated the effect of CRC-specific risk factors on the RR of CRC when both were present. For each risk factor mechanism, earlier initiation of screening resulted in more life years gained, though the magnitude of life years gained varied across risk mechanisms.</p> <p>Conclusions</p> <p>Simulation studies can provide insight into both the effect of risk factor assumptions on model predictions and the type of data needed to calibrate risk factor models.</p

Evidence synthesis as the key to more coherent and efficient research

<p>Abstract</p> <p>Background</p> <p>Systematic review and meta-analysis currently underpin much of evidence-based medicine. Such methodologies bring order to <it>previous </it>research, but <it>future </it>research planning remains relatively incoherent and inefficient.</p> <p>Methods</p> <p>To outline a framework for evaluation of health interventions, aimed at increasing coherence and efficiency through i) making better use of information contained within the existing evidence-base when designing future studies; and ii) maximising the information available and thus potentially reducing the need for future studies.</p> <p>Results</p> <p>The framework presented insists that an up-to-date meta-analysis of existing randomised controlled trials (RCTs) should always be considered before future trials are conducted. Such a meta-analysis should inform critical design issues such as sample size determination. The contexts in which the use of individual patient data meta-analysis and mixed treatment comparisons modelling may be beneficial before further RCTs are conducted are considered. Consideration should also be given to how any newly planned RCTs would contribute to the totality of evidence through its incorporation into an updated meta-analysis. We illustrate how new RCTs can have very low power to change inferences of an existing meta-analysis, particularly when between study heterogeneity is taken into consideration.</p> <p>Conclusion</p> <p>While the collation of existing evidence as the basis for clinical practice is now routine, a more coherent and efficient approach to planning future RCTs to strengthen the evidence base needs to be developed. The framework presented is a proposal for how this situation can be improved.</p

Symptom lead times in lung and colorectal cancers: what are the benefits of symptom-based approaches to early diagnosis?

BACKGROUND: Individuals with undiagnosed lung and colorectal cancers present with non-specific symptoms in primary care more often than matched controls. Increased access to diagnostic services for patients with symptoms generates more early-stage diagnoses, but the mechanisms for this are only partially understood. METHODS: We re-analysed a UK-based case–control study to estimate the Symptom Lead Time (SLT) distribution for a range of potential symptom criteria for investigation. Symptom Lead Time is the time between symptoms caused by cancer and eventual diagnosis, and is analogous to Lead Time in a screening programme. We also estimated the proportion of symptoms in lung and colorectal cancer cases that are actually caused by the cancer. RESULTS: Mean Symptom Lead Times were between 4.1 and 6.0 months, with medians between 2.0 and 3.2 months. Symptom Lead Time did not depend on stage at diagnosis, nor which criteria for investigation are adopted. Depending on the criteria, an estimated 27–48% of symptoms in individuals with as yet undiagnosed lung cancer, and 12–32% with undiagnosed colorectal cancer are not caused by the cancer. CONCLUSIONS: In most cancer cases detected by a symptom-based programme, the symptoms are caused by cancer. These cases have a short lead time and benefit relatively little. However, in a significant minority of cases cancer detection is serendipitous. This group experiences the benefits of a standard screening programme, a substantial mean lead time and a higher probability of early-stage diagnosis