Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness.

BACKGROUND AND OBJECTIVES: Drawing causal conclusions from real-world data (RWD) poses methodological challenges and risk of bias. We aimed to systematically assess the type and impact of potential biases that may occur when analyzing RWD using the case of progressive ovarian cancer. METHODS: We retrospectively compared overall survival with and without second-line chemotherapy (LOT2) using electronic medical records. Potential biases were determined using directed acyclic graphs. We followed a stepwise analytic approach ranging from crude analysis and multivariable-adjusted Cox model up to a full causal analysis using a marginal structural Cox model with replicates emulating a reference randomized controlled trial (RCT). To assess biases, we compared effect estimates (hazard ratios [HRs]) of each approach to the HR of the reference trial. RESULTS: The reference trial showed an HR for second line vs. delayed therapy of 1.01 (95% confidence interval [95% CI]: 0.82-1.25). The corresponding HRs from the RWD analysis ranged from 0.51 for simple baseline adjustments to 1.41 (95% CI: 1.22-1.64) accounting for immortal time bias with time-varying covariates. Causal trial emulation yielded an HR of 1.12 (95% CI: 0.96-1.28). CONCLUSION: Our study, using ovarian cancer as an example, shows the importance of a thorough causal design and analysis if one is expecting RWD to emulate clinical trial results.

Estimating Treatment-Switching Bias in a Randomized Clinical Trial of Ovarian Cancer Treatment: Combining Causal Inference with Decision-Analytic Modeling.

BACKGROUND: Bevacizumab is efficacious in delaying ovarian cancer progression and controlling ascites. The ICON7 trial showed a significant benefit in overall survival for bevacizumab, whereas the GOG-218 trial did not. GOG-218 allowed control group patients to switch to bevacizumab upon progression, which may have biased the results. Lack of data on switching behavior prevented the application of g-methods to adjust for switching. The objective of this study was to apply decision-analytic modeling to estimate the impact of switching bias on causal treatment-effect estimates. METHODS: We developed a causal decision-analytic Markov model (CDAMM) to emulate the GOG-218 trial and estimate overall survival. CDAMM input parameters were based on data from randomized clinical trials and the published literature. Overall switching proportion was based on GOG-218 trial information, whereas the proportion switching with and without ascites was estimated using calibration. We estimated the counterfactual treatment effect that would have been observed had no switching occurred by denying switching in the CDAMM. RESULTS: The survival curves generated by the CDAMM matched well with the ones reported in the GOG-218 trial. The survival curve correcting for switching showed an estimated bias such that 79% of the true treatment effect could not be observed in the GOG-218 trial. Results were most sensitive to changes in the proportion progressing with severe ascites and mortality. LIMITATIONS: We used a simplified model structure and based model parameters on published data and assumptions. Robustness of the CDAMM was tested and model assumptions transparently reported. CONCLUSIONS: Medical-decision science methods may be merged with empirical methods of causal inference to integrate data from other sources where empirical data are not sufficient. We recommend collecting sufficient information on switching behavior when switching cannot be avoided.

A Systematic Review on Cost-effectiveness Studies Evaluating Ovarian Cancer Early Detection and Prevention Strategies.

Ovarian cancer imposes a substantial health and economic burden. We systematically reviewed current health-economic evidence for ovarian cancer early detection or prevention strategies. Accordingly, we searched relevant databases for cost-effectiveness studies evaluating ovarian cancer early detection or prevention strategies. Study characteristics and results including quality-adjusted life years (QALY), and incremental cost-effectiveness ratios (ICER) were summarized in standardized evidence tables. Economic results were transformed into 2017 Euros. The included studies (N = 33) evaluated ovarian cancer screening, risk-reducing interventions in women with heterogeneous cancer risks and genetic testing followed by risk-reducing interventions for mutation carriers. Multimodal screening with a risk-adjusted algorithm in postmenopausal women achieved ICERs of 9,800-81,400 Euros/QALY, depending on assumptions on mortality data extrapolation, costs, test performance, and screening frequency. Cost-effectiveness of risk-reducing surgery in mutation carriers ranged from cost-saving to 59,000 Euros/QALY. Genetic testing plus risk-reducing interventions for mutation carriers ranged from cost-saving to 54,000 Euros/QALY in women at increased mutation risk. Our findings suggest that preventive surgery and genetic testing plus preventive surgery in women at high risk for ovarian cancer can be considered effective and cost-effective. In postmenopausal women from the general population, multimodal screening using a risk-adjusted algorithm may be cost-effective.

Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment.

Aim: The aim of this project is to describe a causal (counterfactual) approach for analyzing when to start statin treatment to prevent cardiovascular disease using real-world evidence. Methods: We use directed acyclic graphs to operationalize and visualize the causal research question considering selection bias, potential time-independent and time-dependent confounding. We provide a study protocol following the 'target trial' approach and describe the data structure needed for the causal assessment. Conclusion: The study protocol can be applied to real-world data, in general. However, the structure and quality of the database play an essential role for the validity of the results, and database-specific potential for bias needs to be explicitly considered.

Treatment for hepatitis C virus in human immunodeficiency virus-infected patients: clinical benefits and cost-effectiveness.

BACKGROUND: Hepatitis C virus (HCV) is an important cause of liver disease in human immunodeficiency virus (HIV)-infected patients. OBJECTIVE: To assess the cost-effectiveness of alternative management strategies for chronic HCV in co-infected patients with moderate hepatitis. METHODS: A state-transition model was used to simulate a cohort of HIV-infected patients with a mean CD4 cell count of 350 cells/ micro L and moderate chronic hepatitis C stratified by genotype. Strategies included interferon alfa (48 weeks), pegylated interferon alfa (48 weeks), interferon alfa and ribavirin (24 and 48 weeks), pegylated interferon alfa and ribavirin (48 weeks), and no treatment. Outcomes included life expectancy, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios. RESULTS: Treatment for moderate chronic HCV with combination therapy using an interferon-based regimen reduced the incidence of cirrhosis and provided gains in quality-adjusted life expectancy ranging from 6.2 to 13.9 months, depending on genotype. Regardless of genotype, the cost-effectiveness of interferon alfa and ribavirin for patients with moderate hepatitis was lower than $50 000 per QALY vs the next best strategy. With genotype 1, pegylated interferon alfa (vs interferon alfa) and ribavirin therapy provided an additional 1.6 quality-adjusted life-months for $40 000 per QALY. Because treatment is more effective with non-1 genotypes, pegylated interferon (vs interferon alfa) and ribavirin provided only 3 additional quality-adjusted life-months for $105 300 per QALY. For patients who were intolerant of ribavirin, monotherapy with pegylated interferon was always the most cost-effective option. CONCLUSIONS: Combination therapy for moderate hepatitis in coinfected patients will increase quality-adjusted life expectancy and have a cost-effectiveness ratio comparable to that of other well-accepted clinical interventions.