A comparison of two approaches to implementing propensity score methods following multiple imputation

Background. In observational research on causal effects, missing data and confounding are very common problems. Multiple imputation and propensity score methods have gained increasing interest as methods to deal with these, but despite their popularity methodologists have mainly focused on how they perform in isolation.   Methods. We studied two approaches to implementing propensity score methods following multiple imputation, both of which have been used in applied research, and compared their performance by way of Monte Carlo simulation for a continuous outcome and partially unobserved covariate, treatment or outcome data. In the first, so-called Within, approach, propensity score analysis is performed within each of m imputed datasets, and the resulting m effect estimates are averaged. In the Across approach, for each subject the m estimated propensity scores are averaged first, after which the propensity score method is implemented based on each subject’s average propensity score. Because of its common use, complete case analysis was also implemented. Five propensity score estimators were studied, including regression, matching, and inverse probability weighting.   Results. The Within approach was found to be superior to the Across approach in terms of bias as well as variance in settings with missing covariate data, when missing data were missing at random as well as when they were missing completely at random. In settings with incomplete treatment or outcome values only, the Within and Across approaches yielded similar results. Complete case analysis was generally least efficient and unbiased only in scenarios where missing data were missing completely at random.   Conclusion. We advise researchers not to use the Across approach as the default method, because even when data are missing completely at random, this may yield biased effect estimates. Instead, the Within is the preferred approach when implementing propensity score methods following multiple imputation

Patient-derived head and neck cancer organoids allow treatment stratification and serve as a tool for biomarker validation and identification

Background: Organoids are in vitro three-dimensional structures that can be grown from patient tissue. Head and neck cancer (HNC) is a collective term used for multiple tumor types including squamous cell carcinomas and salivary gland adenocarcinomas.Methods: Organoids were established from HNC patient tumor tissue and characterized using immunohistochemistry and DNA sequencing. Organoids were exposed to chemo- and radiotherapy and a panel of targeted agents. Organoid response was correlated with patient clinical response. CRISPR-Cas9-based gene editing of organoids was applied for biomarker validation.Findings: A HNC biobank consisting of 110 models, including 65 tumor models, was generated. Organoids retained DNA alterations found in HNC. Comparison of organoid and patient response to radiotherapy (primary [n = 6] and adjuvant [n = 15]) indicated potential for guiding treatment options in the adjuvant setting. In organoids, the radio-sensitizing potential of cisplatin and carboplatin could be validated. However, cetuximab conveyed radioprotection in most models. HNC-targeted treatments were tested on 31 models, indicating possible novel treatment options with the potential for treatment stratification in the future. Activating PIK3CA mutations did not predict alpelisib response in organoids. Protein arginine methyltransferase 5 (PRMT5) inhibitors were identified as a potential treatment option for cyclin-dependent kinase inhibitor 2A (CDKN2A) null HNC.Conclusions: Organoids hold potential as a diagnostic tool in personalized medicine for HNC. In vitro organoid response to radiotherapy (RT) showed a trend that mimics clinical response, indicating the predictive potential of patient-derived organoids. Moreover, organoids could be used for biomarker discovery and validation.</p

Patient-derived head and neck cancer organoids allow treatment stratification and serve as a tool for biomarker validation and identification

Background: Organoids are in vitro three-dimensional structures that can be grown from patient tissue. Head and neck cancer (HNC) is a collective term used for multiple tumor types including squamous cell carcinomas and salivary gland adenocarcinomas. Methods: Organoids were established from HNC patient tumor tissue and characterized using immunohistochemistry and DNA sequencing. Organoids were exposed to chemo- and radiotherapy and a panel of targeted agents. Organoid response was correlated with patient clinical response. CRISPR-Cas9-based gene editing of organoids was applied for biomarker validation. Findings: A HNC biobank consisting of 110 models, including 65 tumor models, was generated. Organoids retained DNA alterations found in HNC. Comparison of organoid and patient response to radiotherapy (primary [n = 6] and adjuvant [n = 15]) indicated potential for guiding treatment options in the adjuvant setting. In organoids, the radio-sensitizing potential of cisplatin and carboplatin could be validated. However, cetuximab conveyed radioprotection in most models. HNC-targeted treatments were tested on 31 models, indicating possible novel treatment options with the potential for treatment stratification in the future. Activating PIK3CA mutations did not predict alpelisib response in organoids. Protein arginine methyltransferase 5 (PRMT5) inhibitors were identified as a potential treatment option for cyclin-dependent kinase inhibitor 2A (CDKN2A) null HNC. Conclusions: Organoids hold potential as a diagnostic tool in personalized medicine for HNC. In vitro organoid response to radiotherapy (RT) showed a trend that mimics clinical response, indicating the predictive potential of patient-derived organoids. Moreover, organoids could be used for biomarker discovery and validation

Improved clinical investigation and evaluation of high-risk medical devices: the rationale and objectives of CORE-MD (Coordinating Research and Evidence for Medical Devices)

: In the European Union (EU) the delivery of health services is a national responsibility but there are concerted actions between member states to protect public health. Approval of pharmaceutical products is the responsibility of the European Medicines Agency, whereas authorizing the placing on the market of medical devices is decentralized to independent 'conformity assessment' organizations called notified bodies. The first legal basis for an EU system of evaluating medical devices and approving their market access was the medical device directives, from the 1990s. Uncertainties about clinical evidence requirements, among other reasons, led to the EU Medical Device Regulation (2017/745) that has applied since May 2021. It provides general principles for clinical investigations but few methodological details-which challenges responsible authorities to set appropriate balances between regulation and innovation, pre- and post-market studies, and clinical trials and real-world evidence. Scientific experts should advise on methods and standards for assessing and approving new high-risk devices, and safety, efficacy, and transparency of evidence should be paramount. The European Commission recently awarded a Horizon 2020 grant to a consortium led by the European Society of Cardiology and the European Federation of National Associations of Orthopaedics and Traumatology, that will review methodologies of clinical investigations, advise on study designs, and develop recommendations for aggregating clinical data from registries and other real-world sources. The CORE-MD project (Coordinating Research and Evidence for Medical Devices) will run until March 2024; here we describe how it may contribute to the development of regulatory science in Europe

A comparison of approaches to implementing propensity score methods following multiple imputation

Background: In observational research on causal effects, missing data and confounding are very common problems. Multiple imputation and propensity score methods have gained increasing interest as methods to deal with these, but despite their popularity methodologists have mainly focused on how they perform in isolation. Methods: We studied two approaches to implementing propensity score methods following multiple imputation, both of which have been used in applied research, and compared their performance by way of Monte Carlo simulation for a continuous outcome and partially unobserved covariate, treatment or outcome data. In the first, so-called Within, approach, propensity score analysis is performed within each of m imputed datasets, and the resulting m effect estimates are averaged. In the Across approach, for each subject the m estimated propensity scores are averaged first, after which the propensity score method is implemented based on each subject’s average propensity score. Because of its common use, complete case analysis was also implemented. Five propensity score estimators were studied, including regression, matching, and inverse probability weighting. Results: The Within approach was found to be superior to the Across approach in terms of bias as well as variance in settings with missing covariate data, when missing data were missing at random as well as when they were missing completely at random. In settings with incomplete treatment or outcome values only, the Within and Across approaches yielded similar results. Complete case analysis was generally least efficient and unbiased only in scenarios where missing data were missing completely at random. Conclusion: We advise researchers not to use the Across approach as the default method, because even when data are missing completely at random, this may yield biased effect estimates. Instead, the Within is the preferred approach when implementing propensity score methods following multiple imputation

Cautionary note : Propensity score matching does not account for bias due to censoring

This article gives a review of the limitations of propensity score matching as a tool for confounding control in the presence of censoring. Using an illustrative simulation study, we emphasize the importance of explicit adjustment for selective loss to follow-up and explain how this may be achieved

Atmospheric Pressure and Abdominal Aortic Aneurysm Rupture : Results from a Time Series Analysis and Case-Crossover Study

Background: Associations between atmospheric pressure and abdominal aortic aneurysm (AAA) rupture risk have been reported, but empirical evidence is inconclusive and largely derived from studies that did not account for possible nonlinearity, seasonality, and confounding by temperature. Methods: Associations between atmospheric pressure and AAA rupture risk were investigated using local meteorological data and a case series of 358 patients admitted to hospital for ruptured AAA during the study period, January 2002 to December 2012. Two analyses were performed - a time series analysis and a case-crossover study. Results: Results from the 2 analyses were similar; neither the time series analysis nor the case-crossover study showed a significant association between atmospheric pressure (P =.627 and P =.625, respectively, for mean daily atmospheric pressure) or atmospheric pressure variation (P =.464 and P =.816, respectively, for 24-hour change in mean daily atmospheric pressure) and AAA rupture risk. Conclusion: This study failed to support claims that atmospheric pressure causally affects AAA rupture risk. In interpreting our results, one should be aware that the range of atmospheric pressure observed in this study is not representative of the atmospheric pressure to which patients with AAA may be exposed, for example, during air travel or travel to high altitudes in the mountains. Making firm claims regarding these conditions in relation to AAA rupture risk is difficult at best. Furthermore, despite the fact that we used one of the largest case series to date to investigate the effect of atmospheric pressure on AAA rupture risk, it is possible that this study is simply too small to demonstrate a causal link

Atmospheric Pressure and Abdominal Aortic Aneurysm Rupture : Results from a Time Series Analysis and Case-Crossover Study

Background: Associations between atmospheric pressure and abdominal aortic aneurysm (AAA) rupture risk have been reported, but empirical evidence is inconclusive and largely derived from studies that did not account for possible nonlinearity, seasonality, and confounding by temperature. Methods: Associations between atmospheric pressure and AAA rupture risk were investigated using local meteorological data and a case series of 358 patients admitted to hospital for ruptured AAA during the study period, January 2002 to December 2012. Two analyses were performed - a time series analysis and a case-crossover study. Results: Results from the 2 analyses were similar; neither the time series analysis nor the case-crossover study showed a significant association between atmospheric pressure (P =.627 and P =.625, respectively, for mean daily atmospheric pressure) or atmospheric pressure variation (P =.464 and P =.816, respectively, for 24-hour change in mean daily atmospheric pressure) and AAA rupture risk. Conclusion: This study failed to support claims that atmospheric pressure causally affects AAA rupture risk. In interpreting our results, one should be aware that the range of atmospheric pressure observed in this study is not representative of the atmospheric pressure to which patients with AAA may be exposed, for example, during air travel or travel to high altitudes in the mountains. Making firm claims regarding these conditions in relation to AAA rupture risk is difficult at best. Furthermore, despite the fact that we used one of the largest case series to date to investigate the effect of atmospheric pressure on AAA rupture risk, it is possible that this study is simply too small to demonstrate a causal link

Missing Values, Measurement Error and Confounding in Epidemiologic Studies with a Continuous Outcome

This paper is now published at Journal of Clinical Epidemiology: https://www.jclinepi.com/article/S0895-4356(20)31175-6/pd