Multiple imputation procedures for estimating causal effects with multiple treatments with application to the comparison of healthcare providers

2021 ◽  
Author(s):  
Gabriella C. Silva ◽  
Roee Gutman
Keyword(s):  
Multiple Imputation ◽  
Healthcare Providers ◽  
Causal Effects ◽  
Multiple Treatments

scholarly journals Methodological review to develop a list of bias items used to assess reviews incorporating network meta-analysis: protocol and rationale

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e045987
Author(s):  
Carole Lunny ◽  
Andrea C Tricco ◽  
Areti-Angeliki Veroniki ◽  
Sofia Dias ◽  
Brian Hutton ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Methodological Quality ◽  
Meta Analysis ◽  
Healthcare Providers ◽  
Cochrane Library ◽  
Methodological Review ◽  
Multiple Treatment ◽  
Full Study ◽  
Multiple Treatments ◽  
Treatment Comparisons

IntroductionSystematic reviews with network meta-analysis (NMA; ie, multiple treatment comparisons, indirect comparisons) have gained popularity and grown in number due to their ability to provide comparative effectiveness of multiple treatments for the same condition. The methodological review aims to develop a list of items relating to biases in reviews with NMA. Such a list will inform a new tool to assess the risk of bias in NMAs, and potentially other reporting or quality checklists for NMAs which are being updated.Methods and analysisWe will include articles that present items related to bias, reporting or methodological quality, articles assessing the methodological quality of reviews with NMA, or papers presenting methods for NMAs. We will search Ovid MEDLINE, the Cochrane library and difficult to locate/unpublished literature. Once all items have been extracted, we will combine conceptually similar items, classifying them as referring to bias or to other aspects of quality (eg, reporting). When relevant, reporting items will be reworded into items related to bias in NMA review conclusions, and then reworded as signalling questions.Ethics and disseminationNo ethics approval was required. We plan to publish the full study open access in a peer-reviewed journal, and disseminate the findings via social media (Twitter, Facebook and author affiliated websites). Patients, healthcare providers and policy-makers need the highest quality evidence to make decisions about which treatments should be used in healthcare practice. Being able to critically appraise the findings of systematic reviews that include NMA is central to informed decision-making in patient care.

scholarly journals 1230Should multiple imputation be stratified by exposure group when estimating causal effects via outcome regression?

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Jiaxin Zhang ◽  
S. Ghazaleh Dashti ◽  
John B. Carlin ◽  
Katherine J. Lee ◽  
Margarita Moreno-Betancur
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Observational Studies ◽  
Controlled Trial ◽  
Simulated Data ◽  
Causal Effects ◽  
Exposure Group ◽  
Complete Case ◽  
Main Effect ◽  
Health Cohort Study

Abstract Background Outcome regression remains widely applied for estimating causal effects in observational studies, in which causal inference is conceptualised as emulating a randomized controlled trial (RCT). Multiple imputation (MI) is a commonly used method for handling missing data, but while in RCTs it has been shown that MI should be conducted by treatment group to reduce bias, whether imputation should be conducted by exposure group in observational studies has not been studied. Methods We conducted a simulation study to evaluate the performance of seven methods for handling missing data: Complete-case analysis (CCA), MI of main effect, MI with interactions (between exposure and: outcome, a strong confounder, outcome and a strong confounder, all incomplete), and MI conducted by exposure group. We simulated data based on an example from the Victorian Adolescent Health Cohort Study. Three exposure prevalences and seven outcome generation models were considered, the latter ranging from no interaction to strong-positive or negative exposure-confounder interaction. Various missingness scenarios were examined: with incomplete outcome only or also incomplete confounders, and three levels of complexity regarding the missingness mechanism. Results For all scenarios, MI by exposure led to the least bias, followed by MI approaches that included exposure-confounder interactions. Conclusions If MI is adopted in outcome regression, we recommend conducting MI by exposure group and, when not feasible, including exposure-confounder interactions in the imputation model. Key messages Similar to RCTs, MI should be conducted by exposure group when estimating average causal effects using outcome regression in observational studies.

scholarly journals A methodological review to develop a list of bias items used to assess reviews incorporating network meta-analysis: Protocol and rationale

2021 ◽  
Author(s):  
Carole Lunny ◽  
Areti Angeliki Veroniki ◽  
Andrea Tricco ◽  
Sofia Dias ◽  
Brian Hutton ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Methodological Quality ◽  
Meta Analysis ◽  
Healthcare Providers ◽  
Cochrane Library ◽  
Methodological Review ◽  
Multiple Treatment ◽  
Ovid Medline ◽  
Multiple Treatments ◽  
Treatment Comparisons

Abstract Introduction: Systematic reviews with network meta-analysis (NMA; i.e., multiple treatment comparisons, indirect comparisons) have gained popularity and grown in number due to their ability to provide comparative effectiveness of multiple treatments for the same condition. The methodological review aims to develop a list of items relating to biases in reviews with NMA. Such a list will inform a new tool to assess the risk of bias in NMAs, and potentially other reporting or quality checklists for NMAs which are being updated.Methods and Analysis: We will include articles that present items related to bias, reporting, or methodological quality, articles assessing the methodological quality of reviews with NMA, or papers presenting methods for NMAs. We will search Ovid MEDLINE, the Cochrane library, and difficult to locate/unpublished literature. Once all items have been extracted, we will combine conceptually similar items, classifying them as referring to bias or to other aspects of quality (e.g. reporting). When relevant, reporting items will be re-worded into items related to bias in NMA review conclusions, and then re-worded as signalling questions. Ethics and Dissemination: No ethics approval was required. Patients, healthcare providers and policy makers need the highest quality evidence to make decisions about which treatments should be used in healthcare practice. Being able to critically appraise the findings of systematic reviews that include NMA is central to informed decision-making in patient care.

scholarly journals Multi-Cause Effect Estimation with Disentangled Confounder Representation

2021 ◽  
Author(s):  
Jing Ma ◽  
Ruocheng Guo ◽  
Aidong Zhang ◽  
Jundong Li
Keyword(s):  
Real World ◽  
Fundamental Problem ◽  
Causal Effect ◽  
Causal Effects ◽  
Treatment Effect Estimation ◽  
Latent Representations ◽  
Real World Datasets ◽  
New Perspective ◽  
Multiple Treatments ◽  
Effect Estimation

One fundamental problem in causality learning is to estimate the causal effects of one or multiple treatments (e.g., medicines in the prescription) on an important outcome (e.g., cure of a disease). One major challenge of causal effect estimation is the existence of unobserved confounders -- the unobserved variables that affect both the treatments and the outcome. Recent studies have shown that by modeling how instances are assigned with different treatments together, the patterns of unobserved confounders can be captured through their learned latent representations. However, the interpretability of the representations in these works is limited. In this paper, we focus on the multi-cause effect estimation problem from a new perspective by learning disentangled representations of confounders. The disentangled representations not only facilitate the treatment effect estimation but also strengthen the understanding of causality learning process. Experimental results on both synthetic and real-world datasets show the superiority of our proposed framework from different aspects.

scholarly journals Matching estimators for causal effects of multiple treatments

2019 ◽  
Vol 29 (4) ◽  
pp. 1051-1066 ◽  
Author(s):  
Anthony D Scotina ◽  
Francesca L Beaudoin ◽  
Roee Gutman
Keyword(s):  
Motor Vehicle ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Treatment Groups ◽  
Matching Estimators ◽  
Vehicle Collision ◽  
Matching Estimator ◽  
Multiple Treatments ◽  
Inference Methods

Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcomes of all matches for each unit. With more than two treatment groups, however, estimation using matching requires additional techniques. In this paper, we propose a nearest-neighbors matching estimator for use with multiple, nominal treatments, and use simulations to show that this method is precise and has coverage levels that are close to nominal. In addition, we implement the proposed inference methods to examine the effects of different medication regimens on long-term pain for patients experiencing motor vehicle collision.

scholarly journals Clinical features and prognostic factors of intensive and non-intensive 1014 COVID-19 patients: an experience cohort from Alahsa, Saudi Arabia

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Saad Alhumaid ◽  
Abbas Al Mutair ◽  
Zainab Al Alawi ◽  
Khulud Al Salman ◽  
Nourah Al Dossary ◽  
...  
Keyword(s):  
White Blood Cells ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Shortness Of Breath ◽  
Icu Patients ◽  
Icu Admission ◽  
Care Services ◽  
Multiple Treatments ◽  
The Right ◽  
Diabetes And Obesity

Abstract Background COVID-19 is a worldwide pandemic and has placed significant demand for acute and critical care services on hospitals in many countries. Objectives To determine the predictors of severe COVID-19 disease requiring admission to an ICU by comparing patients who were ICU admitted to non-ICU groups. Methods A cohort study was conducted for the laboratory-confirmed COVID-19 patients who were admitted to six Saudi Ministry of Health’s hospitals in Alahsa, between March 1, 2020, and July 30, 2020, by reviewing patient’s medical records retrospectively. Results This cohort included 1014 patients with an overall mean age of 47.2 ± 19.3 years and 582 (57%) were males. A total of 205 (20%) of the hospitalized patients were admitted to the ICU. Hypertension, diabetes and obesity were the most common comorbidities in all study patients (27.2, 19.9, and 9%, respectively). The most prevalent symptoms were cough (47.7%), shortness of breath (35.7%) and fever (34.3%). Compared with non-ICU group, ICU patients had older age (p ≤ 0.0005) and comprised a higher proportion of the current smokers and had higher respiratory rates (p ≤ 0.0005), and more percentage of body temperatures in the range of 37.3–38.0 °C (p ≥ 0.0005); and had more comorbidities including diabetes (p ≤ 0.0005), hypertension (p ≥ 0.0005), obesity (p = 0.048), and sickle cell disease (p = 0.039). There were significant differences between the non-ICU and ICU groups for fever, shortness of breath, cough, fatigue, vomiting, dizziness; elevated white blood cells, neutrophils, alanine aminotransferase and alkaline aminotransferase, lactate dehydrogenase, and ferritin, and decreased hemoglobin; and proportion of abnormal bilateral chest CT images (p < 0.05). Significant differences were also found for multiple treatments (p < 0.05). ICU patients group had a much higher mortality rate than those with non-ICU admission (p ≤ 0.0005). Conclusion Identifying key clinical characteristics of COVID-19 that predict ICU admission and high mortality can be useful for frontline healthcare providers in making the right clinical decision under time-sensitive and resource-constricted environment.

scholarly journals Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas

2017 ◽  
Vol 32 (3) ◽  
pp. 432-454 ◽  
Author(s):  
Michael J. Lopez ◽  
Roee Gutman
Keyword(s):  
Causal Effects ◽  
New Ideas ◽  
Multiple Treatments
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