Comparative binary outcome data are of fundamental interest in statistics and are often pooled in meta-analyses. Here we examine the simplest case where for each study there are two patient groups and a binary event of interest, giving rise to a series of $2 \times 2$ tables. A variety of measures of treatment effect are then available and are conventionally used in meta-analyses, such as the odds ratio, the risk ratio and the risk difference. Here we propose a new type of measure of treatment effect for this type of data that is very easily interpretable by lay audiences. We give the rationale for the new measure and we present three contrasting methods for computing its within-study variance so that it can be used in conventional meta-analyses. We then develop three alternative methods for random-effects meta-analysis that use our measure and we apply our methodolgy to some real examples. We conclude that our new measure is a fully viable alternative to existing measures. It has the advantage that its interpretation is especially simple and direct, so that its meaning can be more readily understood by those with little or no formal statistical training. This may be especially valuable when presenting `plain language summaries', such as those used by Cochrane.
Experiments often include multiple treatments, with the primary goal to compare the causal effects of those treatments. This study focuses on comparing the causal anatomies of multiple treatments through the use of causal mediation analysis. It proposes a novel set of comparative causal mediation (CCM) estimands that compare the mediation effects of different treatments via a common mediator. Further, it derives the properties of a set of estimators for the CCM estimands and shows these estimators to be consistent (or conservative) under assumptions that do not require the absence of unobserve…
Adjusting for Outcome Reporting Bias in Meta-analysis: A Multiple Imputation Approach
Epidemiology, Biostatistics and Prevention Institute (EBPI)（流行病学、生物统计学与预防研究所）；Center for Reproducible Science and Research Synthesis (CRS)（可重复科学与研究综合中心）；University of Zurich (UZH)（苏黎世大学）
This paper presents a protocol, or design, for the analysis of a comparative effectiveness evaluation of abiraterone acetate against enzalutamide, two drugs given to prostate cancer patients. The design explicitly make use of differences in prescription practices across 21 Swedish county councils for the estimation of the two drugs comparative effectiveness on overall mortality, pain and skeleton related events. The design requires that the county factor: (1) affects the probability to be treated (i.e. being prescribed abiraterone acetate instead of enzalutamide) but (2) is not otherwise corre…
Institute of Medical Technology, Peking University Health Science Center（北京大学人民医院医学技术研究所）；National Institute of Health Data Science (NIHDS), Peking University（北京大学国家健康数据科学研究院）；Chalmers University of Technology（挑战大学）；NIHDS, Peking University（北京大学国家健康数据科学研究院）；Visualization Research Center (VISUS), University of Stuttgart（斯图加特大学可视化研究中心）；iDV x Lab, Tongji University（同济大学iDV x实验室）；National Institute of Traditional Chinese Medicine Constitution and Preventive Treatment of Diseases, Beijing University of Chinese Medicine（北京中医药大学国家中医体质与疾病预防治疗研究所）
The preponderance of large-scale healthcare databases provide abundant opportunities for comparative effectiveness research. Evidence necessary to making informed treatment decisions often relies on comparing effectiveness of multiple treatment options on outcomes of interest observed in a small number of individuals. Causal inference with multiple treatments and rare outcomes is a subject that has been treated sparingly in the literature. This paper designs three sets of simulations, representative of the structure of our healthcare database study, and propose causal analysis strategies for s…
Survival outcomes are common in comparative effectiveness studies and require unique handling because they are usually incompletely observed due to right-censoring. A ``once for all'' approach for causal inference with survival outcomes constructs pseudo-observations and allows standard methods such as propensity score weighting to proceed as if the outcomes are completely observed. For a general class of model-free causal estimands with survival outcomes on user-specified target populations, we develop corresponding propensity score weighting estimators based on the pseudo-observations and es…
Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection process…
Causally Interpretable Meta-Mediation Analysis With Missing At Random Mediator and Outcome Data
EPILOGY, Institut Mondor of Biomedical Research, INSERM U955（INSERM U955 蒙多尔生物医学研究所 EPILOGY）；Université Paris-Est Créteil（巴黎东克雷泰伊大学）；Mines Paris, Université Paris Sciences Lettres（巴黎矿业学院，巴黎文理研究大学）；Department of Mathematics, Université du Québec à Montréal（蒙特利尔大学数学系）
Harvard University（哈佛大学）；Harvard Business School（哈佛商学院）
This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow-The-Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes are chosen by an adversary, synthetic control predictions of counterfactual outcomes for the treated unit perform almost as well as an oracle weighted average of control units' outcomes. Synthetic control on differenced data performs almost as well as oracle weighted difference-in-differences, potentially making it an attractive choice in prac…
A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso
Evidence supporting the current World Health Organization recommendations of early antiretroviral therapy (ART) initiation for adolescents is inconclusive. We leverage a large observational data and compare, in terms of mortality and CD4 cell count, the dynamic treatment initiation rules for HIV-infected adolescents. Our approaches extend the marginal structural model for estimating outcome distributions under dynamic treatment regimes (DTR), developed in Robins et al. (2008), to allow the causal comparisons of both specific regimes and regimes along a continuum. Furthermore, we propose strate…
Step-by-Step Guide to Conducting Meta-Analysis of Dichotomous Outcomes Using RevMan in Dental Research Step-by-Step Guide to Conducting Meta-Analysis of Dichotomous Outcomes Using RevMan in Dental Research
Ideally, a meta-analysis will summarize data from several unbiased studies. Here we consider the less than ideal situation in which contributing studies may be compromised by measurement error. Measurement error affects every study design, from randomized controlled trials to retrospective observational studies. We outline a flexible Bayesian framework for continuous outcome data which allows one to obtain appropriate point and interval estimates with varying degrees of prior knowledge about the magnitude of the measurement error. We also demonstrate how, if individual-participant data (IPD) a…
Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent betw…
Impact of clinical decision support systems (cdss) on clinical outcomes and healthcare delivery in low- and middle-income countries: protocol for a systematic review and meta-analysis
ICMR National Institute for Research in Digital Health & Data Science（印度国家数字健康与数据科学研究所）；ICMR Headquarters（印度国家医学研究委员会总部）；ICMR-RMRC Bhubaneshwar（印度国家医学研究委员会比哈尔邦研究中心）
Testing effect size homogeneity is an essential part when conducting a meta-analysis. Comparative studies of effect size homogeneity tests in case of binary outcomes are found in the literature, but no test has come out as an absolute winner. A alternative approach would be to carry out multiple effect size homogeneity tests on the same meta-analysis and combine the resulting dependent p-values. In this article we applied the correlated Lancaster method for dependent statistical tests. To investigate the proposed approach's performance, we applied eight different effect size homogeneity tests …
Publication bias occurs when the publication of research results depends not only on the quality of the research but also on its nature and direction. The consequence is that published studies may not be truly representative of all valid studies undertaken, and this bias may threaten the validity of systematic reviews and meta-analyses - on which evidence-based medicine increasingly relies. Multivariate meta-analysis has recently received increasing attention for its ability reducing potential bias and improving statistical efficiency by borrowing information across outcomes. However, detectin…
In precision medicine, Dynamic Treatment Regimes (DTRs) are treatment protocols that adapt over time in response to a patient's observed characteristics. A DTR is a set of decision functions that takes an individual patient's information as arguments and outputs an action to be taken. Building on observed data, the aim is to identify the DTR that optimizes expected patient outcomes. Multiple methods have been proposed for optimal DTR estimation with continuous outcomes. However, optimal DTR estimation with binary outcomes is more complicated and has received comparatively little attention. Sol…
An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs
Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several metho…
Identifying compromise solutions across multiple treatment benefit-harm profiles in multivariate network meta-analysis
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg（弗赖堡大学医学院医学生物计量与统计研究所）；Oslo Center for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo（奥斯陆大学生物统计系奥斯陆生物统计与流行病学中心）
In the comparative interrupted time series design (also called the method of difference-in-differences), the change in outcome in a group exposed to treatment in the periods before and after the exposure is compared to the change in outcome in a control group not exposed to treatment in either period. The standard difference-in-difference estimator for a comparative interrupted time series design will be biased for estimating the causal effect of the treatment if there is an interaction between history in the after period and the groups; for example, there is a historical event besides the sta…
Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study-level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta-analysis. The current study proposes a novel statistical approach, the Zero-inflation Bias Correction (ZIBC) method, that can account for the bias introduced when…
Multivariate meta-analysis can be adapted to a wide range of situations for multiple outcomes and multiple treatment groups when combining studies together. The within-study correlation between effect sizes is often assumed known in multivariate meta-analysis while it is not always known practically. In this paper, we propose a generic method to approximate the within-study covariance for effect sizes in multivariate meta-analysis and apply this method to the scenarios with multiple outcomes and one outcome with multiple treatment groups respectively.
