scholarly journals Blocking for Sequential Political Experiments

2013 ◽  
Vol 21 (4) ◽  
pp. 507-523 ◽  
Author(s):  
Ryan T. Moore ◽  
Sally A. Moore
Keyword(s):  
Clinical Trial ◽  
Causal Effect ◽  
Simulated Data ◽  
Randomization Test ◽  
Covariate Balance ◽  
Treatment Conditions ◽  
Continuous Covariates ◽  
Background Data ◽  
The Rich ◽  
Political Trials

In typical political experiments, researchers randomize a set of households, precincts, or individuals to treatments all at once, and characteristics of all units are known at the time of randomization. However, in many other experiments, subjects “trickle in” to be randomized to treatment conditions, usually via complete randomization. To take advantage of the rich background data that researchers often have (but underutilize) in these experiments, we develop methods that use continuous covariates to assign treatments sequentially. We build on biased coin and minimization procedures for discrete covariates and demonstrate that our methods outperform complete randomization, producing better covariate balance in simulated data. We then describe how we selected and deployed a sequential blocking method in a clinical trial and demonstrate the advantages of our having done so. Further, we show how that method would have performed in two larger sequential political trials. Finally, we compare causal effect estimates from differences in means, augmented inverse propensity weighted estimators, and randomization test inversion.

scholarly journals Low intratumor heterogeneity correlates with increased response to PD-1 blockade in renal cell carcinoma

2020 ◽  
Vol 12 ◽  
pp. 175883592097711
Author(s):  
Xia Ran ◽  
Jinyuan Xiao ◽  
Yi Zhang ◽  
Huajing Teng ◽  
Fang Cheng ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Checkpoint Inhibitors ◽  
Simulated Data ◽  
The Cancer Genome Atlas ◽  
Intratumor Heterogeneity ◽  
Cell Renal Cell Carcinoma ◽  
Immune Activity

Background: Intratumor heterogeneity (ITH) has been shown to be inversely associated with immune infiltration in several cancers including clear cell renal cell carcinoma (ccRCC), but it remains unclear whether ITH is associated with response to immunotherapy (e.g. PD-1 blockade) in ccRCC. Methods: We quantified ITH using mutant-allele tumor heterogeneity, investigated the association of ITH with immune parameters in patients with ccRCC ( n = 336) as well as those with papillary RCC (pRCC, n = 280) from The Cancer Genome Atlas, and validations were conducted in patients with ccRCC from an independent cohort ( n = 152). The relationship between ITH and response to anti-PD-1 immunotherapy was explored in patients with metastatic ccRCC from a clinical trial of anti-PD-1 therapy ( n = 35), and validated in three equal-size simulated data sets ( n = 60) generated by random sampling with replacement based on this clinical trial cohort. Results: In ccRCC, low ITH was associated with better survival, more reductions in tumor burden, and clinical benefit of anti-PD-1 immunotherapy through modulating immune activity involving more neoantigens, elevated expression of HLA class I genes, and higher abundance of dendritic cells. Furthermore, we found that the association between the level of ITH and response to PD-1 blockade was independent of the mutation status of PBRM1 and that integrating both factors performed better than the individual predictors in predicting the benefit of anti-PD-1 immunotherapy in ccRCC patients. In pRCC, increased immune activity was also observed in low- versus high-ITH tumors, including higher neoantigen counts, increased abundance of monocytes, and decreased expression of PD-L1 and PD-L2. Conclusions: ITH may be helpful in the identification of patients who could benefit from PD-1 blockade in ccRCC, and even in pRCC where no genomic metrics has been found to correlate with response to immune checkpoint inhibitors.

scholarly journals Women's Participation in the Australian Digital Content Industry

2014 ◽  
pp. 125-142
Author(s):  
Anitza Geneve
Keyword(s):  
Agency Theory ◽  
Empirical Data ◽  
Causal Effect ◽  
Digital Content ◽  
Women's Participation ◽  
Digital Content Industry ◽  
Australian Case ◽  
The Rich ◽  
Insight Into

There is a need to understand the phenomenon of women's under-representation in the Australian Digital Content Industry (DCI) workforce. This chapter presents the findings from an Australian case study where both women working in the industry and industry stakeholders were interviewed for their insight into the influences on women's participation. The rich empirical data and findings from the case study are interpreted using the Acts of Agency theory—an original theory by the author of this chapter. As the chapter reveals there are five ‘Acts of Agency' (containing 10 agent-driven mechanisms) identified as influencing women's participation. Agent-driven mechanisms recognise the causal effect of people themselves; that is, the role individuals play in their participation.

Observed effects of education on intelligence in longitudinal data may be spurious: a formal model and reanalysis

2021 ◽  
Author(s):  
Kimmo Eriksson ◽  
Kimmo Sorjonen ◽  
Daniel Falkstedt ◽  
Bo Melin ◽  
Gustav Nilsonne
Keyword(s):  
Higher Education ◽  
Longitudinal Data ◽  
Measurement Errors ◽  
Basic Education ◽  
Formal Model ◽  
Causal Effect ◽  
Simulated Data ◽  
School Age ◽  
Policy Changes ◽  
Zero Effect

Effects of education on intelligence are controversial. Earlier studies of longitudinal data have observed positive associations between level of education and a later measurement of intelligence, when statistically controlling for an earlier measurement of intelligence, and furthermore that this association is stronger among individuals with lower pre-education intelligence. Here we challenge the interpretation that these observations reflect a causal effect of education. We develop and analyze a mathematical model in which education is assumed to have zero effect on intelligence, showing that precisely the observed pattern of results arises as a statistical artefact due to measurement errors. Fitting our model to a dataset used in a prior study, we show that observed associations between education and intelligence are closely replicated in simulated data generated by our model. Thus, our reanalysis indicates that additional higher education does not cause an increase in intelligence. We discuss how positive findings in studies of policy changes and school-age cutoff are limited to basic education and may not generalize to higher education.

Empirical Bayes Microarray ANOVA and Grouping Cell Lines by Equal Expression Levels

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Ingrid Lönnstedt ◽  
Rebecca Rimini ◽  
Peter Nilsson
Keyword(s):  
Cell Lines ◽  
Empirical Bayes ◽  
Dna Microarrays ◽  
Linear Models ◽  
Simulated Data ◽  
Probabilistic Methods ◽  
Expression Levels ◽  
Statistical Framework ◽  
Treatment Conditions ◽  
One Way Anova

In the exploding field of gene expression techniques such as DNA microarrays, there are still few general probabilistic methods for analysis of variance. Linear models and ANOVA are heavily used tools in many other disciplines of scientific research. The usual F-statistic is unsatisfactory for microarray data, which explore many thousand genes in parallel, with few replicates.We present three potential one-way ANOVA statistics in a parametric statistical framework. The aim is to separate genes that are differently regulated across several treatment conditions from those with equal regulation. The statistics have different features and are evaluated using both real and simulated data. Our statistic B1 generally shows the best performance, and is extended for use in an algorithm that groups cell lines by equal expression levels for each gene. An extension is also outlined for more general ANOVA tests including several factors.The methods presented are implemented in the freely available statistical language R. They are available at http://www.math.uu.se/staff/pages/?uname=ingrid.

scholarly journals A Critique of the Random Intercept Cross-Lagged Panel Model

2021 ◽  
Author(s):  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Causal Effect ◽  
Formal Analysis ◽  
Simulated Data ◽  
Psychological Constructs ◽  
Delayed Effects ◽  
Panel Model ◽  
Random Intercept ◽  
Lagged Effects ◽  
The Individual ◽  
Lagged Effect

The random intercept cross-lagged panel model (RI-CLPM) is an extension of the traditional cross-lagged panel model (CLPM) that allows controlling for stable trait factors when estimating cross-lagged effects. It has been argued that the RI-CLPM more appropriately accounts for trait-like, time-invariant stability of many psychological constructs and that it should be preferred over the CLPM when at least three waves of measurement are available. The basic idea of the RI-CLPM is to decompose longitudinal associations between two constructs into stable between-person associations and temporal within-person dynamics. The present article critically examines the RI-CLPM from a causal inference perspective. Using formal analysis and simulated data, we show that the RI-CLPM has limited potential to control for unobserved stable confounder variables when estimating cross-lagged effects. The CLPM with additional lag-2 effects sufficiently controls for delayed effects, as long as all relevant covariates are measured. Furthermore, we clarify that, in general, the RI-CLPM targets a different causal estimand than the CLPM. Whereas the cross-lagged effect in the CLPM targets the effect of increasing the exposure by one unit, the within-person cross-lagged effect in the RI-CLPM provides an estimate of the effect of increasing the exposure by one unit around the person mean. We argue that this within-person causal effect is typically less relevant for testing causal hypotheses with longitudinal data because it only captures temporary fluctuations around the individual person means and ignores the potential effects of causes that explain differences between persons.

scholarly journals An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Amal Almohisen ◽  
Robin Henderson ◽  
Arwa M. Alshingiti
Keyword(s):  
Clinical Trial ◽  
Sensitivity Analysis ◽  
Simulated Data ◽  
Real Data ◽  
Missing At Random ◽  
Parameter Estimates ◽  
Mixed Effect ◽  
Missing Not At Random ◽  
Missing Completely At Random ◽  
Sensitivity Approach

In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared Parameter (SP). In this paper we estimate the parameters of the longitudinal model for simulated data and real data using the Linear Mixed Effect (LME) method. We investigate the consequences of misspecifying the missingness mechanism by deriving the so-called least false values. These are the values the parameter estimates converge to, when the assumptions may be wrong. The knowledge of the least false values allows us to conduct a sensitivity analysis, which is illustrated. This method provides an alternative to a local misspecification sensitivity procedure, which has been developed for likelihood-based analysis. We compare the results obtained by the method proposed with the results found by using the local misspecification method. We apply the local misspecification and least false methods to estimate the bias and sensitivity of parameter estimates for a clinical trial example.

scholarly journals Correction for sample overlap, winner's curse and weak instrument bias in two-sample Mendelian Randomization

2021 ◽  
Author(s):  
Ninon Mounier ◽  
Zoltan Kutalik
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Simulated Data ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Winner's Curse ◽  
Weak Instruments ◽  
Winner’S Curse ◽  
Wide Range

Inverse-variance weighted two-sample Mendelian Randomization (IVW-MR) is the most widely used approach that uses genome-wide association studies summary statistics to infer the existence and strength of the causal effect between an exposure and an outcome. Estimates from this approach can be subject to different biases due to: (i) the overlap between the exposure and outcome samples; (ii) the use of weak instruments and winner's curse. We developed a method that aims at tackling all these biases together. Assuming spike-and-slab genomic architecture and leveraging LD-score regression and other techniques, we could analytically derive and reliably estimate the bias of IVW-MR using association summary statistics only. This allowed us to apply a bias correction to IVW-MR estimates, which we tested using simulated data for a wide range of realistic scenarios. In all the explored scenarios, our correction reduced the bias, in some situations by as much as 30 folds. When applied to real data on obesity-related exposures, we observed significant differences between IVW-based and corrected effects, both for non-overlapping and fully overlapping samples. While most studies are extremely careful to avoid any sample overlap when performing two-sample MR analysis, we have demonstrated that the incurred bias is much less substantial than the one due to weak instruments or winner's curse, which are often ignored.

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