Variance estimators for weighted and stratified linear dose–response function estimators using generalized propensity score

2021 ◽  
Author(s):  
Valérie Garès ◽  
Guillaume Chauvet ◽  
David Hajage
Keyword(s):  
Propensity Score ◽  
Response Function ◽  
Dose Response ◽  
Variance Estimators ◽  
Linear Dose ◽  
Generalized Propensity Score ◽  
Dose Response Function

scholarly journals Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on survival or time-to-event outcomes

2018 ◽  
Vol 28 (8) ◽  
pp. 2348-2367
Author(s):  
Peter C Austin
Keyword(s):  
Propensity Score ◽  
Response Function ◽  
Dose Response ◽  
Covariate Adjustment ◽  
Time To Event ◽  
Probability Of Survival ◽  
Propensity Score Methods ◽  
Generalized Propensity Score ◽  
Dose Response Function ◽  
Do So

Propensity score methods are frequently used to estimate the effects of interventions using observational data. The propensity score was originally developed for use with binary exposures. The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (e.g. pack-years of cigarettes smoked, dose of medication, or years of education). We describe how the GPS can be used to estimate the effect of continuous exposures on survival or time-to-event outcomes. To do so we modified the concept of the dose–response function for use with time-to-event outcomes. We used Monte Carlo simulations to examine the performance of different methods of using the GPS to estimate the effect of quantitative exposures on survival or time-to-event outcomes. We examined covariate adjustment using the GPS and weighting using weights based on the inverse of the GPS. The use of methods based on the GPS was compared with the use of conventional G-computation and weighted G-computation. Conventional G-computation resulted in estimates of the dose–response function that displayed the lowest bias and the lowest variability. Amongst the two GPS-based methods, covariate adjustment using the GPS tended to have the better performance. We illustrate the application of these methods by estimating the effect of average neighbourhood income on the probability of survival following hospitalization for an acute myocardial infarction.

scholarly journals A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool

2022 ◽  
Vol 7 ◽  
pp. 7
Author(s):  
Robert Smith ◽  
Chloe Thomas ◽  
Hazel Squires ◽  
Elizabeth Goyder
Keyword(s):  
Physical Activity ◽  
Response Function ◽  
Dose Response ◽  
World Health ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Linear Dose ◽  
Monetary Benefit ◽  
Non Linear ◽  
Dose Response Function

IntroductionThe WHO-Europe’s Health Economic Assessment Tool is a tool used to estimatethe costs and benefits of changes in walking and cycling. Due to data limitationsthe tool’s physical activity module assumes a linear dose response relationship be-tween physical activity and mortality.MethodsThis study estimates baseline population physical activity distributions for 44 coun-tries included in the HEAT. It then compares, for three different scenarios, the re-sults generated by the current method, using a linear dose-response relationship,with results generated using a non-linear dose-response relationship.ResultsThe study finds that estimated deaths averted are relatively higher (lower) using thenon-linear effect in countries with less (more) active populations. This difference islargest for interventions which affect the activity levels of the least active the most.Since more active populations, e.g. in Eastern Europe, also tend to have lowerValue of a Statistical Life estimates the net monetary benefit estimated by the sce-narios are much higher in western-Europe than eastern-Europe.ConclusionsUsing a non-linear dose response function results in materially different estimateswhere populations are particularly inactive or particularly active. Estimating base-line distributions is possible with limited additional data requirements, although themethod has yet to be validated. Given the significant role of the physical activitymodule within the HEAT tool it is likely that in the evaluation of many interventionsthe monetary benefit estimates will be sensitive to the choice of the physical activitydose response function.

Methodology for Determining the Appropriateness of a Linear Dose-Response Function

Risk Analysis ◽  
2010 ◽  
Vol 31 (3) ◽  
pp. 345-350 ◽  
Author(s):  
Michael S. Williams ◽  
Eric D. Ebel ◽  
David Vose
Keyword(s):  
Response Function ◽  
Dose Response ◽  
Linear Dose ◽  
Dose Response Function

Propensity score-based methods for causal inference in observational studies with non-binary treatments

2020 ◽  
Vol 29 (3) ◽  
pp. 709-727
Author(s):  
Shandong Zhao ◽  
David A van Dyk ◽  
Kosuke Imai
Keyword(s):  
Propensity Score ◽  
Response Function ◽  
Dose Response ◽  
Propensity Scores ◽  
Model Misspecification ◽  
Average Treatment Effect ◽  
Treatment Regimes ◽  
Propensity Score Methods ◽  
Empirical Performance ◽  
Dose Response Function

Propensity score methods are a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. Such extensions have widened the applicability of propensity score methods and are indeed becoming increasingly popular themselves. In this article, we closely examine two methods that generalize propensity scores in this direction, namely, the propensity function (PF), and the generalized propensity score (GPS), along with two extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. On a theoretical level, the GPS and its extensions are advantageous in that they are designed to estimate the full dose response function rather than the average treatment effect that is estimated with the PF. We compare GPS with a new PF method, both of which estimate the dose response function. We illustrate our findings and proposals through simulation studies, including one based on an empirical study about the effect of smoking on healthcare costs. While our proposed PF-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation.

scholarly journals Encoding fear intensity in human sweat

2020 ◽  
Vol 375 (1800) ◽  
pp. 20190271 ◽  
Author(s):  
Jasper H. B. de Groot ◽  
Peter A. Kirk ◽  
Jay A. Gottfried
Keyword(s):  
Response Function ◽  
Dose Response ◽  
Physiological Data ◽  
Olfactory Communication ◽  
Film Clips ◽  
Combination Of Methods ◽  
Multivariate Pattern ◽  
And Control ◽  
Photo Ionization ◽  
Dose Response Function

Humans, like other animals, have an excellent sense of smell that can serve social communication. Although ample research has shown that body odours can convey transient emotions like fear, these studies have exclusively treated emotions as categorical , neglecting the question whether emotion quantity can be expressed chemically. Using a unique combination of methods and techniques, we explored a dose–response function: Can experienced fear intensity be encoded in fear sweat? Specifically, fear experience was quantified using multivariate pattern classification (combining physiological data and subjective feelings with partial least-squares-discriminant analysis), whereas a photo-ionization detector quantified volatile molecules in sweat. Thirty-six male participants donated sweat while watching scary film clips and control (calming) film clips. Both traditional univariate and novel multivariate analysis (100% classification accuracy; Q 2 : 0.76; R 2 : 0.79) underlined effective fear induction. Using their regression-weighted scores, participants were assigned significantly above chance (83% > 33%) to fear intensity categories (low–medium–high). Notably, the high fear group ( n = 12) produced higher doses of armpit sweat, and greater doses of fear sweat emitted more volatile molecules ( n = 3). This study brings new evidence to show that fear intensity is encoded in sweat (dose–response function), opening a field that examines intensity coding and decoding of other chemically communicable states/traits. This article is part of the Theo Murphy meeting issue ‘Olfactory communication in humans’.

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