scholarly journals Statistical considerations in the design and analysis of non-inferiority trials with binary endpoints in the presence of non-adherence: a simulation study

2020 ◽  
Vol 4 ◽  
pp. 207 ◽  
Author(s):  
Yin Mo ◽  
Cherry Lim ◽  
Mavuto Mukaka ◽  
Ben S. Cooper
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Experimental Treatment ◽  
Inverse Probability Weighting ◽  
Alternative Methods ◽  
Sensitivity Analyses ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Instrumental Variable Estimation ◽  
The Impact

Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes, especially in non-inferiority trials. We performed simulations of a non-inferiority trial with a time-fixed treatment and a binary endpoint in order to: i) explore the impact of various patterns of non-adherence and analysis methods on treatment effect estimates; ii) quantify the probability of claiming non-inferiority when the experimental treatment effect is actually inferior; and iii) evaluate alternative methods such as inverse probability weighting and instrumental variable estimation. We found that the probability of concluding non-inferiority when the experimental treatment is actually inferior depends on whether non-adherence is due to confounding or non-confounding factors, and the actual treatments received by the non-adherent participants. With non-adherence, intention-to-treat analysis has a higher tendency to conclude non-inferiority when the experimental treatment is actually inferior under most patterns of non-adherence. This probability of concluding non-inferiority can be increased to as high as 0.1 from 0.025 when the adherence is relatively high at 90%. The direction of bias for the per-protocol analysis depends on the directions of influence the confounders have on adherence and probability of outcome. The inverse probability weighting approach can reduce bias but will only eliminate it if all confounders can be measured without error and are appropriately adjusted for. Instrumental variable estimation overcomes this limitation and gives unbiased estimates even when confounders are not known, but typically requires large sample sizes to achieve acceptable power. Investigators need to consider patterns of non-adherence and potential confounders in trial designs. Adjusted analysis of the per-protocol population with sensitivity analyses on confounders and other approaches, such as instrumental variable estimation, should be considered when non-compliance is anticipated. We provide an online power calculator allowing for various patterns of non-adherence using the above methods.

scholarly journals Statistical considerations in the design and analysis of non-inferiority trials with binary endpoints in the presence of non-adherence: a simulation study

2019 ◽  
Vol 4 ◽  
pp. 207
Author(s):  
Yin Mo ◽  
Cherry Lim ◽  
Mavuto Mukaka ◽  
Ben S. Cooper
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Experimental Treatment ◽  
Inverse Probability Weighting ◽  
Alternative Methods ◽  
Sensitivity Analyses ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Instrumental Variable Estimation ◽  
The Impact

Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes, especially in non-inferiority trials. We performed simulations of a non-inferiority trial with a time-fixed treatment and a binary endpoint in order to: i) explore the impact of various patterns of non-adherence and analysis methods on treatment effect estimates; ii) quantify the probability of claiming non-inferiority when the experimental treatment effect is actually inferior; and iii) evaluate alternative methods such as inverse probability weighting and instrumental variable estimation. We found that the probability of concluding non-inferiority when the experimental treatment is actually inferior depends on whether non-adherence is due to confounding or non-confounding factors, and the actual treatments received by the non-adherent participants. With non-adherence, intention-to-treat analysis has a higher tendency to conclude non-inferiority when the experimental treatment is actually inferior under most patterns of non-adherence. This probability of concluding non-inferiority can be increased to as high as 0.1 from 0.025 when the adherence is relatively high at 90%. The direction of bias for the per-protocol analysis depends on the directions of influence the confounders have on adherence and probability of outcome. The inverse probability weighting approach can reduce bias but will only eliminate it if all confounders can be measured without error and are appropriately adjusted for. Instrumental variable estimation overcomes this limitation and gives unbiased estimates even when confounders are not known, but typically requires large sample sizes to achieve acceptable power. Investigators need to consider patterns of non-adherence and potential confounders in trial designs. Adjusted analysis of the per-protocol population with sensitivity analyses on confounders and other approaches, such as instrumental variable estimation, should be considered when non-compliance is anticipated. We provide an online power calculator allowing for various patterns of non-adherence using the above methods.

Unifying instrumental variable and inverse probability weighting approaches for inference of causal treatment effect and unmeasured confounding in observational studies

2020 ◽  
pp. 096228022097183
Author(s):  
Tao Liu ◽  
Joseph W Hogan
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Observational Studies ◽  
Treatment Effects ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Unmeasured Confounding ◽  
Inverse Probability ◽  
Causal Treatment ◽  
Weighting Methods

Confounding is a major concern when using data from observational studies to infer the causal effect of a treatment. Instrumental variables, when available, have been used to construct bound estimates on population average treatment effects when outcomes are binary and unmeasured confounding exists. With continuous outcomes, meaningful bounds are more challenging to obtain because the domain of the outcome is unrestricted. In this paper, we propose to unify the instrumental variable and inverse probability weighting methods, together with suitable assumptions in the context of an observational study, to construct meaningful bounds on causal treatment effects. The contextual assumptions are imposed in terms of the potential outcomes that are partially identified by data. The inverse probability weighting component incorporates a sensitivity parameter to encode the effect of unmeasured confounding. The instrumental variable and inverse probability weighting methods are unified using the principal stratification. By solving the resulting system of estimating equations, we are able to quantify both the causal treatment effect and the sensitivity parameter (i.e. the degree of the unmeasured confounding). We demonstrate our method by analyzing data from the HIV Epidemiology Research Study.

Causal inference with measurement error in outcomes: Bias analysis and estimation methods

2017 ◽  
Vol 28 (7) ◽  
pp. 2049-2068 ◽  
Author(s):  
Di Shu ◽  
Grace Y Yi
Keyword(s):  
Measurement Error ◽  
Treatment Effect ◽  
Inverse Probability Weighting ◽  
Average Treatment Effect ◽  
Estimation Methods ◽  
Outcome Variable ◽  
Probability Weighting ◽  
Validation Data ◽  
Inverse Probability ◽  
Average Treatment

Inverse probability weighting estimation has been popularly used to consistently estimate the average treatment effect. Its validity, however, is challenged by the presence of error-prone variables. In this paper, we explore the inverse probability weighting estimation with mismeasured outcome variables. We study the impact of measurement error for both continuous and discrete outcome variables and reveal interesting consequences of the naive analysis which ignores measurement error. When a continuous outcome variable is mismeasured under an additive measurement error model, the naive analysis may still yield a consistent estimator; when the outcome is binary, we derive the asymptotic bias in a closed-form. Furthermore, we develop consistent estimation procedures for practical scenarios where either validation data or replicates are available. With validation data, we propose an efficient method for estimation of average treatment effect; the efficiency gain is substantial relative to usual methods of using validation data. To provide protection against model misspecification, we further propose a doubly robust estimator which is consistent even when either the treatment model or the outcome model is misspecified. Simulation studies are reported to assess the performance of the proposed methods. An application to a smoking cessation dataset is presented.

scholarly journals Association between Hydroxyzine Use and Reduced Mortality in Patients Hospitalized for Coronavirus Disease 2019: Results from a multicenter observational study

2020 ◽  
Author(s):  
Nicolas Hoertel ◽  
Marina Sánchez ◽  
Raphaël Vernet ◽  
Nathanaël Beeker ◽  
Antoine Neuraz ◽  
...  
Keyword(s):  
Observational Study ◽  
Cox Model ◽  
Inverse Probability Weighting ◽  
Dose Effect ◽  
Sensitivity Analyses ◽  
Study Endpoint ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Effect Relationship ◽  
Anti Inflammatory

ABSTRACTObjectiveTo examine the association between hydroxyzine use and mortality in patients hospitalized for COVID-19, based on its anti-inflammatory and antiviral properties.DesignMulticenter observational retrospective cohort study.SettingGreater Paris University hospitals, France.Participants7,345 adults hospitalized for COVID-19 between 24 January and 1 April 2020, including 138 patients (1.9%) who received hydroxyzine during the visit at a mean dose of 49.8 mg (SD=51.5) for an average of 22.4 days (SD=25.9).Data sourceAssistance Publique-Hôpitaux de Paris Health Data Warehouse.Main outcome measuresThe study endpoint was death. We compared this endpoint between patients who received hydroxyzine and those who did not in time-to-event analyses adjusting for patient characteristics (such as age, sex, and comorbidities), clinical and biological markers of disease’s severity, and use of other medications. The primary analysis was a multivariable Cox model with inverse probability weighting. Sensitivity analyses included a multivariable Cox model and a univariate Cox regression model in a matched analytic sample in a 1:1 ratio.ResultsOver a mean follow-up of 20.3 days (SD=27.5), 994 patients (13.5%) had a primary end-point event. The primary multivariable analysis with inverse probability weighting showed a significant association between hydroxyzine use and reduced mortality (HR, 0.42; 95% CI, 0.25 to 0.71; p=0.001) with a significant dose-effect relationship (HR, 0.10; 95% CI, 0.02 to 0.45; p=0.003). This association was similar in sensitivity analyses. In secondary analyses conducted among subsamples of patients, we found a significant association between hydroxyzine use and a faster decrease in biological inflammatory markers associated with COVID-19-related mortality, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-C-reactive protein ratio (LCRP), and circulating interleukin 6 levels (IL-6) (all p<0.016), with a significant dose-effect relationship for NLR and LCRP (both p<0.037).ConclusionsIn this retrospective observational study, hydroxyzine use was associated with reduced mortality in patients hospitalized for COVID-19. This association may be partially mediated by specific anti-inflammatory properties of H1 antihistamines. Double-blind controlled randomized clinical trials of hydroxyzine for COVID-19 are needed to confirm these results.

The impact of COVID-19 on tourists’ length of stay and daily expenditures

2021 ◽  
pp. 135481662110534
Author(s):  
José F Baños-Pino ◽  
David Boto-García ◽  
Eduardo Del Valle ◽  
Inés Sustacha
Keyword(s):  
Length Of Stay ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Regression Adjustment ◽  
The Impact ◽  
Spanish Region

This study evaluates the effect of the COVID-19 pandemic on tourists’ length of stay and daily expenditures at a destination. The paper compares detailed microdata for visitors to a Northern Spanish region in the summer periods of 2019 (pre-pandemic) and 2020 (after the pandemic outbreak). We estimate the pandemic-induced impacts on the length of stay and expenditures per person for several categories using regression adjustment, inverse probability weighting regression and propensity score matching. We find clear evidence of a drop in the length of stay of around 1.26 nights, representing a 23.8% decline. We also show that, although total expenditures per person and day have remained constant, there has been a change in the allocations for categories in the tourism budget.

Propensity score weighting analysis and treatment effect discovery

2018 ◽  
Vol 28 (8) ◽  
pp. 2439-2454 ◽  
Author(s):  
Huzhang Mao ◽  
Liang Li ◽  
Tom Greene
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Statistical Power ◽  
Variance Estimation ◽  
Propensity Score Analysis ◽  
Inverse Probability Weighting ◽  
Average Treatment Effect ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Average Treatment

Inverse probability weighting can be used to estimate the average treatment effect in propensity score analysis. When there is lack of overlap in the propensity score distributions between the treatment groups under comparison, some weights may be excessively large, causing numerical instability and bias in point and variance estimation. We study a class of modified inverse probability weighting estimators that can be used to avoid this problem. These weights cause the estimand to deviate from the average treatment effect. We provide some justification for this deviation from the perspective of treatment effect discovery. We show that when lack of overlap occurs, the modified weights can achieve substantial gains in statistical power compared with inverse probability weighting and other propensity score methods. We develop analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness. Results from extensive simulations and a real data application support our conclusions. The proposed methodology is implemented in R package PSW.

scholarly journals Propensity score weighting under limited overlap and model misspecification

2020 ◽  
Vol 29 (12) ◽  
pp. 3721-3756
Author(s):  
Yunji Zhou ◽  
Roland A Matsouaka ◽  
Laine Thomas
Keyword(s):  
Propensity Score ◽  
Mean Squared Error ◽  
Specific Treatment ◽  
Inverse Probability Weighting ◽  
Alternative Methods ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Wide Range

Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the propensity score distributions between treatment groups. When these practical violations occur, a small number of highly influential inverse probability weights may lead to unstable inverse probability weighting estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including inverse probability weighting trimming, overlap weights, matching weights, and entropy weights. Because overlap weights, matching weights, and entropy weights target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true propensity score, a common criticism is that these estimators may be more sensitive to misspecifications of the propensity score model. In this paper, we conduct extensive simulation studies to compare the performances of inverse probability weighting and inverse probability weighting trimming against those of overlap weights, matching weights, and entropy weights under limited overlap and misspecified propensity score models. Across the wide range of scenarios we considered, overlap weights, matching weights, and entropy weights consistently outperform inverse probability weighting in terms of bias, root mean squared error, and coverage probability.

scholarly journals Impact of Adopting Improved Seeds on Access to Broader Food Groups Among Small-Scale Sorghum Producers in Tanzania

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402097999
Author(s):  
Aloyce R. Kaliba ◽  
Anne G. Gongwe ◽  
Kizito Mazvimavi ◽  
Ashagre Yigletu
Keyword(s):  
Food Security ◽  
Dietary Diversity ◽  
Sampling Technique ◽  
Inverse Probability Weighting ◽  
Reproductive Age ◽  
Small Scale ◽  
Probability Weighting ◽  
Food Groups ◽  
Inverse Probability ◽  
Double Robust

In this study, we use double-robust estimators (i.e., inverse probability weighting and inverse probability weighting with regression adjustment) to quantify the effect of adopting climate-adaptive improved sorghum varieties on household and women dietary diversity scores in Tanzania. The two indicators, respectively, measure access to broader food groups and micronutrient and macronutrient availability among children and women of reproductive age. The selection of sample households was through a multistage sampling technique, and the population was all households in the sorghum-producing regions of Central, Northern, and Northwestern Tanzania. Before data collection, enumerators took part in a 1-week training workshop and later collected data from 822 respondents using a structured questionnaire. The main results from the study show that the adoption of improved sorghum seeds has a positive effect on both household and women dietary diversity scores. Access to quality food groups improves nutritional status, food security adequacy, and general welfare of small-scale farmers in developing countries. Agricultural projects that enhance access to improved seeds are, therefore, likely to generate a positive and sustainable effect on food security and poverty alleviation in sorghum-producing regions of Tanzania.

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