Подтвердите e-mail

Для публикаций, комментариев, реакций и сообщений подтвердите адрес.

←

Публикация

Noah Greifer

I'm so happy to announce version 2.0.0 of my #Rstats package WeightIt is out on CRAN! New features: censoring weights, multilevel propensity scores, improved weights for continuous treatments, bias-reduced ordinal and multinomial models, M-estimation in subgroups Check out the website below!

Weighting for Covariate Balance in Observational StudiesGenerates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality ...ngreifer.github.io
Обсуждение

3 прямых ответов · 12 сообщений

Erik Westlund

Well done.

Ответ для Noah Greifer
Noah Greifer

- Censoring weights WeightIt now supports the estimation of censoring weights, as part of a point or longitudinal treatment. To identify that a variable is a censoring indicator, its model formula should have the .cens() marker, e.g., .cens(C) ~ X1 + X2. M-estimation SEs are supported!

Mark a censoring indicator in a model formula — .cens.cens() marks a variable as a censoring indicator rather than a treatment, requesting inverse probability of censoring weights (IPCW). It is most often used on the left side of a formula supplied to w...ngreifer.github.io
Ответ для Noah Greifer
Noah Greifer

- Multilevel PS Propensity scores can be estimated incorporating random effects to model, e.g., students nested within schools. The model formula is passed to lme4::glmer(). All treatment types are supported, as are BART-propensity scores with random effects through the stan4bart package.

Propensity Score Weighting Using Generalized Linear Models — method_glmThis page explains the details of estimating weights from generalized linear model-based propensity scores by setting method = "glm" in the call to weightit() or weightitMSM(). This method can be used...ngreifer.github.io
Ответ для Noah Greifer
Noah Greifer

- Weights for continuous treatments Weights need estimates of the marginal density as well as the conditional density. Previously, the same density was used for both. Now, the marginal density is computed by marginalizing over the conditional density, yielding better weights.

Ответ для Noah Greifer
Noah Greifer

- Bias-reduced models Ordinal and multinomial models fit through ordinal_weightit() and multinom_weightit() now can be bias-reduced using corrections by Firth and @ikosmidis.com, which prevents infinite estimates and improves over MLE in bias and variance. M-estimation supported!

Ответ для Noah Greifer
Noah Greifer

- M-estimation in subgroups Previously, using the `by` argument to estimate weights in subgroups meant you couldn't use M-estimation. Now, M-estimation is supported, which should facilitate subgroup and moderation analysis.

Ответ для Noah Greifer
Noah Greifer

I hope you find these updates to WeightIt useful! If you have any comments, feature requests, or bug reports, please get in touch! #Rstats #Statssky #EpiSky #Causalinference

WeightIt: Weighting for Covariate Balance in Observational StudiesGenerates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality ...cran.r-project.org
Ответ для Noah Greifer
Pausal Zivference

I understand the emoji hint now lol

sylvie tuder 🏳️‍🌈🇵🇸

Do you have any insight on this package’s functionality with Lumley’s “survey”? Put differently—does WeightIt work with design-based analysis w/ complex survey data?

Ответ для sylvie tuder 🏳️‍🌈🇵🇸
Noah Greifer

Yup! It doesn't accept svydesign objects, but you can supply sampling to weights to almost all weighting methods via the `s.weights` argument to weightit(), and you can adjust for clustering in the outcome models using the `cluster` argument to, e.g., glm_weightit(). See here:

Estimating Effects After Weightingngreifer.github.io