sachsmc r-universe repositoryhttps://sachsmc.r-universe.devPackage updated in sachsmccranlike-server https://github.com/sachsmc.png?size=400sachsmc r-universe repositoryhttps://sachsmc.r-universe.devTue, 10 Sep 2024 16:48:34 GMT[sachsmc] stdReg2 1.0.1sachsmc@gmail.com (Michael C Sachs)Contains more modern tools for causal inference using
regression standardization. Four general classes of models are
implemented; generalized linear models, conditional generalized
estimating equation models, Cox proportional hazards models,
and shared frailty gamma-Weibull models. Methodological details
are described in Sjölander, A. (2016)
<doi:10.1007/s10654-016-0157-3>. Also includes functionality
for doubly robust estimation for generalized linear models in
some special cases, and the ability to implement custom models.https://github.com/r-universe/sachsmc/actions/runs/11268288559Tue, 10 Sep 2024 16:48:34 GMTstdReg21.0.1successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/stdReg2overview.Rmdoverview.htmlEstimation of causal effects using stdReg22023-11-07 10:23:092024-09-04 11:36:07custom.Rmdcustom.htmlImplementing custom and new methods for standardization2024-08-12 09:16:442024-09-04 09:40:17[sachsmc] eventglm 1.4.4sachsmc@gmail.com (Michael C Sachs)A user friendly, easy to understand way of doing event
history regression for marginal estimands of interest,
including the cumulative incidence and the restricted mean
survival, using the pseudo observation framework for
estimation. For a review of the methodology, see Andersen and
Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs and
Gabriel (2022) <doi:10.18637/jss.v102.i09>. The interface uses
the well known formulation of a generalized linear model and
allows for features including plotting of residuals, the use of
sampling weights, and corrected variance estimation.https://github.com/r-universe/sachsmc/actions/runs/11199103865Mon, 08 Jul 2024 11:32:42 GMTeventglm1.4.4successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/eventglmstata-sas-comparason.Rmdstata-sas-comparason.htmlComparison to other software2020-12-11 11:30:542022-09-04 10:01:00example-analysis.Rmdexample-analysis.htmlExamples of using eventglm and interpreting the results2020-08-26 06:46:172024-02-22 12:16:10extenstions.Rmdextenstions.htmlExtending eventglm2021-01-23 15:41:402022-09-04 10:01:00[sachsmc] xactonomial 0.4.0sachsmc@gmail.com (Michael C Sachs)We consider the k sample multinomial problem where we
observe k vectors (possibly of different lengths), each
representing an independent sample from a multinomial. For a
given function psi which takes in the concatenated vector of
multinomial probabilities and outputs a real number, we are
interested in constructing a confidence interval for psi. We
use an exact (but computational and stochastic) method to
compute a confidence interval and a function for calculation of
p values. The details of the method will be in a forthcoming
preprint.https://github.com/r-universe/sachsmc/actions/runs/10878076841Tue, 18 Jun 2024 10:29:38 GMTxactonomial0.4.0successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/xactonomialexample.Rmdexample.htmlBasic Examples2024-01-24 14:41:132024-06-18 09:58:34[sachsmc] drsurv 0.1.0sachsmc@gmail.com (Michael C Sachs)An implementation of several doubly robust estimators for
the survival difference at a given time point and one more
complex doubly robust estimator for the survival curve process.
The estimators are doubly robust in the sense that they are
consistent if the censoring model is correctly specified for
censoring and either the outcome model is correctly specified
for confounding or the exposure model is correctly specified
for confounding. See <doi:10.48550/arXiv.2310.16207> for more
details and examples.https://github.com/r-universe/sachsmc/actions/runs/10878076767Mon, 29 Jan 2024 16:19:41 GMTdrsurv0.1.0successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/drsurvcomparison.Rmdcomparison.htmlComparison of Methods in the Rotterdam Data2024-01-29 16:16:212024-01-29 16:16:21[sachsmc] plotROC 2.3.2sachsmc@gmail.com (Michael C. Sachs)Most ROC curve plots obscure the cutoff values and inhibit
interpretation and comparison of multiple curves. This attempts
to address those shortcomings by providing plotting and
interactive tools. Functions are provided to generate an
interactive ROC curve plot for web use, and print versions. A
Shiny application implementing the functions is also included.https://github.com/r-universe/sachsmc/actions/runs/10876807770Tue, 21 Nov 2023 10:42:22 GMTplotROC2.3.2successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/plotROCexamples.Rmdexamples.htmlGenerate ROC Curve Charts for Print and Interactive Use2014-11-12 17:44:252023-11-21 10:42:22[sachsmc] causaloptim 0.9.8sachsmc@gmail.com (Michael C Sachs)When causal quantities are not identifiable from the
observed data, it still may be possible to bound these
quantities using the observed data. We outline a class of
problems for which the derivation of tight bounds is always a
linear programming problem and can therefore, at least
theoretically, be solved using a symbolic linear optimizer. We
extend and generalize the approach of Balke and Pearl (1994)
<doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user
friendly graphical interface for setting up such problems via
directed acyclic graphs (DAG), which only allow for problems
within this class to be depicted. The user can then define
linear constraints to further refine their assumptions to meet
their specific problem, and then specify a causal query using a
text interface. The program converts this user defined DAG,
query, and constraints, and returns tight bounds. The bounds
can be converted to R functions to evaluate them for specific
datasets, and to latex code for publication. The methods and
proofs of tightness and validity of the bounds are described in
a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022)
<doi:10.1080/10618600.2022.2071905>.https://github.com/r-universe/sachsmc/actions/runs/11137806521Tue, 31 Oct 2023 14:14:52 GMTcausaloptim0.9.8successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/causaloptimexample-code.Rmdexample-code.htmlCode from examples in manuscript2020-03-09 13:50:192022-06-30 08:54:17shinyapp.Rmdshinyapp.htmlHow to use the causaloptim Shiny app to analyze graphs2019-08-15 17:24:062023-10-05 08:19:21vertexenum-speed.Rmdvertexenum-speed.htmlImproving the speed of computing causal bounds2021-01-20 09:25:562023-01-21 11:02:50[sachsmc] testassay 0.1.1sachsmc@gmail.com (Michael C Sachs)A common way of validating a biological assay for is
through a procedure, where m levels of an analyte are measured
with n replicates at each level, and if all m estimates of the
coefficient of variation (CV) are less than some prespecified
level, then the assay is declared validated for precision
within the range of the m analyte levels. Two limitations of
this procedure are: there is no clear statistical statement of
precision upon passing, and it is unclear how to modify the
procedure for assays with constant standard deviation. We
provide tools to convert such a procedure into a set of m
hypothesis tests. This reframing motivates the m:n:q procedure,
which upon completion delivers a 100q% upper confidence limit
on the CV. Additionally, for a post-validation assay output of
y, the method gives an ``effective standard deviation
interval'' of log(y) plus or minus r, which is a 68% confidence
interval on log(mu), where mu is the expected value of the
assay output for that sample. Further, the m:n:q procedure can
be straightforwardly applied to constant standard deviation
assays. We illustrate these tools by applying them to a growth
inhibition assay. This is an implementation of the methods
described in Fay, Sachs, and Miura (2018)
<doi:10.1002/sim.7528>.https://github.com/r-universe/sachsmc/actions/runs/10960321001Wed, 03 Jun 2020 13:04:23 GMTtestassay0.1.1successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/testassayGIAValidationExample.RmdGIAValidationExample.htmlValidation of Growth Inhibition Assay using testassay2016-10-31 07:55:252020-06-01 16:07:53[sachsmc] pseval 1.3.1sachsmc@gmail.com (Michael C Sachs)Contains the core methods for the evaluation of principal
surrogates in a single clinical trial. Provides a flexible
interface for defining models for the risk given treatment and
the surrogate, the models for integration over the missing
counterfactual surrogate responses, and the estimation methods.
Estimated maximum likelihood and pseudo-score can be used for
estimation, and the bootstrap for inference. A variety of
post-estimation summary methods are provided, including print,
summary, plot, and testing.https://github.com/r-universe/sachsmc/actions/runs/10987672066Fri, 25 Jan 2019 13:24:06 GMTpseval1.3.1successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/psevalintroduction.Rmdintroduction.htmlIntroduction to Principal Surrogate Evaluation in R2015-10-30 20:09:422019-01-25 13:24:06[sachsmc] tufterhandout 1.2.2sachsmc@gmail.com (Michael C Sachs)Custom template and output formats for use with rmarkdown.
Produce Edward Tufte-style handouts in html formats with full
support for rmarkdown featureshttps://github.com/r-universe/sachsmc/actions/runs/11137551337Thu, 20 Aug 2015 18:11:02 GMTtufterhandout1.2.2successhttps://sachsmc.r-universe.devhttps://github.com/sachsmc/tufterhandoutexample.Rmdexample.htmlTufte-style Handouts for Web Use2014-11-12 13:25:232015-01-27 19:18:06