From 1bd3312e2f3cb8928e9098b3db35eb760cc87a05 Mon Sep 17 00:00:00 2001
From: Qinyun Lin <qinyun.lin@gu.se>
Date: Mon, 7 Oct 2024 09:51:59 +0200
Subject: [PATCH 1/2] update the documentation for needtworows as a raw output
---
R/pkonfound.R | 1 +
man/pkonfound.Rd | 1 +
2 files changed, 2 insertions(+)
diff --git a/R/pkonfound.R b/R/pkonfound.R
index a5b8450..87cd288 100644
--- a/R/pkonfound.R
+++ b/R/pkonfound.R
@@ -59,6 +59,7 @@
#' \item{starting_table}{Observed 2 by 2 table before replacement and switching. Implied table for logistic regression.}
#' \item{final_table}{The 2 by 2 table after replacement and switching.}
#' \item{user_SE}{user entered standard error. Only applicable for logistic regression.}
+#' \item{needtworows}{whether double row switches are needed.}
#' \item{analysis_SE}{the standard error used to generate a plausible 2 by 2 table. Only applicable for logistic regression.}
#' \item{Fig_ITCV}{figure for ITCV.}
#' \item{Fig_RIR}{figure for RIR.}
diff --git a/man/pkonfound.Rd b/man/pkonfound.Rd
index 331d0c5..e239177 100644
--- a/man/pkonfound.Rd
+++ b/man/pkonfound.Rd
@@ -118,6 +118,7 @@ pkonfound prints the bias and the number of cases that would have to be replaced
\item{starting_table}{Observed 2 by 2 table before replacement and switching. Implied table for logistic regression.}
\item{final_table}{The 2 by 2 table after replacement and switching.}
\item{user_SE}{user entered standard error. Only applicable for logistic regression.}
+ \item{needtworows}{whether double row switches are needed.}
\item{analysis_SE}{the standard error used to generate a plausible 2 by 2 table. Only applicable for logistic regression.}
\item{Fig_ITCV}{figure for ITCV.}
\item{Fig_RIR}{figure for RIR.}
From cd47e2eaf7f2eae7327d7340b5f7523983c19be8 Mon Sep 17 00:00:00 2001
From: Qinyun Lin <qinyun.lin@gu.se>
Date: Tue, 15 Oct 2024 11:23:05 +0200
Subject: [PATCH 2/2] udpate readme to include ITCV
---
README.Rmd | 12 ++++++++-
README.md | 71 ++++++++++++++++++++++++++++++++++++++++++++++++++++--
2 files changed, 80 insertions(+), 3 deletions(-)
diff --git a/README.Rmd b/README.Rmd
index ef4baac..599f48a 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -42,7 +42,7 @@ devtools::install_github("konfound-project/konfound")
## pkonfound() for published studies
-`pkonfound()`, for published studies, calculates (1) how much bias there must be in an estimate to invalidate/sustain an inference, and interprets in terms of how much data would need to be replaced to nullify an inference; (2) the impact of an omitted variable necessary to invalidate/sustain an inference for a regression coefficient:
+`pkonfound()`, for published studies, calculates (1) how much bias there must be in an estimate to invalidate/sustain an inference, and interprets in terms of how much data would need to be replaced to nullify an inference (Robustness of an Inference to Replacement, RIR); (2) the impact of an omitted variable necessary to invalidate/sustain an inference for a regression coefficient (Impact Threshold of a Confounding Variable, ITCV). RIR is reported by default. ITCV can be generated by specifying `index = "IT"`.
```{r, eval = T}
library(konfound)
@@ -53,6 +53,11 @@ pkonfound(est_eff = 2,
std_err = .4,
n_obs = 100,
n_covariates = 3)
+pkonfound(est_eff = 2,
+ std_err = .4,
+ n_obs = 100,
+ n_covariates = 3,
+ index = "IT")
```
## konfound() for models fit in R
@@ -71,6 +76,11 @@ Sensitivity analysis for the effect for `wt` on `mpg` can be carried out as foll
konfound(m1, wt)
```
+Similar to `pkonfound`, ITCV can be generated by specifying `index = "IT"`.
+```{r}
+konfound(m1, wt, index = "IT")
+```
+
## mkonfound for meta-analyses including sensitivity analysis
`mkonfound()` supports sensitivity that can be compared or synthesized across multiple analyses. We can use an existing (and built-in) dataset, such as `mkonfound_ex`.
diff --git a/README.md b/README.md
index a1e533b..53047d2 100644
--- a/README.md
+++ b/README.md
@@ -40,8 +40,11 @@ devtools::install_github("konfound-project/konfound")
`pkonfound()`, for published studies, calculates (1) how much bias there
must be in an estimate to invalidate/sustain an inference, and
interprets in terms of how much data would need to be replaced to
-nullify an inference; (2) the impact of an omitted variable necessary to
-invalidate/sustain an inference for a regression coefficient:
+nullify an inference (Robustness of an Inference to Replacement, RIR);
+(2) the impact of an omitted variable necessary to invalidate/sustain an
+inference for a regression coefficient (Impact Threshold of a
+Confounding Variable, ITCV). RIR is reported by default. ITCV can be
+generated by specifying `index = "IT"`.
``` r
library(konfound)
@@ -76,6 +79,39 @@ pkonfound(est_eff = 2,
#> For other forms of output, run
#> ?pkonfound and inspect the to_return argument
#> For models fit in R, consider use of konfound().
+pkonfound(est_eff = 2,
+ std_err = .4,
+ n_obs = 100,
+ n_covariates = 3,
+ index = "IT")
+#> Impact Threshold for a Confounding Variable (ITCV):
+#>
+#> The minimum impact of an omitted variable to invalidate an inference for
+#> a null hypothesis of an effect of nu (0) is based on a correlation of 0.566
+#> with the outcome and 0.566 with the predictor of interest (conditioning
+#> on all observed covariates in the model; signs are interchangeable). This is
+#> based on a threshold effect of 0.2 for statistical significance (alpha = 0.05).
+#>
+#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be
+#> 0.566 X 0.566 = 0.321 to invalidate an inference for a null hypothesis of an effect of nu (0).
+#>
+#> For calculation of unconditional ITCV using pkonfound(), additionally include
+#> the R2, sdx, and sdy as input, and request raw output.
+#>
+#> See Frank (2000) for a description of the method.
+#>
+#> Citation:
+#> Frank, K. (2000). Impact of a confounding variable on the inference of a
+#> regression coefficient. Sociological Methods and Research, 29 (2), 147-194
+#>
+#> Accuracy of results increases with the number of decimals reported.
+#>
+#> The ITCV analysis was originally derived for OLS standard errors. If the
+#> standard errors reported in the table were not based on OLS, some caution
+#> should be used to interpret the ITCV.
+#> For other forms of output, run
+#> ?pkonfound and inspect the to_return argument
+#> For models fit in R, consider use of konfound().
```
## konfound() for models fit in R
@@ -141,6 +177,37 @@ konfound(m1, wt)
#> NULL
```
+Similar to `pkonfound`, ITCV can be generated by specifying
+`index = "IT"`.
+
+``` r
+konfound(m1, wt, index = "IT")
+#> Impact Threshold for a Confounding Variable (ITCV):
+#>
+#> The minimum (in absolute value) impact of an omitted variable to invalidate
+#> an inference for a null hypothesis of an effect of nu (0) is based on
+#> a correlation of -0.425 with the outcome and 0.425 with the predictor of
+#> interest (conditioning on all observed covariates in the model; signs are
+#> interchangeable). This is based on a threshold effect of -0.355 for statistical
+#> significance (alpha = 0.05).
+#>
+#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be
+#> -0.425 X 0.425 = -0.18 to invalidate an inference for a null hypothesis of an effect of nu (0).
+#>
+#> See Frank (2000) for a description of the method.
+#>
+#> Citation:
+#> Frank, K. (2000). Impact of a confounding variable on the inference of a
+#> regression coefficient. Sociological Methods and Research, 29 (2), 147-194
+#>
+#> Accuracy of results increases with the number of decimals reported.
+#>
+#> The ITCV analysis was originally derived for OLS standard errors. If the
+#> standard errors reported in the table were not based on OLS, some caution
+#> should be used to interpret the ITCV.
+#> NULL
+```
+
## mkonfound for meta-analyses including sensitivity analysis
`mkonfound()` supports sensitivity that can be compared or synthesized