Screening Outcomes and Predictors with MakeUnivariateRegressionTable() • SciDataReportR

1 Overview

MakeUnivariateRegressionTable() screens outcome-predictor relationships and returns a report-facing table, a detail table, a tidy results dataframe, fitted models, and metadata.

Use it when you want to quickly ask:

Which predictors are associated with one or more outcomes?
Do associations persist after covariate adjustment?
Are the displayed labels and logistic event levels what I think they are?

The function chooses the model family automatically:

Numeric outcomes use linear regression.
Two-level factor, character, or logical outcomes use logistic regression.
Logistic estimates are exponentiated by default and reported as odds ratios.

Note

MakeUnivariateRegressionTable() was previously named UnivariateRegressionTable(). The old name still works as a backwards-compatible synonym, so existing scripts continue to run.

2 Load packages

library(SciDataReportR)
library(dplyr)

3 Load example data

The examples use SampleData and SampleVariableTypes, then apply labels and recoding with RevalueData().

data("SampleData")
data("SampleVariableTypes")

RevaluedObj <- RevalueData(
  SampleData,
  SampleVariableTypes
)

df_Revalued <- RevaluedObj$RevaluedData

4 Basic linear screening

This example screens several labelled biomarkers against two predictors.

vars_Outcomes <- c(
  "Calbindin",
  "Ferritin",
  "MMP7",
  "Sortilin"
)

vars_Predictors <- c(
  "Diagnosis",
  "age"
)

Reg_Obj_Un <- MakeUnivariateRegressionTable(
  data = df_Revalued,
  outcome_vars = vars_Outcomes,
  predictor_vars = vars_Predictors,
  Standardize = TRUE
)

5 Formatted and detailed tables

FormattedTable is the report-facing table.

Reg_Obj_Un$FormattedTable

Characteristic	Calbindin	Ferritin	Matrix metalloproteinase 7	Sortilin
Characteristic	Beta^1,1	Beta^1,1	Beta^1,1	Beta^1,1
Diagnosis	( )	( )	( )	( )
Impaired	0.22 (0.123)	0.26 (0.122)*	0.55 (0.119)***	0.42 (0.121)***
Age	0.05 (0.056)	0.03 (0.056)	-0.05 (0.056)	0.07 (0.056)
¹ p<0.05; p<0.01; p<0.001
Abbreviations: CI = Confidence Interval, SE = Standard Error

LargeTable keeps more of the model detail and is useful for QC.

Reg_Obj_Un$LargeTable

Characteristic	Calbindin			Ferritin			Matrix metalloproteinase 7			Sortilin
Characteristic	Beta	95% CI	p-value	Beta	95% CI	p-value	Beta	95% CI	p-value	Beta	95% CI	p-value
Diagnosis
Impaired	0.22	-0.02, 0.46	0.069	0.26	0.02, 0.50	0.035	0.55	0.32, 0.79	<0.001	0.42	0.18, 0.66	<0.001
Age	0.05	-0.06, 0.16	0.36	0.03	-0.08, 0.14	0.59	-0.05	-0.16, 0.06	0.41	0.07	-0.04, 0.18	0.23
Abbreviation: CI = Confidence Interval

6 Tidy results dataframe

Results holds the underlying numbers as a plain dataframe: one row per estimated term, with the estimate, standard error, confidence interval, p-value, and the labels used in the tables.

Reg_Obj_Un$Results

    Outcome               OutcomeLabel OutcomeFamily EffectType Predictor
1 Calbindin                  Calbindin        linear   Estimate Diagnosis
2 Calbindin                  Calbindin        linear   Estimate       age
3  Ferritin                   Ferritin        linear   Estimate Diagnosis
4  Ferritin                   Ferritin        linear   Estimate       age
5      MMP7 Matrix metalloproteinase 7        linear   Estimate Diagnosis
6      MMP7 Matrix metalloproteinase 7        linear   Estimate       age
7  Sortilin                   Sortilin        linear   Estimate Diagnosis
8  Sortilin                   Sortilin        linear   Estimate       age
  PredictorLabel              Term    Level            TermLabel   N
1      Diagnosis DiagnosisImpaired Impaired Diagnosis : Impaired 333
2            Age               age     <NA>                  Age 322
3      Diagnosis DiagnosisImpaired Impaired Diagnosis : Impaired 333
4            Age               age     <NA>                  Age 322
5      Diagnosis DiagnosisImpaired Impaired Diagnosis : Impaired 333
6            Age               age     <NA>                  Age 322
7      Diagnosis DiagnosisImpaired Impaired Diagnosis : Impaired 333
8            Age               age     <NA>                  Age 322
     Estimate   StdError     ConfLow   ConfHigh       PValue Significant
1  0.22318256 0.12254159 -0.01787596 0.46424108 6.946687e-02       FALSE
2  0.05166760 0.05580595 -0.05812530 0.16146050 3.552248e-01       FALSE
3  0.25934156 0.12232632  0.01870650 0.49997662 3.474297e-02        TRUE
4  0.03027304 0.05597431 -0.07985109 0.14039718 5.889954e-01       FALSE
5  0.55090515 0.11937344  0.31607888 0.78573143 5.630656e-06        TRUE
6 -0.04643922 0.05571349 -0.15605022 0.06317177 4.051640e-01       FALSE
7  0.41966369 0.12097458  0.18168773 0.65763966 5.914370e-04        TRUE
8  0.06679376 0.05597311 -0.04332801 0.17691553 2.336285e-01       FALSE
  ReferenceValue
1              0
2              0
3              0
4              0
5              0
6              0
7              0
8              0

Because it is an ordinary dataframe, you can filter, sort, or export it directly, and pass it (or a subset of it) straight to PlotForestFromTable().

Reg_Obj_Un$Results %>%
  filter(Significant) %>%
  arrange(PValue)

   Outcome               OutcomeLabel OutcomeFamily EffectType Predictor
1     MMP7 Matrix metalloproteinase 7        linear   Estimate Diagnosis
2 Sortilin                   Sortilin        linear   Estimate Diagnosis
3 Ferritin                   Ferritin        linear   Estimate Diagnosis
  PredictorLabel              Term    Level            TermLabel   N  Estimate
1      Diagnosis DiagnosisImpaired Impaired Diagnosis : Impaired 333 0.5509052
2      Diagnosis DiagnosisImpaired Impaired Diagnosis : Impaired 333 0.4196637
3      Diagnosis DiagnosisImpaired Impaired Diagnosis : Impaired 333 0.2593416
   StdError   ConfLow  ConfHigh       PValue Significant ReferenceValue
1 0.1193734 0.3160789 0.7857314 5.630656e-06        TRUE              0
2 0.1209746 0.1816877 0.6576397 5.914370e-04        TRUE              0
3 0.1223263 0.0187065 0.4999766 3.474297e-02        TRUE              0

7 Covariate adjustment

Use covariates when each screen should adjust for the same covariates.

Reg_Obj_Un_Covar <- MakeUnivariateRegressionTable(
  data = df_Revalued,
  outcome_vars = vars_Outcomes,
  predictor_vars = vars_Predictors,
  covariates = "sex",
  Standardize = TRUE
)

Reg_Obj_Un_Covar$FormattedTable

Characteristic	Calbindin	Ferritin	Matrix metalloproteinase 7	Sortilin
Characteristic	Beta^1,1	Beta^1,1	Beta^1,1	Beta^1,1
Diagnosis	( )	( )	( )	( )
Impaired	0.28 (0.121)*	0.22 (0.122)	0.52 (0.120)***	0.43 (0.122)***
Age	0.03 (0.055)	0.05 (0.056)	-0.03 (0.056)	0.07 (0.056)
¹ p<0.05; p<0.01; p<0.001
Abbreviations: CI = Confidence Interval, SE = Standard Error

8 Many predictors for one outcome

The same function also works when the question is one outcome against many candidate predictors.

Reg_Obj_ManyPredictors <- MakeUnivariateRegressionTable(
  data = df_Revalued,
  outcome_vars = "Sortilin",
  predictor_vars = c("Diagnosis", "age", "Calbindin", "Ferritin", "MMP7"),
  Standardize = TRUE
)

Reg_Obj_ManyPredictors$FormattedTable

Characteristic	Sortilin
Characteristic	Beta^1,1,1,1,1
Diagnosis	( )
Impaired	0.42 (0.121)***
Age	0.07 (0.056)
Calbindin	0.48 (0.048)***
Ferritin	0.62 (0.043)***
Matrix metalloproteinase 7	0.18 (0.054)**
¹ p<0.05; p<0.01; p<0.001
Abbreviations: CI = Confidence Interval, SE = Standard Error

9 Logistic outcomes

Binary outcomes use logistic regression automatically. By default, estimates are exponentiated and shown as odds ratios.

Reg_Obj_Logistic <- MakeUnivariateRegressionTable(
  data = df_Revalued,
  outcome_vars = "Diagnosis",
  predictor_vars = c("age", "Calbindin", "Ferritin", "MMP7"),
  Standardize = TRUE
)

Reg_Obj_Logistic$FormattedTable

Characteristic	Diagnosis
Characteristic	OR^1,1,1,1
Age	0.93 (0.124)
Calbindin	1.25 (0.125)
Ferritin	1.30 (0.126)*
Matrix metalloproteinase 7	1.80 (0.135)***
¹ p<0.05; p<0.01; p<0.001
Abbreviations: CI = Confidence Interval, OR = Odds Ratio, SE = Standard Error

Always check the metadata for logistic models. It records the reference level and event level.

Reg_Obj_Logistic$Metadata$Outcomes

    Outcome OutcomeLabel OutcomeFamily ReferenceLevel EventLevel
1 Diagnosis    Diagnosis      logistic        Control   Impaired

10 Access fitted models

Fitted models are returned by outcome, then predictor.

names(Reg_Obj_Un$ModelSummaries)

[1] "Calbindin" "Ferritin"  "MMP7"      "Sortilin"

names(Reg_Obj_ManyPredictors$ModelSummaries$Sortilin)

[1] "Diagnosis" "age"       "Calbindin" "Ferritin"  "MMP7"

summary(Reg_Obj_ManyPredictors$ModelSummaries$Sortilin$Ferritin)


Call:
stats::lm(formula = f, data = ModelData)

Residuals:
     Min       1Q   Median       3Q      Max
-1.90775 -0.50046 -0.04898  0.50987  2.18216

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.396e-17  4.318e-02    0.00        1
Ferritin    6.173e-01  4.324e-02   14.28   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7879 on 331 degrees of freedom
Multiple R-squared:  0.3811,    Adjusted R-squared:  0.3792
F-statistic: 203.8 on 1 and 331 DF,  p-value: < 2.2e-16

11 Reproducibility

# save.image("MakeUnivariateRegressionTable_workspace.RData")
print(sessionInfo())

R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] dplyr_1.2.1           SciDataReportR_20.9.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6           xfun_0.59              bayestestR_0.18.1
 [4] ggplot2_4.0.3          insight_1.5.2          rstatix_1.0.0
 [7] lattice_0.22-9         paletteer_1.7.0        vctrs_0.7.3
[10] tools_4.6.1            generics_0.1.4         datawizard_1.3.1
[13] tibble_3.3.1           pkgconfig_2.0.3        RColorBrewer_1.1-3
[16] correlation_0.8.8      S7_0.2.2               gt_1.3.0
[19] RcppParallel_5.1.11-2  lifecycle_1.0.5        compiler_4.6.1
[22] farver_2.1.2           stringr_1.6.0          carData_3.0-6
[25] snakecase_0.11.1       litedown_0.9           sass_0.4.10
[28] htmltools_0.5.9        yaml_2.3.12            Formula_1.2-5
[31] pillar_1.11.1          car_3.1-5              tidyr_1.3.2
[34] broom.helpers_1.22.0   statsExpressions_2.0.0 abind_1.4-8
[37] commonmark_2.0.0       tidyselect_1.2.1       sjlabelled_1.2.0
[40] digest_0.6.39          stringi_1.8.7          mvtnorm_1.4-1
[43] gtsummary_2.5.1        purrr_1.2.2            rematch2_2.1.2
[46] forcats_1.0.1          ggstatsplot_1.0.0      labelled_2.16.0
[49] fastmap_1.2.0          grid_4.6.1             cli_3.6.6
[52] magrittr_2.0.5         base64enc_0.1-6        cards_0.8.0
[55] patchwork_1.3.2        dichromat_2.0-0.1      broom_1.0.13
[58] withr_3.0.3            scales_1.4.0           backports_1.5.1
[61] estimability_2.0.0     rmarkdown_2.31         emmeans_2.0.3
[64] otel_0.2.0             hms_1.1.4              coda_0.19-4.1
[67] evaluate_1.0.5         knitr_1.51             haven_2.5.5
[70] parameters_0.29.2      markdown_2.0           rstantools_2.6.0
[73] rlang_1.2.0            xtable_1.8-8           glue_1.8.1
[76] xml2_1.6.0             jsonlite_2.0.0         effectsize_1.0.2
[79] R6_2.6.1               fs_2.1.0