Regression analysis quantifies the relationship between a response variable and one or more predictors while accounting for potential confounding. The standard analytical workflow proceeds in two phases: univariable (unadjusted) analysis, in which each predictor is examined independently; and multivariable (adjusted) analysis, in which effects conditional on other predictors are examined collectively. Comparing these estimates found in these analyses reveals the extent of confounding and identifies independent predictors of a given outcome.

The summata package provides three principal functions for regression analysis:

All functions support a wide range of model types including linear models, generalized linear models (logistic, Poisson, Gaussian, Gamma, negative binomial), Cox proportional hazards models, and mixed-effects models, with consistent syntax and formatted output.

Function	Purpose
`uniscreen()`	Univariable screening of multiple predictors
`fit()`	Single univariable/multivariable model
`fullfit()`	Combined univariable and multivariable analysis

As with other summata functions, these functions adhere to the standard calling convention:

where data is the dataset, outcome is the outcome variable, predictors is a vector of predicting variables, and model_type is the type of model to be implemented. This vignette demonstrates the various capabilities of these functions using the included sample dataset.

Preliminaries

The examples in this vignette use the clintrial dataset included with summata:

library(summata)
library(survival)

data(clintrial)
data(clintrial_labels)

The clintrial dataset simulates a multisite oncology clinical trial with realistic outcomes and site-level clustering. Variables suitable for different regression approaches include:

Variable	Description	Type	Application
`pain_score`	Postoperative Pain Score (0-10)	Continuous	Linear regression
`readmission_30d`	30-Day Readmission	Binary	Logistic regression
`any_complication`	Any Complication	Binary	Logistic regression
`los_days`	Length of Hospital Stay (days)	Continuous (positive)	Linear or Gamma regression
`recovery_days`	Days to Functional Recovery	Continuous (positive)	Linear or Gamma regression
`ae_count`	Adverse Event Count	Count (overdispersed)	Negative binomial / quasipoisson
`fu_count`	Follow-Up Visit Count	Count (equidispersed)	Poisson regression
`pfs_months`, `pfs_status`	Progression-Free Survival Time (months)	Time-to-event	Cox regression
`os_months`, `os_status`	Overall Survival Time (months)	Time-to-event	Cox regression
`site`	Study Site	Clustering variable	Mixed-effects models

Supported Model Types

The following table summarizes all supported model types (model_type) and families/links (family). For most estimates, effect measures are displayed by default; set exponentiate = FALSE to display raw coefficients.

`model_type`	`family`	Outcome	Effect Measure	Package
`"lm"`	—	Continuous	β coefficient	`stats`
`"glm"`	`"binomial"`	Binary	Odds ratio	`stats`
`"glm"`	`"quasibinomial"`	Binary (overdispersed)	Odds ratio	`stats`
`"glm"`	`"poisson"`	Count	Rate ratio	`stats`
`"glm"`	`"quasipoisson"`	Count (overdispersed)	Rate ratio	`stats`
`"glm"`	`"gaussian"`	Continuous	β coefficient	`stats`
`"glm"`	`"Gamma"`	Positive continuous	Ratio (with log link)	`stats`
`"glm"`	`"inverse.gaussian"`	Positive continuous	Coefficient	`stats`
`"negbin"`	—	Count (overdispersed)	Rate ratio	`MASS`
`"coxph"`	—	Time-to-event	Hazard ratio	`survival`
`"clogit"`	—	Matched binary	Odds ratio	`survival`
`"lmer"`	—	Continuous (clustered)	β coefficient	`lme4`
`"glmer"`	`"binomial"`	Binary (clustered)	Odds ratio	`lme4`
`"glmer"`	`"poisson"`	Count (clustered)	Rate ratio	`lme4`
`"coxme"`	—	Time-to-event (clustered)	Hazard ratio	`coxme`

Univariable Screening

The uniscreen() function fits separate regression models for each predictor, combining results into a single table. This approach identifies candidate predictors for multivariable modeling.

Example 1: Binary Outcome Screen (Logistic Regression)

For binary outcomes, use model_type = "glm" (logistic regression is the default family). Here we examine predictors of 30-day hospital readmission:

screening_vars <- c("age", "sex", "race", "bmi", "smoking", 
                    "diabetes", "stage", "ecog", "treatment")

example1 <- uniscreen(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = screening_vars,
  model_type = "glm",
  labels = clintrial_labels
)

example1
#> 
#> Univariable Screening Results
#> Outcome: readmission_30d
#> Model Type: Logistic
#> Predictors Screened: 9
#> Significant (p < 0.05): 7
#> 
#>                    Variable   Group      n Events       OR (95% CI) p-value
#>                      <char>  <char> <char> <char>            <char>  <char>
#>  1:             Age (years)       -    850    427  1.03 (1.02-1.04) < 0.001
#>  2:                     Sex  Female    450    210         reference       -
#>  3:                            Male    400    217  1.36 (1.03-1.78)   0.027
#>  4:                    Race   White    598    298         reference       -
#>  5:                           Black    126     66  1.11 (0.75-1.63)   0.603
#>  6:                           Asian     93     51  1.22 (0.79-1.90)   0.370
#>  7:                           Other     33     12  0.58 (0.28-1.19)   0.136
#>  8: Body Mass Index (kg/m²)       -    838    418  1.00 (0.98-1.03)   0.787
#>  9:          Smoking Status   Never    337    156         reference       -
#> 10:                          Former    311    141  0.96 (0.71-1.31)   0.808
#> 11:                         Current    185    119  2.09 (1.45-3.03) < 0.001
#> 12:                Diabetes      No    637    301         reference       -
#> 13:                             Yes    197    116  1.60 (1.16-2.21)   0.004
#> 14:           Disease Stage       I    211     89         reference       -
#> 15:                              II    263    126  1.26 (0.88-1.82)   0.213
#> 16:                             III    241    133  1.69 (1.16-2.45)   0.006
#> 17:                              IV    132     77  1.92 (1.23-2.98)   0.004
#> 18: ECOG Performance Status       0    265    108         reference       -
#> 19:                               1    302    145  1.34 (0.96-1.87)   0.083
#> 20:                               2    238    139  2.04 (1.43-2.91) < 0.001
#> 21:                               3     37     30 6.23 (2.64-14.70) < 0.001
#> 22:         Treatment Group Control    196     91         reference       -
#> 23:                          Drug A    292    127  0.89 (0.62-1.28)   0.523
#> 24:                          Drug B    362    209  1.58 (1.11-2.24)   0.011
#>                    Variable   Group      n Events       OR (95% CI) p-value
#>                      <char>  <char> <char> <char>            <char>  <char>

Each entry represents a separate univariable model. For categorical predictors, each non-reference level appears as a separate row.

Example 2: Filtering Screen by p-value

The p_threshold parameter retains only predictors meeting a significance criterion:

example2 <- uniscreen(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = screening_vars,
  model_type = "glm",
  p_threshold = 0.01,
  labels = clintrial_labels
)

example2
#> 
#> Univariable Screening Results
#> Outcome: readmission_30d
#> Model Type: Logistic
#> Predictors Screened: 9
#> Significant (p < 0.01): 5
#> 
#>                    Variable   Group      n Events       OR (95% CI) p-value
#>                      <char>  <char> <char> <char>            <char>  <char>
#>  1:             Age (years)       -    850    427  1.03 (1.02-1.04) < 0.001
#>  2:                     Sex  Female    450    210         reference       -
#>  3:                            Male    400    217  1.36 (1.03-1.78)   0.027
#>  4:                    Race   White    598    298         reference       -
#>  5:                           Black    126     66  1.11 (0.75-1.63)   0.603
#>  6:                           Asian     93     51  1.22 (0.79-1.90)   0.370
#>  7:                           Other     33     12  0.58 (0.28-1.19)   0.136
#>  8: Body Mass Index (kg/m²)       -    838    418  1.00 (0.98-1.03)   0.787
#>  9:          Smoking Status   Never    337    156         reference       -
#> 10:                          Former    311    141  0.96 (0.71-1.31)   0.808
#> 11:                         Current    185    119  2.09 (1.45-3.03) < 0.001
#> 12:                Diabetes      No    637    301         reference       -
#> 13:                             Yes    197    116  1.60 (1.16-2.21)   0.004
#> 14:           Disease Stage       I    211     89         reference       -
#> 15:                              II    263    126  1.26 (0.88-1.82)   0.213
#> 16:                             III    241    133  1.69 (1.16-2.45)   0.006
#> 17:                              IV    132     77  1.92 (1.23-2.98)   0.004
#> 18: ECOG Performance Status       0    265    108         reference       -
#> 19:                               1    302    145  1.34 (0.96-1.87)   0.083
#> 20:                               2    238    139  2.04 (1.43-2.91) < 0.001
#> 21:                               3     37     30 6.23 (2.64-14.70) < 0.001
#> 22:         Treatment Group Control    196     91         reference       -
#> 23:                          Drug A    292    127  0.89 (0.62-1.28)   0.523
#> 24:                          Drug B    362    209  1.58 (1.11-2.24)   0.011
#>                    Variable   Group      n Events       OR (95% CI) p-value
#>                      <char>  <char> <char> <char>            <char>  <char>

Example 3: Continuous Outcome Screen (Linear Regression)

For continuous outcomes, specify model_type = "lm":

example4 <- uniscreen(
  data = clintrial,
  outcome = "los_days",
  predictors = c("age", "sex", "stage", "diabetes", "ecog"),
  model_type = "lm",
  labels = clintrial_labels
)

example4
#> 
#> Univariable Screening Results
#> Outcome: los_days
#> Model Type: Linear
#> Predictors Screened: 5
#> Significant (p < 0.05): 5
#> 
#>                    Variable  Group      n Coefficient (95% CI) p-value
#>                      <char> <char> <char>               <char>  <char>
#>  1:             Age (years)      -    830     0.14 (0.11-0.16) < 0.001
#>  2:                     Sex Female    450            reference       -
#>  3:                           Male    400     0.86 (0.20-1.52)   0.010
#>  4:           Disease Stage      I    211            reference       -
#>  5:                             II    263     1.16 (0.30-2.02)   0.009
#>  6:                            III    241     2.72 (1.84-3.60) < 0.001
#>  7:                             IV    132     2.96 (1.92-4.01) < 0.001
#>  8:                Diabetes     No    637            reference       -
#>  9:                            Yes    197     2.59 (1.83-3.35) < 0.001
#> 10: ECOG Performance Status      0    265            reference       -
#> 11:                              1    302     2.12 (1.35-2.88) < 0.001
#> 12:                              2    238     3.78 (2.96-4.59) < 0.001
#> 13:                              3     37     4.90 (3.32-6.48) < 0.001

Example 4: Time-to-Event Outcome Screen (Cox Regression)

For survival outcomes, specify the outcome using Surv() notation and set model_type = "coxph":

example3 <- uniscreen(
  data = clintrial,
  outcome = "Surv(os_months, os_status)",
  predictors = c("age", "sex", "treatment", "stage", "ecog"),
  model_type = "coxph",
  labels = clintrial_labels
)

example3
#> 
#> Univariable Screening Results
#> Outcome: Surv(os_months, os_status)
#> Model Type: Cox PH
#> Predictors Screened: 5
#> Significant (p < 0.05): 5
#> 
#>                    Variable   Group      n Events      HR (95% CI) p-value
#>                      <char>  <char> <char> <char>           <char>  <char>
#>  1:             Age (years)       -    850    609 1.03 (1.03-1.04) < 0.001
#>  2:                     Sex  Female    450    298        reference       -
#>  3:                            Male    400    311 1.30 (1.11-1.53)   0.001
#>  4:         Treatment Group Control    196    151        reference       -
#>  5:                          Drug A    292    184 0.64 (0.52-0.80) < 0.001
#>  6:                          Drug B    362    274 0.94 (0.77-1.15)   0.567
#>  7:           Disease Stage       I    211    127        reference       -
#>  8:                              II    263    172 1.12 (0.89-1.41)   0.337
#>  9:                             III    241    186 1.69 (1.35-2.11) < 0.001
#> 10:                              IV    132    121 3.18 (2.47-4.09) < 0.001
#> 11: ECOG Performance Status       0    265    159        reference       -
#> 12:                               1    302    212 1.36 (1.11-1.67)   0.003
#> 13:                               2    238    194 1.86 (1.51-2.29) < 0.001
#> 14:                               3     37     37 3.06 (2.13-4.38) < 0.001

Example 5: Retaining Model Objects

Setting keep_models = TRUE stores the fitted model objects for diagnostics:

example5 <- uniscreen(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("age", "sex", "stage"),
  model_type = "glm",
  keep_models = TRUE
)

# Access individual models
models <- attr(example5, "models")
names(models)
#> [1] "age"   "sex"   "stage"

# Examine a specific model
summary(models[["age"]])
#> 
#> Call:
#> stats::glm(formula = formula, family = family, data = data, model = keep_models, 
#>     x = FALSE, y = TRUE)
#> 
#> Coefficients:
#>              Estimate Std. Error z value Pr(>|z|)    
#> (Intercept) -1.780821   0.369774  -4.816 1.46e-06 ***
#> age          0.029855   0.006056   4.930 8.22e-07 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 1178.3  on 849  degrees of freedom
#> Residual deviance: 1152.9  on 848  degrees of freedom
#> AIC: 1156.9
#> 
#> Number of Fisher Scoring iterations: 4

Multivariable Modeling

The fit() function estimates a single regression model with multiple predictors, producing adjusted effect estimates. It is effectively a wrapper for existing model functions in R and other supported packages that enforces summata syntax for more consistent function calling.

Example 6: Logistic Regression

For binary outcomes with multiple predictors:

example6 <- fit(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("age", "sex", "treatment", "stage", "diabetes"),
  model_type = "glm",
  labels = clintrial_labels
)

example6
#> 
#> Multivariable Logistic Model
#> Formula: readmission_30d ~ age + sex + treatment + stage + diabetes
#> n = 834, Events = 417
#> 
#>            Variable   Group      n Events     aOR (95% CI) p-value
#>              <char>  <char> <char> <char>           <char>  <char>
#>  1:     Age (years)       -    834    417 1.03 (1.02-1.05) < 0.001
#>  2:             Sex  Female    444    207        reference       -
#>  3:                    Male    390    210 1.38 (1.04-1.83)   0.028
#>  4: Treatment Group Control    191     88        reference       -
#>  5:                  Drug A    288    126 0.85 (0.58-1.25)   0.409
#>  6:                  Drug B    355    203 1.40 (0.97-2.02)   0.075
#>  7:   Disease Stage       I    207     86        reference       -
#>  8:                      II    261    124 1.25 (0.86-1.84)   0.242
#>  9:                     III    237    131 1.77 (1.19-2.62)   0.005
#> 10:                      IV    129     76 2.05 (1.29-3.24)   0.002
#> 11:        Diabetes      No    637    301        reference       -
#> 12:                     Yes    197    116 1.67 (1.19-2.34)   0.003

Example 8: Linear Regression

For continuous outcomes:

example8 <- fit(
  data = clintrial,
  outcome = "los_days",
  predictors = c("age", "sex", "stage", "ecog"),
  model_type = "lm",
  labels = clintrial_labels
)

example8
#> 
#> Multivariable Linear Model
#> Formula: los_days ~ age + sex + stage + ecog
#> n = 822
#> 
#>                    Variable  Group      n Adj. Coefficient (95% CI) p-value
#>                      <char> <char> <char>                    <char>  <char>
#>  1:             Age (years)      -    822          0.14 (0.11-0.16) < 0.001
#>  2:                     Sex Female    440                 reference       -
#>  3:                           Male    382          0.90 (0.33-1.46)   0.002
#>  4:           Disease Stage      I    206                 reference       -
#>  5:                             II    257          1.21 (0.46-1.97)   0.002
#>  6:                            III    235          2.92 (2.15-3.70) < 0.001
#>  7:                             IV    124          3.18 (2.26-4.11) < 0.001
#>  8: ECOG Performance Status      0    261                 reference       -
#>  9:                              1    295          2.15 (1.46-2.83) < 0.001
#> 10:                              2    229          3.63 (2.89-4.36) < 0.001
#> 11:                              3     37          4.93 (3.50-6.35) < 0.001

Example 7: Cox Regression

For time-to-event outcomes:

example7 <- fit(
  data = clintrial,
  outcome = "Surv(os_months, os_status)",
  predictors = c("age", "sex", "treatment", "stage"),
  model_type = "coxph",
  labels = clintrial_labels
)

example7
#> 
#> Multivariable Cox PH Model
#> Formula: Surv(os_months, os_status) ~ age + sex + treatment + stage
#> n = 847, Events = 606
#> 
#>            Variable   Group      n Events     aHR (95% CI) p-value
#>              <char>  <char> <char> <char>           <char>  <char>
#>  1:     Age (years)       -    847    606 1.04 (1.03-1.04) < 0.001
#>  2:             Sex  Female    450    298        reference       -
#>  3:                    Male    400    311 1.33 (1.13-1.56) < 0.001
#>  4: Treatment Group Control    196    151        reference       -
#>  5:                  Drug A    292    184 0.56 (0.45-0.70) < 0.001
#>  6:                  Drug B    362    274 0.83 (0.67-1.01)   0.062
#>  7:   Disease Stage       I    211    127        reference       -
#>  8:                      II    263    172 1.16 (0.92-1.46)   0.214
#>  9:                     III    241    186 1.90 (1.51-2.38) < 0.001
#> 10:                      IV    132    121 3.57 (2.77-4.60) < 0.001

Example 9: Poisson Regression

For count outcomes where variance approximately equals the mean, use model_type = "glm" with family = "poisson". The fu_count variable represents the number of follow-up clinic visits, which is equidispersed (suitable for standard Poisson):

example9 <- fit(
  data = clintrial,
  outcome = "fu_count",
  predictors = c("age", "stage", "treatment", "surgery"),
  model_type = "glm",
  family = "poisson",
  labels = clintrial_labels
)

example9
#> 
#> Multivariable Poisson Model
#> Formula: fu_count ~ age + stage + treatment + surgery
#> n = 839, Events = 5517
#> 
#>               Variable   Group      n Events     aRR (95% CI) p-value
#>                 <char>  <char> <char> <char>           <char>  <char>
#>  1:        Age (years)       -    839  5,517 1.00 (1.00-1.00)   0.038
#>  2:      Disease Stage       I    207  1,238        reference       -
#>  3:                         II    261  1,638 1.05 (0.98-1.13)   0.169
#>  4:                        III    240  1,638 1.14 (1.06-1.23) < 0.001
#>  5:                         IV    131  1,003 1.33 (1.21-1.45) < 0.001
#>  6:    Treatment Group Control    191  1,169        reference       -
#>  7:                     Drug A    290  1,910 1.06 (0.99-1.14)   0.101
#>  8:                     Drug B    358  2,438 1.12 (1.04-1.20)   0.002
#>  9: Surgical Resection      No    476  3,091        reference       -
#> 10:                        Yes    363  2,426 1.09 (1.03-1.16)   0.003

Output displays rate ratios (RR). A rate ratio of 1.10 indicates a 10% higher event rate compared to the reference group. For overdispersed counts (variance > mean), consider negative binomial or quasipoisson regression instead (see Example 18).

Example 10: Toggling Reference Rows for Factors

By default, reference rows are shown in output tables (reference_rows = TRUE). Setting this parameter to FALSE removes these reference rows:

example10 <- fit(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("sex", "stage", "treatment"),
  model_type = "glm",
  reference_rows = FALSE,
  labels = clintrial_labels
)

example10
#> 
#> Multivariable Logistic Model
#> Formula: readmission_30d ~ sex + stage + treatment
#> n = 398, Events = 216
#> 
#>           Variable  Group      n Events     aOR (95% CI) p-value
#>             <char> <char> <char> <char>           <char>  <char>
#> 1:             Sex   Male    398    216 1.38 (1.05-1.82)   0.022
#> 2:   Disease Stage     II    263    126 1.23 (0.85-1.78)   0.270
#> 3:                    III    241    133 1.59 (1.09-2.33)   0.016
#> 4:                     IV    132     77 1.91 (1.22-2.99)   0.004
#> 5: Treatment Group Drug A    292    127 0.88 (0.61-1.28)   0.510
#> 6:                 Drug B    361    208 1.50 (1.05-2.14)   0.027

Example 11: Confidence Level

The conf_level parameter adjusts the confidence interval width:

example11 <- fit(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("age", "sex", "stage"),
  model_type = "glm",
  conf_level = 0.90
)

example11
#> 
#> Multivariable Logistic Model
#> Formula: readmission_30d ~ age + sex + stage
#> n = 847, Events = 425
#> 
#>    Variable  Group      n Events     aOR (90% CI) p-value
#>      <char> <char> <char> <char>           <char>  <char>
#> 1:      age      -    847    425 1.03 (1.02-1.04) < 0.001
#> 2:      sex Female    449    209        reference       -
#> 3:            Male    398    216 1.41 (1.12-1.79)   0.014
#> 4:    stage      I    211     89        reference       -
#> 5:              II    263    126 1.28 (0.94-1.75)   0.196
#> 6:             III    241    133 1.83 (1.32-2.51)   0.002
#> 7:              IV    132     77 2.03 (1.39-2.96)   0.002

Example 12: Raw Coefficients

For logistic and Cox models, set exponentiate = FALSE to display log-scale coefficients (β rather than e^β):

example12 <- fit(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("age", "sex", "stage"),
  model_type = "glm",
  exponentiate = FALSE
)

example12
#> 
#> Multivariable Logistic Model
#> Formula: readmission_30d ~ age + sex + stage
#> n = 847, Events = 425
#> 
#>    Variable  Group      n Events Adj. Coefficient (95% CI) p-value
#>      <char> <char> <char> <char>                    <char>  <char>
#> 1:      age      -    847    425       0.03 (0.02 to 0.04) < 0.001
#> 2:      sex Female    449    209                 reference       -
#> 3:            Male    398    216       0.35 (0.07 to 0.62)   0.014
#> 4:    stage      I    211     89                 reference       -
#> 5:              II    263    126      0.25 (-0.13 to 0.62)   0.196
#> 6:             III    241    133       0.60 (0.22 to 0.98)   0.002
#> 7:              IV    132     77       0.71 (0.26 to 1.16)   0.002

Combined Analysis

The fullfit() function integrates univariable screening and multivariable modeling into a single workflow, producing a combined table with both unadjusted and adjusted estimates.

This function extends fit() with an additional parameter (method) for variable selection:

fullfit(data, outcome, predictors, model_type, method, ...)

For the method parameter, the following options are available. Setting method to one of the following options controls the predictors entering the multivariable model:

Method	Description
`"screen"`	Only predictors meeting the p-threshold in univariable analysis are used in the multivariable model (default)
`"all"`	All predictors are used in both univariable and multivariable analyses
`"custom"`	Multivariable predictors are explicitly specified

Example 13: Screening-Based Selection

The default method = "screen" approach specifies that only predictors with univariable p-value below p_threshold are subsequently used in the multivariable analysis:

example13 <- fullfit(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("age", "sex", "bmi", "smoking", "diabetes",
                 "stage", "treatment"),
  model_type = "glm",
  method = "screen",
  p_threshold = 0.05,
  labels = clintrial_labels
)

example13
#> 
#> Fullfit Analysis Results
#> Outcome: readmission_30d
#> Model Type: glm
#> Method: screen (p < 0.05)
#> Predictors Screened: 7
#> Multivariable Predictors: 6
#> 
#>                    Variable   Group      n Events      OR (95% CI)   Uni p     aOR (95% CI) Multi p
#>                      <char>  <char> <char> <char>           <char>  <char>           <char>  <char>
#>  1:             Age (years)       -    850    427 1.03 (1.02-1.04) < 0.001 1.04 (1.02-1.05) < 0.001
#>  2:                     Sex  Female    450    210        reference       -        reference       -
#>  3:                            Male    400    217 1.36 (1.03-1.78)   0.027 1.39 (1.04-1.86)   0.024
#>  4: Body Mass Index (kg/m²)       -    838    418 1.00 (0.98-1.03)   0.787                -       -
#>  5:          Smoking Status   Never    337    156        reference       -        reference       -
#>  6:                          Former    311    141 0.96 (0.71-1.31)   0.808 1.00 (0.72-1.39)   0.988
#>  7:                         Current    185    119 2.09 (1.45-3.03) < 0.001 2.44 (1.65-3.59) < 0.001
#>  8:                Diabetes      No    637    301        reference       -        reference       -
#>  9:                             Yes    197    116 1.60 (1.16-2.21)   0.004 1.80 (1.28-2.53) < 0.001
#> 10:           Disease Stage       I    211     89        reference       -        reference       -
#> 11:                              II    263    126 1.26 (0.88-1.82)   0.213 1.25 (0.85-1.85)   0.251
#> 12:                             III    241    133 1.69 (1.16-2.45)   0.006 1.76 (1.18-2.63)   0.005
#> 13:                              IV    132     77 1.92 (1.23-2.98)   0.004 2.03 (1.27-3.25)   0.003
#> 14:         Treatment Group Control    196     91        reference       -        reference       -
#> 15:                          Drug A    292    127 0.89 (0.62-1.28)   0.523 0.84 (0.57-1.24)   0.380
#> 16:                          Drug B    362    209 1.58 (1.11-2.24)   0.011 1.39 (0.96-2.03)   0.083

Example 14: All Predictors

The method = "all" approach includes the same predictors in both univariable and multivariable analysis:

example14 <- fullfit(
  data = clintrial,
  outcome = "any_complication",
  predictors = c("age", "sex", "treatment", "stage"),
  model_type = "glm",
  method = "all",
  labels = clintrial_labels
)

example14
#> 
#> Fullfit Analysis Results
#> Outcome: any_complication
#> Model Type: glm
#> Method: all
#> Predictors Screened: 4
#> Multivariable Predictors: 4
#> 
#>            Variable   Group      n Events      OR (95% CI)  Uni p     aOR (95% CI) Multi p
#>              <char>  <char> <char> <char>           <char> <char>           <char>  <char>
#>  1:     Age (years)       -    850    480 1.01 (1.00-1.02)  0.220 1.01 (1.00-1.02)   0.244
#>  2:             Sex  Female    450    241        reference      -        reference       -
#>  3:                    Male    400    239 1.29 (0.98-1.69)  0.069 1.28 (0.97-1.69)   0.076
#>  4: Treatment Group Control    196    111        reference      -        reference       -
#>  5:                  Drug A    292    143 0.73 (0.51-1.06)  0.097 0.74 (0.51-1.07)   0.113
#>  6:                  Drug B    362    226 1.27 (0.89-1.81)  0.182 1.24 (0.87-1.78)   0.236
#>  7:   Disease Stage       I    211    120        reference      -        reference       -
#>  8:                      II    263    133 0.78 (0.54-1.12)  0.172 0.76 (0.52-1.10)   0.140
#>  9:                     III    241    147 1.19 (0.81-1.73)  0.374 1.14 (0.78-1.68)   0.490
#> 10:                      IV    132     77 1.06 (0.68-1.65)  0.790 1.05 (0.68-1.65)   0.815

Example 15: Custom Selection

The method = "custom" approach allows explicit specification of multivariable predictors via the multi_predictors argument:

example15 <- fullfit(
  data = clintrial,
  outcome = "icu_admission",
  predictors = c("age", "sex", "bmi", "smoking", "stage", "treatment"),
  model_type = "glm",
  method = "custom",
  multi_predictors = c("age", "sex", "stage", "treatment"),
  labels = clintrial_labels
)

example15
#> 
#> Fullfit Analysis Results
#> Outcome: icu_admission
#> Model Type: glm
#> Method: custom
#> Predictors Screened: 6
#> Multivariable Predictors: 4
#> 
#>                    Variable   Group      n Events      OR (95% CI)   Uni p     aOR (95% CI) Multi p
#>                      <char>  <char> <char> <char>           <char>  <char>           <char>  <char>
#>  1:             Age (years)       -    850    320 1.02 (1.01-1.03) < 0.001 1.02 (1.01-1.03)   0.001
#>  2:                     Sex  Female    450    173        reference       -        reference       -
#>  3:                            Male    400    147 0.93 (0.70-1.23)   0.611 0.94 (0.71-1.25)   0.680
#>  4: Body Mass Index (kg/m²)       -    838    315 0.99 (0.97-1.02)   0.717                -       -
#>  5:          Smoking Status   Never    337    123        reference       -                -       -
#>  6:                          Former    311    120 1.09 (0.80-1.50)   0.584                -       -
#>  7:                         Current    185     69 1.03 (0.71-1.50)   0.856                -       -
#>  8:           Disease Stage       I    211     76        reference       -        reference       -
#>  9:                              II    263     85 0.85 (0.58-1.24)   0.398 0.85 (0.58-1.24)   0.397
#> 10:                             III    241    105 1.37 (0.94-2.00)   0.103 1.37 (0.93-2.01)   0.113
#> 11:                              IV    132     51 1.12 (0.71-1.75)   0.625 1.12 (0.71-1.76)   0.631
#> 12:         Treatment Group Control    196     64        reference       -        reference       -
#> 13:                          Drug A    292    111 1.26 (0.86-1.85)   0.226 1.26 (0.85-1.85)   0.245
#> 14:                          Drug B    362    145 1.38 (0.96-1.99)   0.085 1.31 (0.90-1.90)   0.159

Example 16: Controlling Output Columns

The columns parameter controls which results are displayed:

# Univariable only
example16a <- fullfit(
  data = clintrial,
  outcome = "wound_infection",
  predictors = c("age", "sex", "stage"),
  model_type = "glm",
  columns = "uni"
)

example16a
#> 
#> Fullfit Analysis Results
#> Outcome: wound_infection
#> Model Type: glm
#> Method: screen (p < 0.05)
#> Predictors Screened: 3
#> 
#>    Variable  Group      n Events      OR (95% CI) p-value
#>      <char> <char> <char> <char>           <char>  <char>
#> 1:      age      -    850    167 0.99 (0.98-1.01)   0.447
#> 2:      sex Female    450     89        reference       -
#> 3:            Male    400     78 0.98 (0.70-1.38)   0.919
#> 4:    stage      I    211     51        reference       -
#> 5:              II    263     52 0.77 (0.50-1.20)   0.249
#> 6:             III    241     40 0.62 (0.39-0.99)   0.046
#> 7:              IV    132     23 0.66 (0.38-1.15)   0.141

# Multivariable only
example16b <- fullfit(
  data = clintrial,
  outcome = "wound_infection",
  predictors = c("age", "sex", "stage"),
  model_type = "glm",
  columns = "multi"
)

example16b
#> 
#> Fullfit Analysis Results
#> Outcome: wound_infection
#> Model Type: glm
#> Method: screen (p < 0.05)
#> Predictors Screened: 3
#> Multivariable Predictors: 1
#> 
#>    Variable  Group      n Events      OR (95% CI) p-value
#>      <char> <char> <char> <char>           <char>  <char>
#> 1:    stage      I    211     51        reference       -
#> 2:              II    263     52 0.77 (0.50-1.20)   0.249
#> 3:             III    241     40 0.62 (0.39-0.99)   0.046
#> 4:              IV    132     23 0.66 (0.38-1.15)   0.141

Example 17: Survival Analysis

Output tables can use Cox regression for survival outcomes by specifying Surv() notation and model_type = "coxph":

example17 <- fullfit(
  data = clintrial,
  outcome = "Surv(os_months, os_status)",
  predictors = c("age", "sex", "treatment", "stage", "ecog"),
  model_type = "coxph",
  method = "screen",
  p_threshold = 0.10,
  labels = clintrial_labels
)

example17
#> 
#> Fullfit Analysis Results
#> Outcome: Surv(os_months, os_status)
#> Model Type: coxph
#> Method: screen (p < 0.1)
#> Predictors Screened: 5
#> Multivariable Predictors: 5
#> 
#>                    Variable   Group      n Events      HR (95% CI)   Uni p     aHR (95% CI) Multi p
#>                      <char>  <char> <char> <char>           <char>  <char>           <char>  <char>
#>  1:             Age (years)       -    850    609 1.03 (1.03-1.04) < 0.001 1.04 (1.03-1.04) < 0.001
#>  2:                     Sex  Female    450    298        reference       -        reference       -
#>  3:                            Male    400    311 1.30 (1.11-1.53)   0.001 1.29 (1.10-1.52)   0.002
#>  4:         Treatment Group Control    196    151        reference       -        reference       -
#>  5:                          Drug A    292    184 0.64 (0.52-0.80) < 0.001 0.54 (0.44-0.68) < 0.001
#>  6:                          Drug B    362    274 0.94 (0.77-1.15)   0.567 0.84 (0.69-1.03)   0.095
#>  7:           Disease Stage       I    211    127        reference       -        reference       -
#>  8:                              II    263    172 1.12 (0.89-1.41)   0.337 1.13 (0.90-1.43)   0.288
#>  9:                             III    241    186 1.69 (1.35-2.11) < 0.001 1.93 (1.53-2.43) < 0.001
#> 10:                              IV    132    121 3.18 (2.47-4.09) < 0.001 3.87 (2.98-5.01) < 0.001
#> 11: ECOG Performance Status       0    265    159        reference       -        reference       -
#> 12:                               1    302    212 1.36 (1.11-1.67)   0.003 1.51 (1.22-1.85) < 0.001
#> 13:                               2    238    194 1.86 (1.51-2.29) < 0.001 1.93 (1.56-2.39) < 0.001
#> 14:                               3     37     37 3.06 (2.13-4.38) < 0.001 3.33 (2.31-4.80) < 0.001

Extended Model Types

The summata package supports several additional model types for specialized analyses. These require external packages that are suggested dependencies.

Example 18: Negative Binomial

Negative binomial models are appropriate for overdispersed count data where the variance exceeds the mean—a common violation of the Poisson assumption. This model type requires the MASS package. The ae_count variable in clintrial is generated with overdispersion, making it ideal for this demonstration.

example18 <- fit(
  data = clintrial,
  outcome = "ae_count",
  predictors = c("age", "sex", "diabetes", "treatment"),
  model_type = "negbin",
  labels = clintrial_labels
)

example18
#> 
#> Multivariable Negative Binomial Model
#> Formula: ae_count ~ age + sex + diabetes + treatment
#> n = 824, Events = 4506
#> 
#>           Variable   Group      n Events     aRR (95% CI) p-value
#>             <char>  <char> <char> <char>           <char>  <char>
#> 1:     Age (years)       -    824  4,506 1.01 (1.01-1.01) < 0.001
#> 2:             Sex  Female    440  2,248        reference       -
#> 3:                    Male    384  2,258 1.10 (0.99-1.23)   0.072
#> 4:        Diabetes      No    630  2,998        reference       -
#> 5:                     Yes    194  1,508 1.56 (1.38-1.76) < 0.001
#> 6: Treatment Group Control    189    826        reference       -
#> 7:                  Drug A    287  1,221 0.96 (0.83-1.12)   0.635
#> 8:                  Drug B    348  2,459 1.52 (1.32-1.75) < 0.001

Example 19: Gamma Regression

Gamma regression is appropriate for positive, continuous, right-skewed outcomes. In the clintrial dataset, length of hospital stay (los_days) fits this description. Note that the default link for Gamma is the inverse link, which produces coefficients on a difficult-to-interpret scale. The log link is generally preferred for interpretability—exponentiated coefficients then represent multiplicative effects on the mean.

example19 <- fit(
  data = clintrial,
  outcome = "los_days",
  predictors = c("age", "ecog", "stage", "treatment"),
  model_type = "glm",
  family = Gamma(link = "log"),
  labels = clintrial_labels
)

example19
#> 
#> Multivariable Gamma Model
#> Formula: los_days ~ age + ecog + stage + treatment
#> n = 822
#> 
#>                    Variable   Group      n Adj. Coefficient (95% CI) p-value
#>                      <char>  <char> <char>                    <char>  <char>
#>  1:             Age (years)       -    822          1.01 (1.01-1.01) < 0.001
#>  2: ECOG Performance Status       0    261                 reference       -
#>  3:                               1    295          1.14 (1.10-1.18) < 0.001
#>  4:                               2    229          1.21 (1.17-1.25) < 0.001
#>  5:                               3     37          1.29 (1.21-1.38) < 0.001
#>  6:           Disease Stage       I    206                 reference       -
#>  7:                              II    257          1.06 (1.02-1.10)   0.002
#>  8:                             III    235          1.13 (1.08-1.17) < 0.001
#>  9:                              IV    124          1.15 (1.10-1.20) < 0.001
#> 10:         Treatment Group Control    190                 reference       -
#> 11:                          Drug A    287          0.95 (0.92-0.99)   0.007
#> 12:                          Drug B    345          1.13 (1.09-1.17) < 0.001

Example 20: Random-Intercept Model

Linear mixed-effects models (LMMs) account for hierarchical or clustered data structures. The clintrial dataset includes a site variable representing the study site, making it suitable for demonstrating random effects. This model type requires the lme4 package.

Include random intercepts for study site using (1|site) notation in the predictors:

example20 <- fit(
  data = clintrial,
  outcome = "los_days",
  predictors = c("age", "sex", "treatment", "stage", "(1|site)"),
  model_type = "lmer",
  labels = clintrial_labels
)

example20
#> 
#> Multivariable Linear Mixed Model
#> Formula: los_days ~ age + sex + treatment + stage + (1|site)
#> n = 827
#> 
#>            Variable   Group      n Adj. Coefficient (95% CI) p-value
#>              <char>  <char> <char>                    <char>  <char>
#>  1:     Age (years)       -    827       0.14 (0.11 to 0.16) < 0.001
#>  2:             Sex  Female    441                 reference       -
#>  3:                    Male    386       0.82 (0.28 to 1.37)   0.003
#>  4: Treatment Group Control    190                 reference       -
#>  5:                  Drug A    288    -0.94 (-1.68 to -0.20)   0.013
#>  6:                  Drug B    349       2.41 (1.70 to 3.13) < 0.001
#>  7:   Disease Stage       I    207                 reference       -
#>  8:                      II    259       1.25 (0.51 to 1.98) < 0.001
#>  9:                     III    235       2.43 (1.67 to 3.19) < 0.001
#> 10:                      IV    126       2.96 (2.07 to 3.85) < 0.001

Example 21: GLMM for Binary Outcome

Generalized linear mixed-effects models (GLMMs) extend mixed-effects models to non-normal outcomes (binary, count). This model type also requires the lme4 package.

Model 30-day readmission with site-level random effects:

example21 <- fit(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("age", "sex", "diabetes", "treatment", "(1|site)"),
  model_type = "glmer",
  family = "binomial",
  labels = clintrial_labels
)

example21
#> 
#> Multivariable glmerMod Model
#> Formula: readmission_30d ~ age + sex + diabetes + treatment + (1|site)
#> n = 834, Events = 417
#> 
#>           Variable   Group      n Events     aOR (95% CI) p-value
#>             <char>  <char> <char> <char>           <char>  <char>
#> 1:     Age (years)       -    834    417 1.03 (1.02-1.05) < 0.001
#> 2:             Sex  Female    444    207        reference       -
#> 3:                    Male    390    210 1.36 (1.02-1.81)   0.036
#> 4:        Diabetes      No    637    301        reference       -
#> 5:                     Yes    197    116 1.65 (1.17-2.33)   0.004
#> 6: Treatment Group Control    191     88        reference       -
#> 7:                  Drug A    288    126 0.87 (0.59-1.28)   0.471
#> 8:                  Drug B    355    203 1.58 (1.09-2.28)   0.016

Example 22: GLMM for Count Outcome

For count outcomes with clustering, use fu_count (equidispersed) with site-level random effects. Standard Poisson GLMMs assume equidispersion:

example22 <- fit(
  data = clintrial,
  outcome = "fu_count",
  predictors = c("age", "stage", "treatment", "(1|site)"),
  model_type = "glmer",
  family = "poisson",
  labels = clintrial_labels
)

example22
#> 
#> Multivariable glmerMod Model
#> Formula: fu_count ~ age + stage + treatment + (1|site)
#> n = 839
#> 
#>           Variable   Group      n Events     aRR (95% CI) p-value
#>             <char>  <char> <char> <char>           <char>  <char>
#> 1:     Age (years)       -    839   <NA> 1.00 (0.99-1.00)   0.005
#> 2:   Disease Stage       I    207  1,238        reference       -
#> 3:                      II    261  1,638 1.05 (0.97-1.13)   0.233
#> 4:                     III    240  1,638 1.12 (1.04-1.21)   0.002
#> 5:                      IV    131  1,003 1.27 (1.17-1.38) < 0.001
#> 6: Treatment Group Control    191  1,169        reference       -
#> 7:                  Drug A    290  1,910 1.07 (1.00-1.16)   0.053
#> 8:                  Drug B    358  2,438 1.11 (1.03-1.19)   0.005

Example 23: Cox Mixed-Effects Model

Cox mixed-effects models account for within-cluster correlation in survival outcomes. This is useful when patients are nested within sites or physicians and outcomes may be correlated within clusters. This model type requires the coxme package. Model overall survival with site-level random effects:

example23 <- fit(
  data = clintrial,
  outcome = "Surv(os_months, os_status)",
  predictors = c("age", "sex", "treatment", "stage", "(1|site)"),
  model_type = "coxme",
  labels = clintrial_labels
)

example23
#> 
#> Multivariable Mixed Effects Cox Model
#> Formula: Surv(os_months, os_status) ~ age + sex + treatment + stage + (1|site)
#> n = 847, Events = 606
#> 
#>            Variable   Group      n Events     aHR (95% CI) p-value
#>              <char>  <char> <char> <char>           <char>  <char>
#>  1:     Age (years)       -    847    606 1.04 (1.03-1.05) < 0.001
#>  2:             Sex  Female    450    298        reference       -
#>  3:                    Male    400    311 1.36 (1.16-1.60) < 0.001
#>  4: Treatment Group Control    196    151        reference       -
#>  5:                  Drug A    292    184 0.57 (0.46-0.72) < 0.001
#>  6:                  Drug B    362    274 0.93 (0.76-1.14)   0.492
#>  7:   Disease Stage       I    211    127        reference       -
#>  8:                      II    263    172 1.17 (0.93-1.48)   0.178
#>  9:                     III    241    186 2.04 (1.62-2.58) < 0.001
#> 10:                      IV    132    121 4.09 (3.16-5.30) < 0.001

Example 24: Quasibinomial for Overdispersed Binary Data

When binary or count data exhibit overdispersion (residual deviance >> residual degrees of freedom), quasi-likelihood models provide more appropriate standard errors.

example24 <- fit(
  data = clintrial,
  outcome = "any_complication",
  predictors = c("age", "sex", "diabetes", "stage"),
  model_type = "glm",
  family = "quasibinomial",
  labels = clintrial_labels
)

example24
#> 
#> Multivariable Quasi-Binomial Model
#> Formula: any_complication ~ age + sex + diabetes + stage
#> n = 834, Events = 468
#> 
#>         Variable  Group      n Events     aOR (95% CI) p-value
#>           <char> <char> <char> <char>           <char>  <char>
#> 1:   Age (years)      -    834    468 1.01 (1.00-1.02)   0.155
#> 2:           Sex Female    444    236        reference       -
#> 3:                 Male    390    232 1.33 (1.00-1.76)   0.048
#> 4:      Diabetes     No    637    335        reference       -
#> 5:                  Yes    197    133 1.93 (1.37-2.71) < 0.001
#> 6: Disease Stage      I    207    117        reference       -
#> 7:                   II    261    131 0.75 (0.52-1.09)   0.132
#> 8:                  III    237    144 1.22 (0.83-1.79)   0.317
#> 9:                   IV    129     76 1.07 (0.68-1.69)   0.764

Example 25: Conditional Logistic Model for Matched Data

Conditional logistic regression is used for matched case-control studies where cases and controls are matched within strata. The stratification variable should be specified using the strata parameter. This model type requires the survival package.

# Matched case-control dataset (100 pairs)
set.seed(456)
n_pairs <- 100
matched_data <- do.call(rbind, lapply(1:n_pairs, function(i) {
  base_bmi <- rnorm(1, 27, 4)
  data.frame(
    match_id = i,
    case = c(1, 0),
    smoking = factor(c(rbinom(1, 1, 0.45), rbinom(1, 1, 0.30)), 
                     levels = c(0, 1), labels = c("No", "Yes")),
    diabetes = factor(c(rbinom(1, 1, 0.30), rbinom(1, 1, 0.20)), 
                      levels = c(0, 1), labels = c("No", "Yes")),
    bmi = c(base_bmi + rnorm(1, 1.5, 2), base_bmi + rnorm(1, -0.5, 2))
  )
}))

example25 <- fit(
  data = matched_data,
  outcome = "case",
  predictors = c("smoking", "diabetes", "bmi"),
  model_type = "clogit",
  strata = "match_id"
)

example25
#> 
#> Multivariable Conditional Logistic Model
#> Formula: case ~ smoking + diabetes + bmi + strata( match_id )
#> n = 124, Events = 100
#> 
#>    Variable  Group      n Events     aHR (95% CI) p-value
#>      <char> <char> <char> <char>           <char>  <char>
#> 1:  smoking     No    124    100        reference       -
#> 2:             Yes     76    100 3.46 (1.48-8.12)   0.004
#> 3: diabetes     No    140    100        reference       -
#> 4:             Yes     60    100 0.90 (0.40-2.03)   0.795
#> 5:      bmi      -    200    100 1.61 (1.30-2.00) < 0.001

The output shows odds ratios conditional on the matching strata.

Exporting Results

Regression tables can be exported to various formats:

# Microsoft Word
table2docx(
  table = example13,
  file = file.path(tempdir(), "Table2_Regression.docx"),
  caption = "Table 2. Univariable and Multivariable Analysis"
)

# PDF
table2pdf(
  table = example13,
  file = file.path(tempdir(), "Table2_Regression.pdf"),
  caption = "Table 2. Regression Results"
)

See the Table Export vignette for comprehensive documentation.

Best Practices

Variable Selection Strategy

Exploratory phase: Use uniscreen() with liberal p-threshold (e.g., 0.20)
Model building: Use fullfit() with method = "screen" or method = "custom"
Confirmatory analysis: Use fit() with pre-specified predictors
Sensitivity analysis: Compare specifications with compfit() (see Model Comparison)

Choosing the Right Model

Outcome Type	Characteristics	Recommended Model
Binary	Independent observations	`glm` with `binomial`
Binary	Overdispersed	`glm` with `quasibinomial`
Binary	Clustered/hierarchical	`glmer` with `binomial`
Binary	Matched case-control	`clogit`
Count	Mean ≈ variance	`glm` with `poisson`
Count	Variance > mean	`negbin` or `quasipoisson`
Count	Clustered	`glmer` with `poisson`
Continuous	Symmetric, normal errors	`lm`
Continuous	Positive, right-skewed	`glm` with `Gamma`
Continuous	Clustered	`lmer`
Time-to-event	Independent	`coxph`
Time-to-event	Clustered	`coxme`

Interpreting Results

The comparison between univariable and multivariable estimates reveals confounding:

Large differences: Substantial confounding present
Similar estimates: Association robust to adjustment
Sign reversal: Simpson’s paradox; investigate carefully

Sample Size Considerations

Adequate events per predictor are required for stable estimation:

Logistic/Cox models: ≥10 events per predictor (rule of thumb)
Linear models: ≥10–20 observations per predictor
Mixed-effects models: ≥5–10 clusters recommended
Use method = "screen" to reduce predictors when sample size is limited

Common Issues

Missing Data

Regression functions use complete-case analysis by default:

# Check missingness
sapply(clintrial[, c("age", "sex", "stage")], function(x) sum(is.na(x)))

# Create complete-case dataset explicitly
complete_data <- na.omit(clintrial[, c("readmission_30d", "age", "sex", "stage")])

Factor Reference Levels

Ensure reference categories are set appropriately:

# Set specific reference level
clintrial$stage <- relevel(factor(clintrial$stage), ref = "I")

Convergence Issues

For models that fail to converge:

# Access model for diagnostics
result <- fit(data, outcome, predictors, model_type = "glm")
model <- attr(result, "model")

# Check convergence
model$converged

# Large coefficients may indicate separation
coef(model)

Overdispersion Diagnostics

To check for overdispersion in Poisson or binomial models:

# Fit Poisson model
result <- fit(data, outcome, predictors, model_type = "glm", family = "poisson")
model <- attr(result, "model")

# Dispersion estimate (should be ~1 for no overdispersion)
sum(residuals(model, type = "pearson")^2) / model$df.residual

If the dispersion is substantially greater than 1, consider using quasipoisson, quasibinomial, or negbin instead.

Mixed-Effects Model Convergence

Mixed-effects models can be sensitive to starting values and optimization:

# Increase iterations
result <- fit(
  data = clintrial,
  outcome = "readmission_30d",
  predictors = c("age", "treatment", "(1|site)"),
  model_type = "glmer",
  family = "binomial",
  control = lme4::glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000))
)

Multinomial and Ordinal Outcomes

The summata package currently supports binary outcomes (via logistic regression) but not multinomial or ordinal outcomes with more than two categories. For categorical outcomes with more than two levels, use dedicated packages such as nnet::multinom() for multinomial regression or MASS::polr() for ordinal regression.

Regression Modeling