Calculates effect estimates from models with all possible combinations of variables. Effect estimates here can be regression coefficients, odds ratios and hazard ratios depending on modeling methods. This approach can be used for assessing the treatment effects in clinical trials or the effects of risk factors in observational biomedical and epidemiological studies.
You can install the released version of allestimates from CRAN with:
install.packages("allestimates")library(allestimates)all_coxRun Cox Proportional Hazards Regression Models with all possible combinations of variables specified in the argument of xlist using coxph from survival package.
vlist <- c("Age", "Sex", "Smoke", "Married", "BMI", "Income")
results <- all_cox(crude = "Surv(t0, t1, Endpoint) ~ Diabetes", xlist = vlist, data = diab_df)
#> estimate: Hazard ratio
#> Crude model: Surv(t0, t1, Endpoint) ~ Diabetes
results
#> $estimate
#> variables estimate conf_low conf_high p aic n
#> 1 Crude 1.602936 1.447701 1.774817 1.096400e-19 19811.20 2050
#> 2 Age 1.604603 1.448449 1.777593 1.397829e-19 19813.16 2050
#> 3 Sex 1.608607 1.452824 1.781094 5.853751e-20 19770.94 2050
#> 4 Smoke 1.644631 1.485056 1.821353 1.248088e-21 19759.22 2050
#> 5 Married 1.603902 1.448565 1.775898 9.905273e-20 19806.36 2050
#> 6 BMI 1.570416 1.416793 1.740696 8.465830e-18 19805.82 2050
#> 7 Income 1.548745 1.397960 1.715793 5.748060e-17 19777.69 2050
#> 8 Age, Sex 1.608208 1.451736 1.781544 9.245663e-20 19772.94 2050
#> 9 Age, Smoke 1.645750 1.485241 1.823605 1.812683e-21 19761.20 2050
#> 10 Age, Married 1.608235 1.451707 1.781640 9.500244e-20 19808.10 2050
#> 11 Age, BMI 1.570428 1.415933 1.741779 1.316518e-17 19807.82 2050
#> 12 Age, Income 1.558416 1.406244 1.727055 2.600521e-17 19777.39 2050
#> 13 Sex, Smoke 1.637362 1.478517 1.813272 2.785608e-21 19742.94 2050
#> 14 Sex, Married 1.618358 1.461535 1.792007 2.095449e-20 19748.50 2050
#> 15 Sex, BMI 1.571950 1.418344 1.742191 6.605897e-18 19762.21 2050
#> 16 Sex, Income 1.543966 1.393714 1.710416 9.166515e-17 19719.18 2050
#> 17 Smoke, Married 1.650623 1.490375 1.828101 6.710436e-22 19749.77 2050
#> 18 Smoke, BMI 1.610709 1.452963 1.785580 1.252512e-19 19752.97 2050
#> 19 Smoke, Income 1.585644 1.431021 1.756973 1.295859e-18 19720.88 2050
#> 20 Married, BMI 1.572164 1.418387 1.742613 6.978273e-18 19801.26 2050
#> 21 Married, Income 1.552276 1.401057 1.719815 4.148424e-17 19778.25 2050
#> 22 BMI, Income 1.521289 1.371681 1.687214 1.965444e-15 19773.96 2050
#> 23 Age, Sex, Smoke 1.637401 1.477751 1.814300 4.473889e-21 19744.94 2050
#> 24 Age, Sex, Married 1.622202 1.464266 1.797172 2.101959e-20 19750.29 2050
#> 25 Age, Sex, BMI 1.569302 1.415133 1.740266 1.328561e-17 19764.12 2050
#> 26 Age, Sex, Income 1.554042 1.402388 1.722097 3.932443e-17 19718.55 2050
#> 27 Age, Smoke, Married 1.655242 1.493685 1.834273 6.750476e-22 19751.52 2050
#> 28 Age, Smoke, BMI 1.609797 1.451193 1.785734 2.326315e-19 19754.96 2050
#> 29 Age, Smoke, Income 1.596162 1.440008 1.769249 5.488279e-19 19720.52 2050
#> 30 Age, Married, BMI 1.574987 1.420034 1.746847 8.215646e-18 19803.16 2050
#> 31 Age, Married, Income 1.562798 1.410079 1.732056 1.742013e-17 19777.71 2050
#> 32 Age, BMI, Income 1.530640 1.379531 1.698300 1.000052e-15 19774.24 2050
#> 33 Sex, Smoke, Married 1.647630 1.487682 1.824775 9.355592e-22 19720.79 2050
#> 34 Sex, Smoke, BMI 1.600443 1.443803 1.774078 3.592314e-19 19734.46 2050
#> 35 Sex, Smoke, Income 1.568890 1.415926 1.738379 7.654131e-18 19692.48 2050
#> 36 Sex, Married, BMI 1.582055 1.427439 1.753419 2.284029e-18 19739.49 2050
#> 37 Sex, Married, Income 1.559178 1.407253 1.727505 2.041332e-17 19708.34 2050
#> 38 Sex, BMI, Income 1.511316 1.362903 1.675890 4.844829e-15 19711.83 2050
#> 39 Smoke, Married, BMI 1.617442 1.458970 1.793128 6.264461e-20 19743.89 2050
#> 40 Smoke, Married, Income 1.593862 1.438213 1.766356 6.039354e-19 19719.32 2050
#> 41 Smoke, BMI, Income 1.555344 1.402225 1.725183 6.637521e-17 19715.81 2050
#> 42 Married, BMI, Income 1.524832 1.374795 1.691244 1.428294e-15 19774.52 2050
#> 43 Age, Sex, Smoke, Married 1.652106 1.490910 1.830729 9.272965e-22 19722.54 2050
#> 44 Age, Sex, Smoke, BMI 1.598056 1.440739 1.772550 7.577121e-19 19736.39 2050
#> 45 Age, Sex, Smoke, Income 1.579718 1.425231 1.750950 3.086309e-18 19691.73 2050
#> 46 Age, Sex, Married, BMI 1.583848 1.428209 1.756447 2.942044e-18 19741.45 2050
#> 47 Age, Sex, Married, Income 1.570948 1.417426 1.741098 7.399515e-18 19707.03 2050
#> 48 Age, Sex, BMI, Income 1.520592 1.370732 1.686836 2.433956e-15 19711.98 2050
#> 49 Age, Smoke, Married, BMI 1.620219 1.460473 1.797438 8.109894e-20 19745.81 2050
#> 50 Age, Smoke, Married, Income 1.605552 1.448221 1.779975 2.298658e-19 19718.62 2050
#> 51 Age, Smoke, BMI, Income 1.565254 1.410518 1.736966 3.272296e-17 19716.11 2050
#> 52 Age, Married, BMI, Income 1.535060 1.383400 1.703346 6.759584e-16 19774.60 2050
#> 53 Sex, Smoke, Married, BMI 1.611052 1.453335 1.785884 1.165826e-19 19712.07 2050
#> 54 Sex, Smoke, Married, Income 1.584644 1.429924 1.756106 1.600942e-18 19681.94 2050
#> 55 Sex, Smoke, BMI, Income 1.535048 1.384039 1.702535 5.011866e-16 19684.80 2050
#> 56 Sex, Married, BMI, Income 1.526068 1.376062 1.692426 1.175969e-15 19700.47 2050
#> 57 Smoke, Married, BMI, Income 1.563486 1.409350 1.734479 3.183370e-17 19714.30 2050
#> 58 Age, Sex, Smoke, Married, BMI 1.613341 1.454467 1.789568 1.523950e-19 19714.01 2050
#> 59 Age, Sex, Smoke, Married, Income 1.597132 1.440704 1.770545 5.449028e-19 19680.52 2050
#> 60 Age, Sex, Smoke, BMI, Income 1.545005 1.392429 1.714300 2.399979e-16 19684.88 2050
#> 61 Age, Sex, Married, BMI, Income 1.537049 1.385401 1.705297 5.025127e-16 19700.05 2050
#> 62 Age, Smoke, Married, BMI, Income 1.574620 1.418704 1.747672 1.412887e-17 19714.29 2050
#> 63 Sex, Smoke, Married, BMI, Income 1.550384 1.397689 1.719760 1.139567e-16 19673.84 2050
#> 64 Age, Sex, Smoke, Married, BMI, Income 1.562040 1.407589 1.733439 4.625451e-17 19673.34 2050
#>
#> $xlist
#> [1] "Age" "Sex" "Smoke" "Married" "BMI" "Income"
#>
#> $fun
#> [1] "all_cox"
#>
#> $crude
#> [1] "Surv(t0, t1, Endpoint) ~ Diabetes"
#>
#> $family
#> [1] "coxph"All hazard ratio estimates are stored in the object results which can be used for further analysis and graphic presentation.
all_plotall_plot quickly presents all hazard ratio (effect) estimates in the graph divided into four parts (quarters).
all_plot(results)
In this example, all hazard ratio estimates are in the left-upper quarter of the graph, indicating a positive association between Diabetes and Endpoint.
all_plot2all_plot2(results)
Plot hazard ratios (effect estimates) indicating whether each of those variables included and not included in the model.
all_glm or all_speedglmall_speedglm calling speedglm is a faster alternative of all_glm which calls glm. By default, those functions run logistic regressions to calculate odds ratios.
diab_df$Overweight = as.numeric(diab_df$BMI >= 25)
vlist <- c("Age", "Sex", "Education","Married", "BMI", "Income")
results <- all_speedglm(crude = "Diabetes ~ Overweight", xlist = vlist, data = diab_df)
#> estimate: Odds Ratio or Rate Ratio
#> Crude model: Diabetes ~ Overweight
all_plot(results)
all_plot2(results)
In this example, odds ratio estimates appeared in two upper quarters, a positive association with alpha levels less than 0.05 in some models (left-upper) and higher than 0.05 in others. Since different models produce conflicting results, inspecting all_plot2 can be helpful in combination with biological background knowledge. As we can see the above plot, estimates were in the right-hand side of the vertical line when BMI was included in models. In this case, including BMI when assessing the association between Overweight and Diabetes can be problematic because Overweight was determined based on BMI.
all_plot_aic2(results)
all_lmall_lm fits Linear Regression Models with all possible combinations of variables specified in the argument of xlist using lm from stats package.
vlist <- c("Age", "Sex", "Education", "Income", "Diabetes", "Smoke")
results <- all_lm(crude = "BMI ~ Married", xlist = vlist, data = diab_df)
#> estimate: Coefficient
#> Crude model: BMI ~ Married
all_plot(results)
all_plot2(results)
All estimates are in the right-hand side of the vertical line. The results does not support an association between the marital status Married and BMI.
Note: If you want to add non-linear terms or interaction terms, generate those terms as variables first and add them to the argument of xlist.