EVO vs.LASIK
Eric Cantu
2026-07-08
#### Cleaning data for new and clean data set along with calculating age and erasing patient info after
el <- EL_DATA %>%
rename(
DOS = DOS,
Name = NAME,
DOB = DOB,
Procedure = PROCEDURE,
Eye = EYE,
Target = `TARGET (DISTANCE or NEAR)`,
Pre_Sphere = `SPHERE (pre-op)`,
Pre_Cyl = `CYL (pre-op)`,
Pre_Axis = `AXIS (pre-op)`,
Lasik_Type = `TYPE (Contoura vs WFO if LASIK)`,
POD1_VA = `POD1 VA`,
POW1_VA = `POW1 VA`,
POM1_VA = `POM1 or 2 VA`,
Post_Sphere = `SPHERE (post-op)`,
Post_Cyl = `CYL (post-op)`,
Post_Axis = `AXIS (post-op)`,
Enhancement = `ENH? (YES or NO)`,
Enh_Sphere = `ENH SPH`,
Enh_Cyl = `ENH CYL`,
Enh_Axis = `ENH AXIS`,
ICL_Exchange_Reason = `Reason for Exchange (ICL)`
)
el <- el %>%
mutate(
DOS = mdy(DOS),
DOB = mdy(DOB),
Age = as.numeric(DOS - DOB) / 365.25
) %>%
select(-Name, -DOB, -DOS)#### further cleaning more focused on prescription section will also be showing where NA values or miss inputs may be hidden
el <- el %>%
mutate(
Pre_Cyl = ifelse(Pre_Cyl %in% c("Sph", "SPH", "sph", "Plano", "plano"), 0, Pre_Cyl),
Post_Cyl = ifelse(Post_Cyl %in% c("Sph", "SPH", "sph", "Plano", "plano"), 0, Post_Cyl),
Pre_Sphere = as.numeric(Pre_Sphere),
Pre_Cyl = as.numeric(Pre_Cyl),
Pre_Axis = as.numeric(Pre_Axis),
Post_Sphere = as.numeric(Post_Sphere),
Post_Cyl = as.numeric(Post_Cyl),
Post_Axis = as.numeric(Post_Axis)
)
EL_DATA %>%
filter(is.na(as.numeric(`SPHERE (pre-op)`))) %>%
select(`SPHERE (pre-op)`) %>%
distinct()## # A tibble: 3 × 1
## `SPHERE (pre-op)`
## <chr>
## 1 <NA>
## 2 -3/75
## 3 --9.00### stats for calculations residual
el <- el %>%
mutate(
Pre_SE = Pre_Sphere + (Pre_Cyl / 2),
Post_SE = Post_Sphere + (Post_Cyl / 2),
Residual_SE = abs(Post_SE),
Change_SE = Post_SE - Pre_SE,
Abs_Cyl_Post = abs(Post_Cyl)
)#### categorical variables
el <- el %>%
mutate(
Procedure = str_trim(Procedure),
Procedure = case_when(
str_detect(Procedure, regex("EVO", ignore_case = TRUE)) ~ "EVO",
str_detect(Procedure, regex("LASIK", ignore_case = TRUE)) ~ "LASIK",
TRUE ~ Procedure
),
Lasik_Type = str_trim(Lasik_Type),
Lasik_Type = case_when(
str_detect(Lasik_Type, regex("contoura", ignore_case = TRUE)) ~ "Contoura",
str_detect(Lasik_Type, regex("WFO", ignore_case = TRUE)) ~ "WFO",
TRUE ~ Lasik_Type
),
Enhancement = str_trim(Enhancement),
Enhancement = case_when(
str_detect(Enhancement, regex("^yes$|^y$", ignore_case = TRUE)) ~ "Yes",
str_detect(Enhancement, regex("^no$|^n$", ignore_case = TRUE)) ~ "No",
TRUE ~ NA_character_
),
Procedure = factor(Procedure),
Lasik_Type = factor(Lasik_Type),
Enhancement = factor(Enhancement, levels = c("No", "Yes"))
)### scope of NA values as suspected
####Abs_Cyl_Post = amount of residual astigmatism Change_SE = how much the patient's refractive error changed because of surgery
####Residual_SE = absolute residual error Spherical Equivalent (SE) Pre_SE = (Pre-operative Spherical Equivalent) Post_SE = Post-operative Spherical Equivalent
table(el$Procedure, useNA = "ifany")##
## EVO LASIK
## 282 286table(el$Eye, useNA = "ifany")##
## OD OS <NA>
## 288 277 3table(el$Procedure, el$Eye, useNA = "ifany")##
## OD OS <NA>
## EVO 145 136 1
## LASIK 143 141 2table(el$Lasik_Type, useNA = "ifany")##
## Contoura WFO <NA>
## 68 216 284table(el$Enhancement, useNA = "ifany")##
## No Yes <NA>
## 525 6 37summary_by_procedure <- el %>%
group_by(Procedure) %>%
summarise(
n = n(),
Mean_Age = mean(Age, na.rm = TRUE),
SD_Age = sd(Age, na.rm = TRUE),
Mean_Pre_SE = mean(Pre_SE, na.rm = TRUE),
SD_Pre_SE = sd(Pre_SE, na.rm = TRUE),
Mean_Post_SE = mean(Post_SE, na.rm = TRUE),
SD_Post_SE = sd(Post_SE, na.rm = TRUE),
Mean_Residual_SE = mean(Residual_SE, na.rm = TRUE),
SD_Residual_SE = sd(Residual_SE, na.rm = TRUE),
Mean_Post_Cyl = mean(Abs_Cyl_Post, na.rm = TRUE),
SD_Post_Cyl = sd(Abs_Cyl_Post, na.rm = TRUE)
)
summary_by_procedure## # A tibble: 2 × 12
## Procedure n Mean_Age SD_Age Mean_Pre_SE SD_Pre_SE Mean_Post_SE SD_Post_SE
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EVO 282 34.1 11.0 -6.69 2.92 -0.216 0.512
## 2 LASIK 286 33.8 9.17 -2.70 1.85 -0.167 0.490
## # ℹ 4 more variables: Mean_Residual_SE <dbl>, SD_Residual_SE <dbl>,
## # Mean_Post_Cyl <dbl>, SD_Post_Cyl <dbl>#### Compare residual refractive error t test
t.test(Residual_SE ~ Procedure, data = el)##
## Welch Two Sample t-test
##
## data: Residual_SE by Procedure
## t = 0.91321, df = 427.7, p-value = 0.3616
## alternative hypothesis: true difference in means between group EVO and group LASIK is not equal to 0
## 95 percent confidence interval:
## -0.04003627 0.10952378
## sample estimates:
## mean in group EVO mean in group LASIK
## 0.3796512 0.3449074wilcox.test(Residual_SE ~ Procedure, data = el)##
## Wilcoxon rank sum test with continuity correction
##
## data: Residual_SE by Procedure
## W = 24346, p-value = 0.3772
## alternative hypothesis: true location shift is not equal to 0Wilcoxon rank-sum test is a nonparametric alternative that compares the distributions without assuming normality.
#### LASIK comparison
lasik_only <- el %>%
filter(Procedure == "LASIK",
Lasik_Type %in% c("Contoura", "WFO"))
table(lasik_only$Lasik_Type, useNA = "ifany")##
## Contoura WFO
## 68 216lasik_summary <- lasik_only %>%
group_by(Lasik_Type) %>%
summarise(
n = n(),
Mean_Pre_SE = mean(Pre_SE, na.rm = TRUE),
SD_Pre_SE = sd(Pre_SE, na.rm = TRUE),
Mean_Post_SE = mean(Post_SE, na.rm = TRUE),
SD_Post_SE = sd(Post_SE, na.rm = TRUE),
Mean_Residual_SE = mean(Residual_SE, na.rm = TRUE),
SD_Residual_SE = sd(Residual_SE, na.rm = TRUE),
Mean_Post_Cyl = mean(Abs_Cyl_Post, na.rm = TRUE),
SD_Post_Cyl = sd(Abs_Cyl_Post, na.rm = TRUE)
)
lasik_summary## # A tibble: 2 × 10
## Lasik_Type n Mean_Pre_SE SD_Pre_SE Mean_Post_SE SD_Post_SE
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Contoura 68 -3.44 1.54 -0.152 0.418
## 2 WFO 216 -2.47 1.89 -0.171 0.510
## # ℹ 4 more variables: Mean_Residual_SE <dbl>, SD_Residual_SE <dbl>,
## # Mean_Post_Cyl <dbl>, SD_Post_Cyl <dbl>t.test(Residual_SE ~ Lasik_Type, data = lasik_only)##
## Welch Two Sample t-test
##
## data: Residual_SE by Lasik_Type
## t = -0.42911, df = 109.25, p-value = 0.6687
## alternative hypothesis: true difference in means between group Contoura and group WFO is not equal to 0
## 95 percent confidence interval:
## -0.12726679 0.08196559
## sample estimates:
## mean in group Contoura mean in group WFO
## 0.3275000 0.3501506wilcox.test(Residual_SE ~ Lasik_Type, data = lasik_only)##
## Wilcoxon rank sum test with continuity correction
##
## data: Residual_SE by Lasik_Type
## W = 4354, p-value = 0.592
## alternative hypothesis: true location shift is not equal to 0#### Plots
ggplot(el, aes(x = Procedure, y = Pre_SE, fill = Procedure)) +
geom_boxplot(alpha = 0.7) +
geom_jitter(width = 0.15, alpha = 0.4) +
labs(
title = "Pre-operative Spherical Equivalent by Procedure",
x = "Procedure",
y = "Pre-op Spherical Equivalent (D)"
) +
theme_minimal()
ggplot(el, aes(x = Procedure, y = Residual_SE, fill = Procedure)) +
geom_boxplot(alpha = 0.7) +
geom_jitter(width = 0.15, alpha = 0.4) +
labs(
title = "Residual Refractive Error by Procedure",
x = "Procedure",
y = "Absolute Post-op SE (D)"
) +
theme_minimal()
ggplot(lasik_only, aes(x = Lasik_Type, y = Residual_SE, fill = Lasik_Type)) +
geom_boxplot(alpha = 0.7) +
geom_jitter(width = 0.15, alpha = 0.4) +
labs(
title = "Residual SE: Contoura vs WFO LASIK",
x = "LASIK Type",
y = "Absolute Post-op SE (D)"
) +
theme_minimal()
ggplot(el, aes(x = Pre_SE, y = Post_SE, color = Procedure)) +
geom_point(alpha = 0.6) +
geom_hline(yintercept = 0, linetype = "dashed") +
labs(
title = "Pre-op SE vs Post-op SE",
x = "Pre-op Spherical Equivalent (D)",
y = "Post-op Spherical Equivalent (D)"
) +
theme_minimal()