Last updated: 2025-04-16

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Summary

Cortisol value calculations (includes bad quality samples, n = 41)

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
A) Standard Method (mult. by sample dilution)	17.13	29.01	32.27	35.28	39.47	82.94	4
B) Spike-Corrected Method	-45.870	-35.833	-5.960	-3.488	23.109	50.963	4
C) Spike-Corrected (Sam’s Method)	7.472	18.533	24.559	27.009	31.196	80.804	4

Cortisol value calculations (removed bad quality samples)

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
A) Standard Method (mult. by sample dilution)	18.27	29.27	31.54	34.13	37.73	69.17
B) Spike-Corrected Method	-39.371	-34.855	-20.577	-14.325	-6.825	40.400
C) Spike-Corrected (Sam’s Method)	11.80	18.45	24.09	24.05	30.32	40.40

I calculated final cortisol values in pg/mg using the following key variables:

Ave_Conc_pg/ml: average ELISA reading per sample in pg/mL
Weight_mg: hair weight in mg
Buffer_nl: assay buffer volume in nL → we convert to mL
Spike: binary indicator (1 = spiked sample)
SpikeVol_uL: volume of spike added in µL
Dilution: dilution factor (already present)
Vol_in_well.tube_uL: total volume in well/tube in µL (for spike correction)
std: standard reading value
extraction: methanol volume ratio = vol added / vol recovered (e.g. 1/0.75 ml)

Results:

Intra-assay CV: 14.5%
Intra-assay CV after removing low quality samples: 10%
Inter-assay CV: 21%

(Bindings for 20mg sample diluted in 250 uL, no spike: 64.8% and 48% in test3 and test4, respectively)

Conclusions:

Concerns: Overall quality of the plate is not great, but serial dilusions show clear parallelism and standards have values within the expected

Cortisol concentration calculations

# set reading value of spike (std1, 0.333 ug/dL), 
# and transforming to ug.dL

std <- (3191+3228)/2
std.r <- (std/10000)
std

[1] 3209.5

std.r

[1] 0.32095

# according to chatgpt, the spike's contribution is
# 1600 pg/mL, which is very similar to half of the reading for std 1 :]

Loading files and transforming units, including low quality data

df <- read.csv(file.path(data_path,"Data_QC_flagged.csv"))
kable(tail(df))

	Wells	Sample	Category	Weight_mg	Buffer_nl	Spike	SpikeVol_uL	Dilution_sample	Dilution_spike	Vol_in_well.tube_uL	Extraction_ratio	Raw.OD	Binding.Perc	Conc_pg.ml	Ave_Conc_pg.ml	CV.Perc	SD	SEM	CV_categ	Binding.Perc_categ	Failed_samples
77	G11	TP3A	P	12	220	1	25	1	1	50	1.351351	0.258	23.2	2800	2792	0.391	10.9	7.71	NA	NA	NA
78	H11	TP3A	P	12	220	1	25	1	1	50	1.351351	0.259	NA	2785	NA	NA	NA	NA	NA	NA	NA
79	A12	TP3B	P	12	60	1	25	1	1	50	1.333333	0.195	15.5	4084	4210	4.230	178.0	126.00	NA	UNDER 20% binding	UNDER 20% binding
80	B12	TP3B	P	12	60	1	25	1	1	50	1.333333	0.186	NA	4336	NA	NA	NA	NA	NA	NA	NA
81	C12	TP3C	P	12	60	1	25	1	1	50	1.333333	0.186	13.9	4336	4661	9.870	460.0	325.00	NA	UNDER 20% binding	UNDER 20% binding
82	D12	TP3C	P	12	60	1	25	1	1	50	1.333333	0.166	NA	4986	NA	NA	NA	NA	NA	NA	NA

# remove outlier
#df<- df[(df$Sample != "TP3A"),]

# Creating variables in indicated units
# dilution (buffer)
df$Buffer_ml <- c(df$Buffer_nl/1000)
df$Failed_samples[is.na(df$Failed_samples)] <- "OK"
table(df$Failed_samples)


        ABOVE 80% binding                   HIGH CV HIGH CV;ABOVE 80% binding 
                        4                         2                         7 
HIGH CV;UNDER 20% binding                        OK         UNDER 20% binding 
                        1                        60                         8

df$Failed_samples[df$Failed_samples == "ABOVE 80% binding"] <- "Out of curve"
df$Failed_samples[df$Failed_samples == "HIGH CV;ABOVE 80% binding"] <- "High CV & Out of curve"
df$Failed_samples[df$Failed_samples == "HIGH CV;UNDER 20% binding"] <- "High CV & Out of curve"
df$Failed_samples[df$Failed_samples == "HIGH CV"] <- "High CV"
df$Failed_samples[df$Failed_samples == "UNDER 20% binding"] <- "Out of curve"

df$Failed_samples <- factor(
  df$Failed_samples,
  levels = c(
    "OK",
    "Out of curve",
    "High CV",
    "High CV & Out of curve"
  )
)



# remove unnecessary information 
data <- df %>%
    dplyr::select(Wells, Sample, Category, Binding.Perc, Ave_Conc_pg.ml, Weight_mg, Buffer_ml, Spike, SpikeVol_uL, Dilution_sample, Dilution_spike, Extraction_ratio, Vol_in_well.tube_uL, Failed_samples) 



# remove duplicates
data <- data[!is.na(data$Binding.Perc), ]

kable(tail(data, 10))

	Wells	Sample	Category	Binding.Perc	Ave_Conc_pg.ml	Weight_mg	Buffer_ml	Spike	SpikeVol_uL	Dilution_sample	Dilution_spike	Extraction_ratio	Vol_in_well.tube_uL	Failed_samples
63	A10	TD7	D	99.0	86.73	20	0.11	1	110	64	64	1.333333	220	Out of curve
65	C10	TP1A	P	21.8	2986.00	6	0.06	1	25	1	1	1.333333	50	OK
67	E10	TP1B	P	18.1	3820.00	6	0.06	1	25	1	1	1.333333	50	High CV & Out of curve
69	G10	TP1C*FAIL	P	27.8	2242.00	6	0.06	1	25	1	1	1.851852	50	OK
71	A11	TP2A	P	17.3	3793.00	9	0.06	1	25	1	1	1.333333	50	Out of curve
73	C11	TP2B	P	21.1	3101.00	9	0.06	1	25	1	1	1.333333	50	OK
75	E11	TP2C	P	18.1	3634.00	9	0.06	1	25	1	1	1.333333	50	Out of curve
77	G11	TP3A	P	23.2	2792.00	12	0.22	1	25	1	1	1.351351	50	OK
79	A12	TP3B	P	15.5	4210.00	12	0.06	1	25	1	1	1.333333	50	Out of curve
81	C12	TP3C	P	13.9	4661.00	12	0.06	1	25	1	1	1.333333	50	Out of curve

dim(data)

[1] 41 14

(A) Standard Calculation

Formula:

((A/B) * (C/D) * E * 10,000) = F

A = μg/dl from assay output;
B = weight (in mg) of hair subjected to extraction;
C = vol. (in ml) of methanol added to the powdered hair;
D = vol. (in ml) of methanol recovered from the extract and subsequently dried down;
E = vol. (in ml) of assay buffer used to reconstitute the dried extract;
F = final value of hair CORT Concentration in pg/mg.

##################################
##### Calculate final values #####
##################################

# Transform to μg/dl from assay output
data$Ave_Conc_ug.dL <- c(data$Ave_Conc_pg.ml/10000)

data$Final_conc_pg.mg <- c(
    ((data$Ave_Conc_ug.dL) / data$Weight_mg) * # A/B *
      data$Extraction_ratio *                                  # C/D  *     
      data$Buffer_ml * 10000 * data$Dilution_sample)                 # E * 10000
data <- data[order(data$Sample),]
write.csv(data, file.path(data_path, "Data_cort_values_methodA.csv"), row.names = F)

# summary for all samples
data <- data %>%
  filter(!Sample %in% c("B0", "BE", "NSB", "POOL")) 
dim(data)

[1] 37 16

summary(data$Final_conc_pg.mg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  17.13   29.01   32.27   35.28   39.47   82.94

# summary for good quality samples only
temp <- data %>% 
  filter(Failed_samples == 
           "OK")
dim(temp)

[1] 18 16

summary(temp$Final_conc_pg.mg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  18.27   29.27   31.54   34.13   37.73   69.17

kable(tail(data, 7))

	Wells	Sample	Category	Binding.Perc	Ave_Conc_pg.ml	Weight_mg	Buffer_ml	Spike	SpikeVol_uL	Dilution_sample	Dilution_spike	Extraction_ratio	Vol_in_well.tube_uL	Failed_samples	Ave_Conc_ug.dL	Final_conc_pg.mg
31	G10	TP1C*FAIL	P	27.8	2242	6	0.06	1	25	1	1	1.851852	50	OK	0.2242	41.51852
32	A11	TP2A	P	17.3	3793	9	0.06	1	25	1	1	1.333333	50	Out of curve	0.3793	33.71556
33	C11	TP2B	P	21.1	3101	9	0.06	1	25	1	1	1.333333	50	OK	0.3101	27.56444
34	E11	TP2C	P	18.1	3634	9	0.06	1	25	1	1	1.333333	50	Out of curve	0.3634	32.30222
35	G11	TP3A	P	23.2	2792	12	0.22	1	25	1	1	1.351351	50	OK	0.2792	69.17117
36	A12	TP3B	P	15.5	4210	12	0.06	1	25	1	1	1.333333	50	Out of curve	0.4210	28.06667
37	C12	TP3C	P	13.9	4661	12	0.06	1	25	1	1	1.333333	50	Out of curve	0.4661	31.07333

(B) Accounting for Spike

We followed the procedure described in Nist et al. 2020:

“Thus, after pipetting 25μL of standards and samples into the appropriate wells of the 96-well assay plate, we added 25μL of the 0.333ug/dL standard to all samples, resulting in a 1:2 dilution of samples. The remainder of the manufacturer’s protocol was unchanged. We analyzed the assay plate in a Powerwave plate reader (BioTek, Winooski, VT) at 450nm and subtracted background values from all assay wells. In the calculations, we subtracted the 0.333ug/dL standard reading from the sample readings. Samples that resulted in a negative number were considered nondetectable. We converted cortisol levels from ug/dL, as measured by the assay, to pg/mg—based on the mass of hair collected and analyzed using the following formula:

A/B * C/D * E * 10,000 * 2 = F

where - A = μg/dl from assay output; - B = weight (in mg) of collected hair; - C = vol. (in ml) of methanol added to the powdered hair; - D = vol. (in ml) of methanol recovered from the extract and subsequently dried down; - E = vol. (in ml) of assay buffer used to reconstitute the dried extract; 10,000 accounts for changes in metrics; 2 accounts for the dilution factor after addition of the spike; and - F = final value of hair cortisol concentration in pg/mg”

dSpike <- data

##################################
##### Calculate final values #####
##################################

# spike is already divided by 10000 (unit is ug/dL)
dSpike$Final_conc_pg.mg <- 
  ifelse(
    dSpike$Spike == 1,    ## Only spiked samples
      ((dSpike$Ave_Conc_ug.dL - (std.r)) / # (A-spike) / B
        dSpike$Weight_mg) 
        * data$Extraction_ratio *                  # C / D
        dSpike$Buffer_ml * 10000 * 2,    # E * 10000 * 2
        dSpike$Final_conc_pg.mg  
    )


write.csv(dSpike, file.path(data_path, "Data_cort_values_methodB.csv"), row.names = F)

# summary for all samples
summary(dSpike$Final_conc_pg.mg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-45.870 -35.833  -5.960  -3.488  23.109  50.963

dSpike$Sample

 [1] "TA1"       "TA2"       "TA3"       "TA4"       "TA5"       "TA6"      
 [7] "TA7"       "TB1"       "TB2"       "TB3"       "TB4"       "TB5"      
[13] "TB6"       "TB7"       "TC1"       "TC2"       "TC3"       "TC4"      
[19] "TC5"       "TC6"       "TC7"       "TD1"       "TD2"       "TD3"      
[25] "TD4"       "TD5"       "TD6"       "TD7"       "TP1A"      "TP1B"     
[31] "TP1C*FAIL" "TP2A"      "TP2B"      "TP2C"      "TP3A"      "TP3B"     
[37] "TP3C"

# summary for all samples
dSpike <- dSpike %>%
  filter(!Sample %in% c("B0", "BE", "NSB", "POOL")) 
dim(dSpike)

[1] 37 16

summary(dSpike$Final_conc_pg.mg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-45.870 -35.833  -5.960  -3.488  23.109  50.963

# summary for good quality samples only
temp <- dSpike %>% 
  filter(Failed_samples == 
           "OK")
dim(temp)

[1] 18 16

summary(temp$Final_conc_pg.mg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-39.371 -34.855 -20.577 -14.325  -6.825  40.400

kable(tail(dSpike, 10))

	Wells	Sample	Category	Binding.Perc	Ave_Conc_pg.ml	Weight_mg	Buffer_ml	Spike	SpikeVol_uL	Dilution_sample	Dilution_spike	Extraction_ratio	Vol_in_well.tube_uL	Failed_samples	Ave_Conc_ug.dL	Final_conc_pg.mg
28	A10	TD7	D	99.0	86.73	20	0.11	1	110	64	64	1.333333	220	Out of curve	0.008673	-45.800627
29	C10	TP1A	P	21.8	2986.00	6	0.06	1	25	1	1	1.333333	50	OK	0.298600	-5.960000
30	E10	TP1B	P	18.1	3820.00	6	0.06	1	25	1	1	1.333333	50	High CV & Out of curve	0.382000	16.280000
31	G10	TP1C*FAIL	P	27.8	2242.00	6	0.06	1	25	1	1	1.851852	50	OK	0.224200	-35.833333
32	A11	TP2A	P	17.3	3793.00	9	0.06	1	25	1	1	1.333333	50	Out of curve	0.379300	10.373333
33	C11	TP2B	P	21.1	3101.00	9	0.06	1	25	1	1	1.333333	50	OK	0.310100	-1.928889
34	E11	TP2C	P	18.1	3634.00	9	0.06	1	25	1	1	1.333333	50	Out of curve	0.363400	7.546667
35	G11	TP3A	P	23.2	2792.00	12	0.22	1	25	1	1	1.351351	50	OK	0.279200	-20.686937
36	A12	TP3B	P	15.5	4210.00	12	0.06	1	25	1	1	1.333333	50	Out of curve	0.421000	13.340000
37	C12	TP3C	P	13.9	4661.00	12	0.06	1	25	1	1	1.333333	50	Out of curve	0.466100	19.353333

(C) Sam’s calculation

Simplify unnecessary unit transformations
Account for spike considering dilution of both sample and the spike

Spike contribution (pg/mL) = (Vol. spike (mL) x Conc. spike (pg/mL) ) / Vol. reconstitution (mL) or total vol. in well (50uL) (depending on where the spike was added)

# Calculate contribution of spike according to the different volumes in which it was added
# Consider that contribution of spike in serial dilutions gets smaller 

# Vol. of spike transformed to mL
data$SpikeVol_ml <- data$SpikeVol_uL/1000
# Concentration of the spike:
std

[1] 3209.5

# Vol. reconstitution (mL) is the total volume in tube or well (sample + spike), after adding spike.
# transform to mL
data$Vol_in_well.tube_ml <- data$Vol_in_well.tube_uL/1000

  ##( Spike vol. x Spike Conc.)
  ## ------------------------  / dilution = Spike contribution
  ##      Total vol. 
  
# Cortisol added by spike in wells: 0.0025 mL x 3200 pg/mL = 80 pg
# Calculate cort contribution of spike to each sample
data$Spike.cont_pg.mL <- ((data$SpikeVol_ml * std  / # Volume of spike * Spike concentration
                            data$Vol_in_well.tube_ml) / # divided by the total volume (spike + sample)
                              data$Dilution_spike) # resulting number changes depending on the dilution

dSpiked <- data
                          
##################################
##### Calculate final values #####
##################################


dSpiked$Final_conc_pg.mg <- 
     (((dSpiked$Ave_Conc_pg.ml - dSpiked$Spike.cont_pg.mL)) / # (A - spike) / B
      dSpiked$Weight_mg) *
     dSpiked$Extraction_ratio *      # C / D
      dSpiked$Buffer_ml * dSpiked$Dilution_sample    # E * 



write.csv(dSpiked, file.path(data_path, "Data_cort_values_methodC.csv"), row.names = F)

# summary for all samples

dSpiked <- dSpiked %>%
  filter(!Sample %in% c("B0", "BE", "NSB", "POOL")) 
dim(dSpiked)

[1] 37 19

summary(dSpiked$Final_conc_pg.mg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  7.472  18.533  24.559  27.009  31.196  80.804

# summary for good quality samples only
temp <- dSpiked %>% 
  filter(Failed_samples == 
           "OK")
dim(temp)

[1] 18 19

summary(temp$Final_conc_pg.mg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  11.80   18.45   24.09   24.05   30.32   40.40

kable(tail(dSpike, 10))

	Wells	Sample	Category	Binding.Perc	Ave_Conc_pg.ml	Weight_mg	Buffer_ml	Spike	SpikeVol_uL	Dilution_sample	Dilution_spike	Extraction_ratio	Vol_in_well.tube_uL	Failed_samples	Ave_Conc_ug.dL	Final_conc_pg.mg
28	A10	TD7	D	99.0	86.73	20	0.11	1	110	64	64	1.333333	220	Out of curve	0.008673	-45.800627
29	C10	TP1A	P	21.8	2986.00	6	0.06	1	25	1	1	1.333333	50	OK	0.298600	-5.960000
30	E10	TP1B	P	18.1	3820.00	6	0.06	1	25	1	1	1.333333	50	High CV & Out of curve	0.382000	16.280000
31	G10	TP1C*FAIL	P	27.8	2242.00	6	0.06	1	25	1	1	1.851852	50	OK	0.224200	-35.833333
32	A11	TP2A	P	17.3	3793.00	9	0.06	1	25	1	1	1.333333	50	Out of curve	0.379300	10.373333
33	C11	TP2B	P	21.1	3101.00	9	0.06	1	25	1	1	1.333333	50	OK	0.310100	-1.928889
34	E11	TP2C	P	18.1	3634.00	9	0.06	1	25	1	1	1.333333	50	Out of curve	0.363400	7.546667
35	G11	TP3A	P	23.2	2792.00	12	0.22	1	25	1	1	1.351351	50	OK	0.279200	-20.686937
36	A12	TP3B	P	15.5	4210.00	12	0.06	1	25	1	1	1.333333	50	Out of curve	0.421000	13.340000
37	C12	TP3C	P	13.9	4661.00	12	0.06	1	25	1	1	1.333333	50	Out of curve	0.466100	19.353333

kable(head(dSpiked[!is.na(dSpiked$Final_conc_pg.mg) , c("Sample", "Final_conc_pg.mg", "Ave_Conc_pg.ml", "Spike.cont_pg.mL", "Binding.Perc", "Weight_mg", "Buffer_ml", "SpikeVol_uL", "Dilution_sample", "Dilution_spike", "Vol_in_well.tube_uL", "Extraction_ratio")],10))

Sample	Final_conc_pg.mg	Ave_Conc_pg.ml	Spike.cont_pg.mL	Binding.Perc	Weight_mg	Buffer_ml	SpikeVol_uL	Dilution_sample	Dilution_spike	Vol_in_well.tube_uL	Extraction_ratio
TA1	31.19595	4617.00	0.0000	14.0	50	0.25	0	1	1	50	1.351351
TA2	39.47297	2921.00	0.0000	22.4	50	0.25	0	2	1	50	1.351351
TA3	30.62162	1133.00	0.0000	44.3	50	0.25	0	4	1	50	1.351351
TA4	40.40000	747.40	0.0000	55.2	50	0.25	0	8	1	50	1.351351
TA5	38.09730	352.40	0.0000	74.1	50	0.25	0	16	1	50	1.351351
TA6	48.86486	226.00	0.0000	84.2	50	0.25	0	32	1	50	1.351351
TA7	26.86703	62.13	0.0000	103.0	50	0.25	0	64	1	50	1.351351
TB1	32.28152	5134.00	291.7727	12.3	50	0.25	25	1	1	275	1.333333
TB2	31.23367	2503.00	160.4750	25.5	50	0.25	25	2	2	250	1.333333
TB3	26.87367	1088.00	80.2375	45.3	50	0.25	25	4	4	250	1.333333

Plots

(A) Standard Calculation

Version	Author	Date
16ce91c	Paloma	2025-04-10
ccad031	Paloma	2025-04-09
ced6eed	Paloma	2025-04-03

(B) Accounting for Spike

  Wells Sample Category Binding.Perc Ave_Conc_pg.ml Weight_mg Buffer_ml Spike
1    C3    TA1        A         14.0         4617.0        50      0.25    No
2    E3    TA2        A         22.4         2921.0        50      0.25    No
3    G3    TA3        A         44.3         1133.0        50      0.25    No
4    A4    TA4        A         55.2          747.4        50      0.25    No
5    C4    TA5        A         74.1          352.4        50      0.25    No
6    E4    TA6        A         84.2          226.0        50      0.25    No
  SpikeVol_uL Dilution_sample Dilution_spike Extraction_ratio
1           0               1              1         1.351351
2           0               2              1         1.351351
3           0               4              1         1.351351
4           0               8              1         1.351351
5           0              16              1         1.351351
6           0              32              1         1.351351
  Vol_in_well.tube_uL         Failed_samples Ave_Conc_ug.dL Final_conc_pg.mg
1                  50           Out of curve        0.46170         31.19595
2                  50                High CV        0.29210         39.47297
3                  50                     OK        0.11330         30.62162
4                  50                     OK        0.07474         40.40000
5                  50                     OK        0.03524         38.09730
6                  50 High CV & Out of curve        0.02260         48.86486
  Buffer
1 250 uL
2 250 uL
3 250 uL
4 250 uL
5 250 uL
6 250 uL

Version	Author	Date
16ce91c	Paloma	2025-04-10
ccad031	Paloma	2025-04-09
ced6eed	Paloma	2025-04-03

(C) Sam’s calculation

Version	Author	Date
16ce91c	Paloma	2025-04-10
ccad031	Paloma	2025-04-09
ced6eed	Paloma	2025-04-03

Evaluation method C

ggplot(dSpiked, aes(y = Final_conc_pg.mg, 
                  x = Spike.cont_pg.mL, 
                  color = (Failed_samples))) + 
                 # fill = factor(Spike.cont_pg.mL))) +
  geom_point(size = 2.5) +  
  geom_text(label = c(dSpiked$Sample), nudge_y = 0.95, nudge_x = -1.2) +
 # geom_hline(yintercept = mean(data$Final_conc_pg.mg), 
  #           color = "gray80",
   #          linetype = "dashed") +
  theme_minimal() +  
  labs(
    title = "Final Cort Concentration and Contribution of spike",
    y = "Final Concentration (pg/mg)",
    x = "Contribution of spike (pg/ml)"  
  ) +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),  
    axis.title = element_text(size = 14),  
    axis.text = element_text(size = 12) 
  )

Version	Author	Date
16ce91c	Paloma	2025-04-10
ccad031	Paloma	2025-04-09
ced6eed	Paloma	2025-04-03

sessionInfo()

R version 4.4.3 (2025-02-28)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Detroit
tzcode source: internal

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

other attached packages:
[1] dplyr_1.1.4     paletteer_1.6.0 broom_1.0.7     ggplot2_3.5.1  
[5] knitr_1.49     

loaded via a namespace (and not attached):
 [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    tidyr_1.3.1      
 [5] prismatic_1.1.2   stringi_1.8.4     digest_0.6.37     magrittr_2.0.3   
 [9] evaluate_1.0.1    grid_4.4.3        fastmap_1.2.0     rprojroot_2.0.4  
[13] workflowr_1.7.1   jsonlite_1.8.9    whisker_0.4.1     backports_1.5.0  
[17] rematch2_2.1.2    promises_1.3.0    purrr_1.0.2       fansi_1.0.6      
[21] scales_1.3.0      jquerylib_0.1.4   cli_3.6.3         rlang_1.1.4      
[25] munsell_0.5.1     withr_3.0.2       cachem_1.1.0      yaml_2.3.10      
[29] tools_4.4.3       colorspace_2.1-1  httpuv_1.6.15     vctrs_0.6.5      
[33] R6_2.5.1          lifecycle_1.0.4   git2r_0.35.0      stringr_1.5.1    
[37] fs_1.6.5          pkgconfig_2.0.3   pillar_1.9.0      bslib_0.8.0      
[41] later_1.3.2       gtable_0.3.6      glue_1.8.0        Rcpp_1.0.13-1    
[45] xfun_0.49         tibble_3.2.1      tidyselect_1.2.1  rstudioapi_0.17.1
[49] farver_2.1.2      htmltools_0.5.8.1 rmarkdown_2.29    labeling_0.4.3   
[53] compiler_4.4.3

Cortisol Concentration Values, Test4

Paloma Contreras

2025-04-16

Summary

Cortisol concentration calculations

(A) Standard Calculation

(B) Accounting for Spike

(C) Sam’s calculation

Plots

(A) Standard Calculation

(B) Accounting for Spike

(C) Sam’s calculation

Evaluation method C