Report: Biomarkers for Kidney Function and Drug C Sample Size

This report is divided into two parts. Part 1 focuses on identifying biomarkers for kidney function using various urine parameters such as urine volume, specific gravity, creatinine, and different forms of nitrogen and phosphorus. The study analyzes data from eight individuals, including one patient, to determine which parameter correlates best with kidney function, with specific gravity being identified as a key indicator. Part 2 addresses sample size determination for a clinical trial of a new anti-hypertensive drug (Drug C), examining how changes in mean blood pressure reduction and standard deviation affect the required sample size. The analysis uses clincalc to calculate sample sizes under different scenarios, highlighting the impact of mean and standard deviation on the efficacy of Drug C and the overall study design. The report concludes that both mean and standard deviation have a significant impact on the sample size needed for effective clinical trials.

Running head: PART 1 AND PART 2
DATA CALCULATION
Name of the Student
Name of the University
Author note

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Abstract
This paper includes part 1 and part 2. Part 1 explains different urine parameters that plays a
role in determining which parameter is a biomarker for identification of the kidney function.
It also explains whether similar age have similar type of result. Whereas part 2 determines the
sample size for examination of given data. It also discusses whether mean and standard
deviation have any effect over sample size.

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Table of Contents
PART 1.......................................................................................................................................3
Introduction............................................................................................................................3
Method...................................................................................................................................3
Discussion..............................................................................................................................4
Conclusion..............................................................................................................................5
PART 2.......................................................................................................................................5
Introduction............................................................................................................................5
Methods..................................................................................................................................5
Discussion..............................................................................................................................6
Conclusion..............................................................................................................................6
References..................................................................................................................................7
Appendix....................................................................................................................................8

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PART 1
Introduction
Biomarker quantifiable indicator for identifying the severity or occurrence of the
disease state. It is defined as the distinctive element that is measured objectively and
evaluated as an indicator of regular pathogenic, pharmacological and biological procedures
towards reactions to a therapeutic intrusion (Perazella, 2015). Biomarker is important as it
helps in performing clinical assessment such as blood cholesterol level as well as blood
pressure and can easily monitor and assume the state of health in an individual such that
necessary therapeutic intervention can be designed.
They are useful in a various of ways, such as measuring the development of disease,
assessing the furthermost operative therapeutic administrations for a specific cancer type, in
addition to founding long-term vulnerability to cancer and its recurrence.
The aim of the part is to determine the parameter that identifies proper kidney
functioning. The hypothesis states that there will be variation in specific parameter content
for patient with different age and health.
Method
The data was collected from 8 individuals. The data shown were for 4-6 days. The
data collected was on everyday basis such 24hr. So that if we are collecting the data for 23
days it means we are calculating the data for 23rd day only. Out of 8 individuals only 1 is
patient and rest all are normal individual. Sample 8 is for patient. The chosen parameters are
UV: Urine volume; SG: Specific gravity; Cr: Creatinine; P2O5: total phosphorus content
indicator; SO3: total sulphur content indicator; Cl2: chlorine content indicator; eSO3 and
nSO3: different form of sulphur; N2: total nitrogen content; UreaN: Nitrogen in the form of

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urea; NH3N: nitrogen in the form of ammonia; UAN: uric acid nitrogen. All the data
collected as per day (24 hours) (Ledezma et al., 2015). The mean and standard deviation for
all the parameters were derived as well as graphical representation were done to show the
standard deviation for the parameters with regards to urine volume. Variability in the data
observed and analysed. The variation in data was interpreted by deriving the correlation
graph that showed which parameter is best with the urine volume.
Discussion
Variation in the data were observed. The important reason is the difference in the days
and weight of normal individual. All the data shows slight variation which is due to the
weight as well day of the month when the urine was collected. Amount of urea in urine is
very high for all individual followed by N2 whereas SG and Cr are least in amount for all
normal individuals. The normal level of urea is 12-20 gm for 24 hours all healthy individual
has urea in this range however the level of urea was low in case of patient irrespective in
which day urine is collected. Low urea in urine suggests kidney disease or malnutrition
(Mathew et al., 2016). Therefore, there is a need to check or identify the best biomarker and after
correlation all the parameters with urine volume is SG because in the graph attached can be
seen that the slope determines correlation coeff and SG has distinct slope. Researches have
shown that if SG falls between 1.002 to 1.03, it means that the kidney is functioning properly
however all the samples from patient showed SG higher than 1.01 that means there is
dehydration or problem in the kidney (Gracia-Lor et al., 2016).

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Fig 1: Correlation between urine volume mean and parameter mean
Conclusion
The strength of this research is that it helps in determining the valuable parameter in
understanding kidney defect. However, in this experiment all the samples were of same
range. Hence, more variation can be observed if there is variation in age. This research is
useful in further diagnosis of the kidney function
PART 2
Introduction
The sample size is defined as the number of patients chosen in an examination, in
addition to one of the most important practical measures needed in planning a trial is the
selection of the sample size required for answering the research questions (Perneger et al.,
2014). The sample size for this part was calculated by using clincalc tool. Drug C is the
chosen drug over here. It reduces the pressure by mean value 10mm Hg. The sample size for
the patient were calculated if there is decrease by blood pressure by 14, 15 and 16 mmHg.
The sample size is important as it helps in tracking the drug activity. As the activity of drug
depends on different aspects. Type I and Type II error explains the rejection or non-rejection
of null hypothesis (Akobeng, 2016). Hypothesis is that drug C will have similar effect sample
size will remain same with decrease in pressure (14, 15 or 16 mmHg) irrespective standard
deviation.
The main aim of this part is to identify whether the sample size is getting affected
with change in mean and standard deviation. The hypothesis for this part is that standard
deviation will decrease with sample size.

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Methods
To calculate the sample size clincalc is used. It determines the total number of
participants chosen if there is any decrease in the blood pressure 14, 15 or 16mmHg. The
calculation is then redone with change in standard deviation to 4, 5, 6 and by keeping the
blood pressure to 15mmHg only. It was done to check whether variation in mean and
standard deviation affects the sample size (Schoemann et al., 2017).
Table 1: showing variation in sample size
Pressure
assumed Standard deviation Decrease in mean (mmHg) Sample size
20mmHg 5 15 8
20mmHg 5
14 12
15 8
16 5
20mmHg
4
15
5
5 8
6 11
Discussion
It is noted from the table 1 that with the variation in mean and standard deviation
affects sample size. Standard pressure chosen 20mmHg, then if the standard deviation is 5,
the sample size is 8, whereas decrease in mean is 14 or 16 then the sample size is 12 or 5
respectively. It is seen from the data that the sample size decreases with the decreased mean
for constant standard deviation (Sedgwick, 2015). However, if the mean is constant but the
standard deviation is changed then it is observed that the sample size increases.
Conclusion
It can be concluded that standard deviation increases standard error increases.
Standard deviation decrease with sample size because the sample size gets closer to the actual
size of the population then means comes more and more around the true mean of the

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population. Drug C is effective depending on mean and standard deviation. The limitation in
this study is its sample size. Bigger sample size will give greater and clear data analysis.

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References
Akobeng, A. (2016). Understanding type I and type II errors, statistical power and sample
size. Acta Paediatrica, 105(6), 605-609. https://doi.org/10.1111/apa.13384
Gracia-Lor, E., Zuccato, E., & Castiglioni, S. (2016). Refining correction factors for back-
calculation of illicit drug use. Science Of The Total Environment, 573, 1648-
1659. https://doi.org/10.1016/j.scitotenv.2016.09.179
Ledezma, P., Kuntke, P., Buisman, C., Keller, J., & Freguia, S. (2015). Source-separated
urine opens golden opportunities for microbial electrochemical technologies. Trends
In Biotechnology, 33(4), 214-220. https://doi.org/10.1016/j.tibtech.2015.01.007
Mathew, A., Fishbane, S., Obi, Y., & Kalantar-Zadeh, K. (2016). Preservation of residual
kidney function in hemodialysis patients: reviving an old concept. Kidney
International, 90(2), 262-271. https://doi.org/10.1016/j.kint.2016.02.037
Perazella, M. (2015). The Urine Sediment as a Biomarker of Kidney Disease. American
Journal Of Kidney Diseases, 66(5), 748-755.
https://doi.org/10.1053/j.ajkd.2015.02.342
Perneger, T., Courvoisier, D., Hudelson, P., & Gayet-Ageron, A. (2014). Sample size for pre-
tests of questionnaires. Quality Of Life Research, 24(1), 147-151.
https://doi.org/10.1007/s11136-014-0752-2
Schoemann, A., Boulton, A., & Short, S. (2017). Determining Power and Sample Size for
Simple and Complex Mediation Models. Social Psychological And
Personality Science, 8(4), 379-386. https://doi.org/10.1177/1948550617715068
Sedgwick, P. (2015). Standard deviation or the standard error of the mean. BMJ, 350(feb17
1), h831-h831. https://doi.org/10.1136/bmj.h831

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Appendix
Person 1
Person 2
Person 3

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Person 4
Person 5
Person 6

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Person 7
Patient 1
Correlation
STDV for all samples (normal and patient)