The document discusses the optimization of biomarker combinations and the estimation of sample sizes for studies on hip infections, utilizing pilot data with 24 patients and over 50,000 biomarker candidates. It emphasizes the use of Area Under the Curve (AUC) to select and combine biomarkers, revealing that a sample size of 60 is needed to validate a specific AUC value. Additionally, it concludes that while AUC is a well-established performance measurement, other metrics may serve as suitable alternatives.

1. www.helmholtz-hzi.de
Combination of informative
biomarkers in small pilot studies and estimation
of sample size for extended studies
Amani Al-Mekhlafi1,2,
Frank Klawonn1,3
Figure 1. HAUCA Curve starting from 0.85 AUC value for hip
infection dataset
Aims:
• Finding the optimal combination of biomarkers to maximize the AUC
• Estimating the sample size for extended studies
1Department of Biostatistics, Helmholtz Centre for Infection Research
2PhD student Epidemiology, Braunschweig-Hannover
3Department of Computer Science, Ostfalia University of Applied Sciences
Method:
Data:
A pilot study by Omar et al.5 has a total of 24 patients, 12 patients with chronic
periprosthetic hip infection and 12 patients with aseptic hip prosthesis loosening, and
50,416 biomarker candidates (Hip infection dataset)
Feature Selection Approach:
The classification criterion is based on area under the receiver operating characteristic
curve (AUC)
Calculation of p-value:
Based on the same statistic that is used for the Wilcoxon-Mann-Whitney-U test 6
Correction of p-value:
Holm- Bonferroni correction
Background:
Biomarker candidates are defined as measurable molecules found in biological media. According to Biomarkers Definitions Working Group, 20011, biomarkers cover a rather
wide range of parameters. Recently, biomarkers are used widely in medical researches, where single biomarkers may not possess the desired cause-effect association for
disease classification and outcome prediction2. Therefore the efforts of the researchers currently is to combine biomarkers. By new technologies like microarrays, next
generation sequencing and mass spectrometry, researchers can obtain many biomarker candidates that can exceed tens of thousands3. To avoid wasting money and time, it
is suggested to control the number of patients strictly. However, pilot studies usually have low statistical power which reduces the chance of detecting a true effect 4.
Step I:
HAUCA Curve:
A method indicating how many good biomarkers a
data set contains compared to pure random effects7
• Calculate the number of biomarkers that exceed
specific values of AUC:
 in the real dataset
 in a random dataset
• Compute 95% quantile of the binomial distribution
of each AUC value to obtain a confidence interval
In the hip infection data, there is more than random
association between the biomarker candidates and
the disease. Moreover, the study is worthwhile for
further studies.
Biomarker AUC p-value Corrected p-value
1 Bio. with high AUC 0.951 3.328e-05 1.678
2 Bio. with high AUC 0.944 4.955e-05 2.498
5 Bio. with high AUC 0.931 1.028e-04 5.183
4 Bio. with high AUC 0.924 1.442e-04 7.271
6 Bio. with high AUC 0.917 2.012e-04 10.142
9 Bio. with high AUC 0.910 2.744e-04 13.834
12 Bio. with high AUC 0.903 3.713e-04 18.718
Table 1. Top Biomarkers with highest AUC values, their p-value, and corrected p-value
Step III:
Estimate the Sample size:
• Specify the AUC value which is wanted to be validated
• Specify the prevalence of the positive cases
• Specify the number of hypothesis tests
n and n+ are increased gradually until the wanted AUC
value with a significant corrected p-value is achieved
In the hip infection data, a sample size of 60 is needed
to validate the 0.85 AUC value
Step II:
Combination of Biomarkers:
• Select the top k features according to the AUC
• Calculate within the groups:
 The difference in means for each feature
 The variance-covariance matrices between combined
features
• Calculate the AUC of the combination of possibly correlated
biomarkers according to Demler et al. 8
• Measure the lower confidence interval for this combination by
bootstrapping with different levels (0.025, 0.05, 0.1)
In the same dataset, we can notice that when 10 biomarkers are
combined, AUC value becomes close to 1 and the different lower
confidence intervals are not less than 0.95.
NO.
COMBINED
BIOMARKERS
AUC
VALUE
1 0.906186
2 0.952129
3 0.935715
4 0.955792
5 0.944372
6 0.958933
7 0.965342
8 0.978783
9 0.986358
10 0.993131
Figure 2. Curve of AUCs of the combination of the
top 20 biomarkers
Figure 3. Sample Size to validate each AUC valueTable 2. Top 10 combined
AUC values
The LEGaTO project has received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 780681.
References
1. Biomarkers Definitions Working Group: Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin. Pharmacol. Ther,2001; 69, 89–95.
2. Yan, L., Tian, L., and Liu, S. Combining large number of weak biomarkers based on AUC. Stat Med, 2015; 34(29): 3811-3830.
3. Soon, W.W., Hariharan, M., and Snyder, M.P. High-throughput sequencing for biology and medicine. Molecular systems biology,2013; 9:640.
4. Button, KS., Ioannidis, JP., Mokrysz C., Nosek BA., Flint J., Robinson ES., and Munafo MR. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;.14(5):365-76.
5. Omar, M., Klawonn, F., Brand, S., Stiesch, M. Krettek, C., and Eberhard, J.Transcriptome wide high-density microarray analysis reveals differential gene transcription in periprosthetic tissue from hips with low-grade
infection versus aseptic loosening. Journal of Arthroplasty, 2017;32: 234-240,2016.
6. Mason, S.J., Graham, N.E.: Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Quarterly Journal of the Royal Meteorological
Society 128(584) (2002) 2145–2166
7. Klawonn, F., Wang, J., Koch, I., Eberhard, J., and Omar, M. HAUCA curves for the evaluation of biomarker pilot studies with small sample sizes and large numbers of features. Advances in Intelligent Data Analysis XV,
2016; 356–367.
8. Demler, O., Pencina, M., D’Agostino, R.S.: Impact of correlation on predictive ability of biomarkers. Statistics in Medicine 32 (2013) 4196–421
Conclusion:
• AUC performance measurement has been used not just because it is well established but also it yields closed form solutions of the required calculations and therefore to fast
computation. However, other measurement like entropy, misclassification rate or mutual information might be very good alternatives to AUC.
• The correlation between biomarkers may influence the performance of their combination. Therefore it has been taken into consideration and the variance-covariance matrices
have been calculated to minimize the overoptimistic performance of the combination when independence is assumed.
• In order to statistically validate biomarker candidates from pilot studies it is necessary to estimate the required larger sample size
Result: