PMID- 23908557
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211021
IS  - 0040-1706 (Print)
IS  - 0040-1706 (Linking)
VI  - 55
IP  - 2
DP  - 2013 May 1
TI  - Robust Analysis of High Throughput Screening (HTS) Assay Data.
PG  - 150-160
AB  - Quantitative high throughput screening (qHTS) assays use cells or tissues to 
      screen thousands of compounds in a short period of time. Data generated from qHTS 
      assays are then evaluated using nonlinear regression models, such as the Hill 
      model, and decisions regarding toxicity are made using the estimates of the 
      parameters of the model. For any given compound, the variability in the observed 
      response may either be constant across dose groups (homoscedasticity) or vary 
      with dose (heteroscedasticity). Since thousands of compounds are simultaneously 
      evaluated in a qHTS assay, it is not practically feasible for an investigator to 
      perform residual analysis to determine the variance structure before performing 
      statistical inferences on each compound. Since it is well-known that the variance 
      structure plays an important role in the analysis of linear and nonlinear 
      regression models it is therefore important to have practically useful and easy 
      to interpret methodology which is robust to the variance structure. Furthermore, 
      given the number of chemicals that are investigated in the qHTS assay, outliers 
      and influential observations are not uncommon. In this article we describe 
      preliminary test estimation (PTE) based methodology which is robust to the 
      variance structure as well as any potential outliers and influential 
      observations. Performance of the proposed methodology is evaluated in terms of 
      false discovery rate (FDR) and power using a simulation study mimicking a real 
      qHTS data. Of the two methods currently in use, our simulations studies suggest 
      that one is extremely conservative with very small power in comparison to the 
      proposed PTE based method whereas the other method is very liberal. In contrast, 
      the proposed PTE based methodology achieves a better control of FDR while 
      maintaining good power. The proposed methodology is illustrated using a data set 
      obtained from the National Toxicology Program (NTP). Additional information, 
      simulation results, data and computer code are available online as supplementary 
      materials.
FAU - Lim, Changwon
AU  - Lim C
AD  - Department of Mathematics and Statistics, Loyola University Chicago, 1032 W 
      Sheridan Rd, Chicago, IL 60660.
FAU - Sen, Pranab K
AU  - Sen PK
FAU - Peddada, Shyamal D
AU  - Peddada SD
LA  - eng
GR  - Z01 ES101744/ImNIH/Intramural NIH HHS/United States
GR  - Z01 ES101744-04/ImNIH/Intramural NIH HHS/United States
PT  - Journal Article
PL  - United States
TA  - Technometrics
JT  - Technometrics : a journal of statistics for the physical, chemical, and 
      engineering sciences
JID - 0404554
PMC - PMC3727440
MID - NIHMS426026
OTO - NOTNLM
OT  - Dose-response study
OT  - False discovery rate (FDR)
OT  - Heteroscedasticity
OT  - Hill model
OT  - M-estimation procedure
OT  - Nonlinear regression model
OT  - Power
OT  - Toxicology
EDAT- 2013/08/03 06:00
MHDA- 2013/08/03 06:01
PMCR- 2013/07/30
CRDT- 2013/08/03 06:00
PHST- 2013/08/03 06:00 [entrez]
PHST- 2013/08/03 06:00 [pubmed]
PHST- 2013/08/03 06:01 [medline]
PHST- 2013/07/30 00:00 [pmc-release]
AID - 10.1080/00401706.2012.749166 [doi]
PST - ppublish
SO  - Technometrics. 2013 May 1;55(2):150-160. doi: 10.1080/00401706.2012.749166.