scholarly journals A Simulation Study Comparing Different Statistical Approaches for the Identification of Predictive Biomarkers

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Bernhard Haller ◽  
Kurt Ulm ◽  
Alexander Hapfelmeier
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Interaction Effect ◽  
Cox Regression ◽  
The Other ◽  
Type I ◽  
Continuous Covariates ◽  
Treatment Interaction ◽  
Continuous Covariate ◽  
Statistical Approaches

Identification of relevant biomarkers that are associated with a treatment effect is one requirement for adequate treatment stratification and consequently to improve health care by administering the best available treatment to an individual patient. Various statistical approaches were proposed that allow assessing the interaction between a continuous covariate and treatment. Nevertheless, categorization of a continuous covariate, e.g., by splitting the data at the observed median value, appears to be very prevalent in practice. In this article, we present a simulation study considering data as observed in a randomized clinical trial with a time-to-event outcome performed to compare properties of such approaches, namely, Cox regression with linear interaction, Multivariable Fractional Polynomials for Interaction (MFPI), Local Partial-Likelihood Bootstrap (LPLB), and the Subpopulation Treatment Effect Pattern Plot (STEPP) method, and of strategies based on categorization of continuous covariates (splitting the covariate at the median, splitting at quartiles, and using an “optimal” split by maximizing a corresponding test statistic). In different scenarios with no interactions, linear interactions or nonlinear interactions, type I error probability and the power for detection of a true covariate-treatment interaction were estimated. The Cox regression approach was more efficient than the other methods for scenarios with monotonous interactions, especially when the number of observed events was small to moderate. When patterns of the biomarker-treatment interaction effect were more complex, MFPI and LPLB performed well compared to the other approaches. Categorization of data generally led to a loss of power, but for very complex patterns, splitting the data into multiple categories might help to explore the nature of the interaction effect. Consequently, we recommend application of statistical methods developed for assessment of interactions between continuous biomarkers and treatment instead of arbitrary or data-driven categorization of continuous covariates.

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

Correction: “Influence of Selection Bias on the Test Decision – A Simulation Study”

2014 ◽  
Vol 53 (05) ◽  
pp. 343-343
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Error Rate ◽  
Type I Error ◽  
Block Size ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rate ◽  
Representation Error ◽  
Numeric Representation

We have to report marginal changes in the empirical type I error rates for the cut-offs 2/3 and 4/7 of Table 4, Table 5 and Table 6 of the paper “Influence of Selection Bias on the Test Decision – A Simulation Study” by M. Tamm, E. Cramer, L. N. Kennes, N. Heussen (Methods Inf Med 2012; 51: 138 –143). In a small number of cases the kind of representation of numeric values in SAS has resulted in wrong categorization due to a numeric representation error of differences. We corrected the simulation by using the round function of SAS in the calculation process with the same seeds as before. For Table 4 the value for the cut-off 2/3 changes from 0.180323 to 0.153494. For Table 5 the value for the cut-off 4/7 changes from 0.144729 to 0.139626 and the value for the cut-off 2/3 changes from 0.114885 to 0.101773. For Table 6 the value for the cut-off 4/7 changes from 0.125528 to 0.122144 and the value for the cut-off 2/3 changes from 0.099488 to 0.090828. The sentence on p. 141 “E.g. for block size 4 and q = 2/3 the type I error rate is 18% (Table 4).” has to be replaced by “E.g. for block size 4 and q = 2/3 the type I error rate is 15.3% (Table 4).”. There were only minor changes smaller than 0.03. These changes do not affect the interpretation of the results or our recommendations.

scholarly journals Charged Residues Flanking the Transmembrane Domain of Two Related Toxin–Antitoxin System Toxins Affect Host Response

Toxins ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 329
Author(s):  
Andrew Holmes ◽  
Jessie Sadlon ◽  
Keith Weaver
Keyword(s):  
Amino Acid ◽  
Enterococcus Faecalis ◽  
Host Response ◽  
Cytoplasmic Membrane ◽  
Transmembrane Domain ◽  
The Other ◽  
Type I ◽  
Transcriptomic Response ◽  
Specific Amino Acid ◽  
Transporter Protein

A majority of toxins produced by type I toxin–antitoxin (TA-1) systems are small membrane-localized proteins that were initially proposed to kill cells by forming non-specific pores in the cytoplasmic membrane. The examination of the effects of numerous TA-1 systems indicates that this is not the mechanism of action of many of these proteins. Enterococcus faecalis produces two toxins of the Fst/Ldr family, one encoded on pheromone-responsive conjugative plasmids (FstpAD1) and the other on the chromosome, FstEF0409. Previous results demonstrated that overexpression of the toxins produced a differential transcriptomic response in E. faecalis cells. In this report, we identify the specific amino acid differences between the two toxins responsible for the differential response of a gene highly induced by FstpAD1 but not FstEF0409. In addition, we demonstrate that a transporter protein that is genetically linked to the chromosomal version of the TA-1 system functions to limit the toxicity of the protein.

scholarly journals Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

2021 ◽  
pp. 096228022110082
Author(s):  
Yang Li ◽  
Wei Ma ◽  
Yichen Qin ◽  
Feifang Hu
Keyword(s):  
Generalized Linear Models ◽  
Treatment Effect ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Test Statistic ◽  
Asymptotic Results ◽  
Adaptive Randomization ◽  
Adjustment Method ◽  
Inflated Type

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

scholarly journals When does the use of individual patient data in network meta-analysis make a difference? A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Steve Kanters ◽  
Mohammad Ehsanul Karim ◽  
Kristian Thorlund ◽  
Aslam Anis ◽  
Nick Bansback
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Model Performance ◽  
Patient Data ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

Burn-in procedures for a generalized model

2001 ◽  
Vol 38 (02) ◽  
pp. 542-553 ◽  
Author(s):  
Ji Hwan Cha
Keyword(s):  
The Other ◽  
Replacement Policy ◽  
Type I ◽  
Type Ii ◽  
Failure Model ◽  
Minimal Repair ◽  
Optimal Replacement ◽  
Complete Repair ◽  
Optimal Replacement Policy ◽  
Type Of Failure

In this paper two burn-in procedures for a general failure model are considered. There are two types of failure in the general failure model. One is Type I failure (minor failure) which can be removed by a minimal repair or a complete repair and the other is Type II failure (catastrophic failure) which can be removed only by a complete repair. During a burn-in process, with burn-in Procedure I, the failed component is repaired completely regardless of the type of failure, whereas, with burn-in Procedure II, only minimal repair is done for the Type I failure and a complete repair is performed for the Type II failure. In field use, the component is replaced by a new burned-in component at the ‘field use age’ T or at the time of the first Type II failure, whichever occurs first. Under the model, the problems of determining optimal burn-in time and optimal replacement policy are considered. The two burn-in procedures are compared in cases when both the procedures are applicable.

Comparison of Means: An F Test Followed by a Modified Multiple Range Procedure

1979 ◽  
Vol 4 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Juliet Popper Shaffer
Keyword(s):  
Error Control ◽  
Type I Error ◽  
Critical Value ◽  
The Other ◽  
Type I ◽  
F Test ◽  
Range Test

If used only when a preliminary F test yields significance, the usual multiple range procedures can be modified to increase the probability of detecting differences without changing the control of Type I error. The modification consists of a reduction in the critical value when comparing the largest and smallest means. Equivalence of modified and unmodified procedures in error control is demonstrated. The modified procedure is also compared with the alternative of using the unmodified range test without a preliminary F test, and it is shown that each has advantages over the other under some circumstances.

Type I Collagen Biosynthesis by Skin Fibroblasts from Patients with Idiopathic Juvenile Osteoporosis

1995 ◽  
Vol 89 (1) ◽  
pp. 69-73 ◽  
Author(s):  
Andrew E. Pocock ◽  
Martin J. O. Francis ◽  
Roger Smith
Keyword(s):  
Osteogenesis Imperfecta ◽  
Type I Collagen ◽  
The Other ◽  
Type I ◽  
Osteogenesis Imperfecta Type ◽  
Unrelated Control ◽  
Type Iii ◽  
Collagen Peptides ◽  
Idiopathic Juvenile Osteoporosis ◽  
Osteogenesis Imperfecta Type I

1. Skin fibroblast lines were cultured from nine patients who had the features of idiopathic juvenile osteoporosis, six relatives, five unrelated control subjects and three unrelated patients with osteogenesis imperfecta type I. Some patients with idiopathic juvenile osteoporosis were adults whose previous osteoporosis was in remission. Two patients with idiopathic juvenile osteoporosis were siblings and one patient with idiopathic juvenile osteoporosis had a daughter with severe osteogenesis imperfecta (type III). 2. The ratio of type III to type I collagen, synthesized by fibroblasts, was increased in two of the patients with osteogenesis imperfecta type I and in the daughter with osteogenesis imperfecta type III, but was normal in all the other patients with idiopathic juvenile osteoporosis and the other relatives. 3. Radiolabelled collagen was digested by cyanogen bromide and separated on SDS-PAGE. Unreduced collagen peptides migrated normally, except those from both the two siblings with idiopathic juvenile osteoporosis. In these two lines, abnormal migration suggested the presence of collagen I mutations. 4. The secretion of synthesized collagen by these two idiopathic juvenile osteoporosis lines and two others was reduced to only 43–45% as compared with a line from a 13-year-old control subject, which was defined as 100%. The three osteogenesis imperfecta type I lines secreted 18–37%, the other five idiopathic juvenile osteoporosis lines secreted 57–75%, the relatives (including the daughter with severe osteogenesis imperfecta) secreted 49–115% and the controls secreted 69–102%. 5. We conclude that qualitative abnormalities of type I collagen associated with a reduction in total secreted collagen synthesis may occur in a minority of patients with idiopathic juvenile osteoporosis; these patients could represent a subset of patients with this disorder.

Psychophysical interactions with a double-slit interference pattern: Exploratory evidence of a causal influence

2021 ◽  
Vol 34 (1) ◽  
pp. 79-88
Author(s):  
Dean Radin ◽  
Helané Wahbeh ◽  
Leena Michel ◽  
Arnaud Delorme
Keyword(s):  
Experimental Data ◽  
Interference Pattern ◽  
Type I Error ◽  
Human Mind ◽  
The Other ◽  
Type I ◽  
Fringe Visibility ◽  
Future Studies ◽  
Before And After ◽  
Double Slit

An experiment we conducted from 2012 to 2013, which had not been previously reported, was designed to explore possible psychophysical effects resulting from the interaction of a human mind with a quantum system. Participants focused their attention toward or away from the slits in a double-slit optical system to see if the interference pattern would be affected. Data were collected from 25 people in individual half-hour sessions; each person repeated the test ten times for a total of 250 planned sessions. “Sham” sessions designed to mimic the experimental sessions without observers present were run immediately before and after as controls. Based on the planned analysis, no evidence for a psychophysical effect was found. Because this experiment differed in two essential ways from similar, previously reported double-slit experiments, two exploratory analyses were developed, one based on a simple spectral analysis of the interference pattern and the other based on fringe visibility. For the experimental data, the outcome supported a pattern of results predicted by a causal psychophysical effect, with the spectral metric resulting in a 3.4 sigma effect (p = 0.0003), and the fringe visibility metric resulting in 7 of 22 fringes tested above 2.3 sigma after adjustment for type I error inflation, with one of those fringes at 4.3 sigma above chance (p = 0.00001). The same analyses applied to the sham data showed uniformly null outcomes. Other analyses exploring the potential that these results were due to mundane artifacts, such as fluctuations in temperature or vibration, showed no evidence of such influences. Future studies using the same protocols and analytical methods will be required to determine if these exploratory results are idiosyncratic or reflect a genuine psychophysical influence.

Investigation of age–treatment interaction in the SPACE trial using different statistical approaches

2018 ◽  
Vol 46 (9) ◽  
pp. 1689-1701 ◽  
Author(s):  
Bernhard Haller ◽  
Hans-Henning Eckstein ◽  
Peter A. Ringleb ◽  
Kurt Ulm
Keyword(s):  
Treatment Interaction ◽  
Statistical Approaches
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