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Empirical Comparison of Publication Bias Tests in Meta-Analysis
BackgroundDecision makers rely on meta-analytic estimates to trade off benefits and harms. Publication bias impairs the validity and generalizability of such estimates. The performance of various statistical tests for publication bias has been largely compared using simulation studies and has not been systematically evaluated in empirical data.MethodsThis study compares seven commonly used publication bias tests (i.e., Begg’s rank test, trim-and-fill, Egger’s, Tang’s, Macaskill’s, Deeks’, and Peters’ regression tests) based on 28,655 meta-analyses available in the Cochrane Library.ResultsEgger’s regression test detected publication bias more frequently than other tests (15.7% in meta-analyses of binary outcomes and 13.5% in meta-analyses of non-binary outcomes). The proportion of statistically significant publication bias tests was greater for larger meta-analyses, especially for Begg’s rank test and the trim-and-fill method. The agreement among Tang’s, Macaskill’s, Deeks’, and Peters’ regression tests for binary outcomes was moderately strong (most κ’s were around 0.6). Tang’s and Deeks’ tests had fairly similar performance (κ > 0.9). The agreement among Begg’s rank test, the trim-and-fill method, and Egger’s regression test was weak or moderate (κ < 0.5).ConclusionsGiven the relatively low agreement between many publication bias tests, meta-analysts should not rely on a single test and may apply multiple tests with various assumptions. Non-statistical approaches to evaluating publication bias (e.g., searching clinical trials registries, records of drug approving agencies, and scientific conference proceedings) remain essential.
Journal Article
A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics
In an age when machine learning and artificial intelligence are broadly employed, traditional statistics can still provide insightful information and results quickly and at a low computational cost. Statistics, in fact, offers many useful tools to researchers, including a series of univariate statistical tests that can identify relationships between pairs of numeric samples: Student’s
t
-test, Mann-Whitney
U
test, Chi-squared test, and Kruskal-Wallis test. These tests generate several outcomes, including probability values (
p
-values) that can express a numerical quantity which accepts or rejects the null hypothesis, based on a certain threshold used. Although effective, these tests are often misused or employed in the wrong contexts, especially among biostatistics studies. Many scientific researchers do not seem to know how to choose one test over the others, and this misuse can lead to incorrect results and wrong conclusions. Here we present a simple theoretical and practical guide to the use of these four tests, first describing their theoretical properties and then displaying the results obtained by applying these tests to real-world medical datasets. Eventually, we explain when and how to use each test based on the data types of the samples considered. Our study can have a strong impact on scientific research by potentially influencing future studies involving these tests. Our recommendations, in turn, can help researchers produce more reliable and sound scientific results, thus increasing the quality of multiple scientific studies across various fields.
Journal Article
T-Friedman Test: A New Statistical Test for Multiple Comparison with an Adjustable Conservativeness Measure
To prove that a certain algorithm is superior to the benchmark algorithms, the statistical hypothesis tests are commonly adopted with experimental results on a number of datasets. Some statistical hypothesis tests draw statistical test results more conservative than the others, while it is not yet possible to characterize quantitatively the degree of conservativeness of such a statistical test. On the basis of the existing nonparametric statistical tests, this paper proposes a new statistical test for multiple comparison which is named as t-Friedman test. T-Friedman test combines
t
test with Friedman test for multiple comparison. The confidence level of the
t
test is adopted as a measure of conservativeness of the proposed t-Friedman test. A bigger confidence level infers a higher degree of conservativeness, and vice versa. Based on the synthetic results generated by Monte Carlo simulations with predefined distributions, the performance of several state-of-the-art multiple comparison tests and post hoc procedures are first qualitatively analyzed. The influences of the type of predefined distribution, the number of benchmark algorithms and the number of datasets are explored in the experiments. The conservativeness measure of the proposed method is also validated and verified in the experiments. Finally, some suggestions for the application of these nonparametric statistical tests are provided.
Journal Article
Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms
In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1×N and N ×N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.
Journal Article
New comparative approach to multi-level thresholding: chaotically initialized adaptive meta-heuristic optimization methods
One method aimed at enhancing the performance of meta-heuristic optimization techniques is the incorporation of chaotic systems. Instead of irregular distributions in the search space, chaotic distributions are employed in the initial population of optimization algorithms to improve the efficiency of the search process. This approach enables search agents distributed in a chaotic manner to effectively explore the search space. The initial populations of both the well-established PSO algorithm and the enhanced WSO algorithm, which incorporates advanced search techniques, are distributed in the search space according to the characteristics of Logistic, Chebyshev, Circle, Sine, and Piecewise chaotic maps in this study. The original PSO and WSO algorithms, as well as the resulting chaotically initialized PSO and chaotically initialized WSO algorithms, were tested using 23 benchmark functions. Subsequently, the Otsu method was integrated into the tested optimization algorithms to obtain multi-level thresholding values. These algorithms were applied to five different test images with a manually determined number of thresholds. The results obtained were presented in the study and evaluated using statistical tests.
Journal Article
Spatio-temporal trends in long-term seasonal groundwater level of South-western Punjab using non-parametric statistical tests
To manage groundwater resources and develop an action plan, it is crucial to understand the long-term behavior of groundwater level (GWL) fluctuations. In this study, Geographic Information System (GIS) and non-parametric statistical tests were applied for detecting long-term (1973 to 2020) spatio-temporal variations and trends in GWL from 137 observation wells evenly distributed across the south-western part of Punjab. This region has experienced significant changes in GWL over the decades. The non-parametric statistical tests included Mann–Kendall (MK), Sens’s Slope Estimator (SSE), and Innovative Trend Analysis (ITA). The study observed significant trends in GWL fluctuations before and after monsoon. The MK and SSE tests showed a statistically increasing trend in observation wells with about 65.7% and 67.2% increase before and after monsoon, respectively. The innovative trend analysis (ITA) also revealed a statistically increasing trend in observation wells with an increase of about 63.5% and 65.7% pre and post-monsoon season, respectively. The results indicate lowering of GWL in the northern districts of southwestern Punjab, while the southern districts experience rising GWLs. This discrepancy can be attributed to diverse agricultural activities and reduced over-exploitation of groundwater in the southern district due to soil salinity and the presence of brackish groundwater. These findings provide valuable insights into the dynamics of GWL in the studied region, highlighting notable trends associated with seasonal variations.
Journal Article
Statistical analysis of enhanced SDEx encryption method based on BLAKE3 hash function
This paper presents a statistical analysis of the enhanced SDEx (Secure Data Exchange) encryption method, using a version that incorporates two session keys. This method has not previously been combined with the BLAKE3 hash function. The statistical analysis was conducted using the NIST Statistical Test Suite. Several real-world sample files were encrypted using the proposed method and then subjected to statistical analysis through selected tests from the NIST suite. These tests aimed to determine whether the resulting ciphertexts meet the criteria for pseudorandomness. Additionally, compression tests were performed using WinRAR, which confirmed that the ciphertexts are not compressible.
Journal Article
Building Test Batteries Based on Analyzing Random Number Generator Tests within the Framework of Algorithmic Information Theory
The problem of testing random number generators is considered and a new method for comparing the power of different statistical tests is proposed. It is based on the definitions of random sequence developed in the framework of algorithmic information theory and allows comparing the power of different tests in some cases when the available methods of mathematical statistics do not distinguish between tests. In particular, it is shown that tests based on data compression methods using dictionaries should be included in test batteries.
Journal Article
Reckoning wheat yield trends
Wheat yields have increased approximately linearly since the mid-twentieth century across the globe, but stagnation of these trends has now been suggested for several nations. We present a new statistical test for whether a yield time series has leveled off and apply it to wheat yield data from 47 different regions to show that nearly half of the production within our sample has transitioned to level trajectories. With the major exception of India, the majority of leveling in wheat yields occurs within developed nations-including the United Kingdom, France and Germany-whose policies appear to have disincentivized yield increases relative to other objectives. The effects of climate change and of yields nearing their maximum potential may also be important.
Journal Article