arXiv subjects

Warren B. Kindzierski

Publications and source records attributed to Warren B. Kindzierski.

10 recordsLinked to original sources

arXiv2024

The reliability of the gender Implicit Association Test (gIAT) for high-ability careers

Males outnumber females in many high-ability careers in the fields of science, technology, engineering, and mathematics, STEM, and academic medicine, to name a few. These differences are often attributed to subconscious bias as measured by the gender Implicit Association Test, gIAT. We compute p-value plots for results from two meta-analyses, one examines the predictive power of gIAT, and the other examines the predictive power of vocational interests, i.e. personal interests, and behaviors, for explaining gender differences in high-ability careers. The results are clear, the gender Implicit Association Test provides little or no information on male versus female differences, whereas vocational interests are strongly predictive. Researchers of implicit bias should expand their modeling to include additional relevant covariates. In short, these meta-analyses provide no support for the gender Implicit Association Test influencing choice and gender differences of high-ability careers.

stat.AP↗

arXiv2023

Reproducibility of Implicit Association Test (IAT) -- Case study of meta-analysis of racial bias research claims

The Implicit Association Test, IAT, is widely used to measure hidden (subconscious) human biases, implicit bias, of many topics: race, gender, age, ethnicity, religion stereotypes. There is a need to understand the reliability of these measures as they are being used in many decisions in society today. A case study was undertaken to independently test the reliability of (ability to reproduce) racial bias research claims of Black White relations based on IAT (implicit bias) and explicit bias measurements using statistical p value plots. These claims were for IAT, real world behavior correlations and explicit bias, real world behavior correlations of Black White relations. The p value plots were constructed using data sets from published literature and the plots exhibited considerable randomness for all correlations examined. This randomness supports a lack of correlation between IAT, implicit bias, and explicit bias measurements with real world behaviors of Whites towards Blacks. These findings were for microbehaviors (measures of nonverbal and subtle verbal behavior) and person perception judgments (explicit judgments about others). Findings of the p value plots were consistent with the case study research claim that the IAT provides little insight into who will discriminate against whom. It was also observed that the amount of real world variance explained by the IAT and explicit bias measurements was small, less than 5 percent. Others have noted that the poor performance of both the IAT and explicit bias measurements are mostly consistent with a (flawed instruments explanation) problems in theories that motivated development and use of these instruments.

stat.AP↗

arXiv2023

Statistical reliability of meta_analysis research claims for gas stove cooking_childhood respiratory health associations

Odds ratios or p_values from individual observational studies can be combined to examine a common cause_effect research question in meta_analysis. However, reliability of individual studies used in meta_analysis should not be taken for granted as claimed cause_effect associations may not reproduce. An evaluation was undertaken on meta_analysis of base papers examining gas stove cooking, including nitrogen dioxide, NO2, and childhood asthma and wheeze associations. Numbers of hypotheses tested in 14 of 27 base papers, 52 percent, used in meta_analysis of asthma and wheeze were counted. Test statistics used in the meta_analysis, 40 odds ratios with 95 percent confidence limits, were converted to p_values and presented in p_value plots. The median and interquartile range of possible numbers of hypotheses tested in the 14 base papers was 15,360, 6,336_49,152. None of the 14 base papers made mention of correcting for multiple testing, nor was any explanation offered if no multiple testing procedure was used. Given large numbers of hypotheses available, statistics drawn from base papers and used for meta-analysis are likely biased. Even so, p-value plots for gas stove_current asthma and gas stove_current wheeze associations show randomness consistent with unproven gas stove harms. The meta-analysis fails to provide reliable evidence for public health policy making on gas stove harms to children in North America. NO2 is not established as a biologically plausible explanation of a causal link with childhood asthma. Biases_multiple testing and p-hacking_cannot be ruled out as explanations for a gas stove_current asthma association claim. Selective reporting is another bias in published literature of gas stove_childhood respiratory health studies. Keywords gas stove, asthma, meta-analysis, p-value plot, multiple testing, p_hacking

stat.AP↗

arXiv2023

Statistical reproducibility of meta-analysis research claims for medical mask use in community settings to prevent COVID infection

The coronavirus pandemic (COVID) has been an exceptional test of current scientific evidence that inform and shape policy. Many US states, cities, and counties implemented public orders for mask use on the notion that this intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. P-value plotting was used to evaluate statistical reproducibility of meta-analysis research claims of a benefit for medical (surgical) mask use in community settings to prevent COVID infection. Eight studies (seven meta-analyses, one systematic review) published between 1 January 2020 and 7 December 2022 were evaluated. Base studies were randomized control trials with outcomes of medical diagnosis or laboratory-confirmed diagnosis of viral (Influenza or COVID) illness. Self-reported viral illness outcomes were excluded because of awareness bias. No evidence was observed for a medical mask use benefit to prevent viral infections in six p-value plots (five meta-analyses and one systematic review). Research claims of no benefit in three meta-analyses and the systematic review were reproduced in p-value plots. Research claims of a benefit in two meta-analyses were not reproduced in p-value plots. Insufficient data were available to construct p-value plots for two meta-analyses because of overreliance on self-reported outcomes. These findings suggest a benefit for medical mask use in community settings to prevent viral, including COVID infection, is unproven.

q-bio.QM↗

arXiv2022

Reproducibility of health claims in meta-analysis studies of COVID quarantine (stay-at-home) orders

The coronavirus pandemic (COVID) has been an extraordinary test of modern government scientific procedures that inform and shape policy. Many governments implemented COVID quarantine (stay-at-home) orders on the notion that this nonpharmaceutical intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. The overall research capacity response to COVID since late 2019 has been massive. Given lack of research transparency, only a small fraction of published research has been judged by others to be reproducible before COVID. Independent evaluation of published meta-analysis on a common research question can be used to assess the reproducibility of a claim coming from that field of research. We used a p-value plotting statistical method to independently evaluate reproducibility of specific research claims made in four meta-analysis studies related to benefits/risks of COVID quarantine orders. Outcomes we investigated included: mortality, mental health symptoms, incidence of domestic violence, and suicidal ideation (thoughts of killing yourself). Three of the four meta-analyses that we evaluated (mortality, mental health symptoms, incidence of domestic violence) raise further questions about benefits/risks of this form of intervention. The fourth meta-analysis study (suicidal ideation) is unreliable. Given lack of research transparency and irreproducibility of published research, independent evaluation of meta-analysis studies using p-value plotting is offered as a way to strengthen or refute (falsify) claims made in COVID research.

q-bio.OT↗

arXiv2021

Case Study: Evaluation of a meta-analysis of the association between soy protein and cardiovascular disease

It is well-known that claims coming from observational studies most often fail to replicate. Experimental (randomized) trials, where conditions are under researcher control, have a high reputation and meta-analysis of experimental trials are considered the best possible evidence. Given the irreproducibility crisis, experiments lately are starting to be questioned. There is a need to know the reliability of claims coming from randomized trials. A case study is presented here independently examining a published meta-analysis of randomized trials claiming that soy protein intake improves cardiovascular health. Counting and p-value plotting techniques (standard p-value plot, p-value expectation plot, and volcano plot) are used. Counting (search space) analysis indicates that reported p-values from the meta-analysis could be biased low due to multiple testing and multiple modeling. Plotting techniques used to visualize the behavior of the data set used for meta-analysis suggest that statistics drawn from the base papers do not satisfy key assumptions of a random-effects meta-analysis. These assumptions include using unbiased statistics all drawn from the same population. Also, publication bias is unaddressed in the meta-analysis. The claim that soy protein intake should improve cardiovascular health is not supported by our analysis.

stat.AP↗

arXiv2021

Standard meta-analysis methods are not robust

P values or risk ratios from multiple, independent studies, observational or randomized, can be computationally combined to provide an overall assessment of a research question in meta-analysis. There is a need to examine the reliability of these methods of combination. It is typical in observational studies to statistically test many questions and not correct the analysis results for multiple testing or multiple modeling, MTMM. The same problem can happen for randomized, experimental trials. There is the additional problem that some of the base studies may be using fabricated or fraudulent data. If there is no attention to MTMM or fraud in the base studies, there is no guarantee that the results to be combined are unbiased, the key requirement for the valid combining of results. We note that methods of combination are not robust; even one extreme base study value can overwhelm standard methods of combination. It is possible that multiple, extreme (MTMM or fraudulent) results can feed from the base studies to bias the combined result. A meta-analysis of observational (or even randomized studies) may not be reliable. Examples are given along with some methods to evaluate existing base studies and meta-analysis studies.

stat.ME↗

arXiv2020

Reliability of meta-analysis of an association between ambient air quality and development of asthma later in life

Claims from observational studies often fail to replicate. A study was undertaken to assess the reliability of cohort studies used in a highly cited meta-analysis of the association between ambient nitrogen dioxide, NO2, and fine particulate matter, PM2.5, concentrations early in life and development of asthma later in life. The numbers of statistical tests possible were estimated for 19 base papers considered for the meta-analysis. A p-value plot for NO2 and PM2.5 was constructed to evaluate effect heterogeneity of p-values used from the base papers. The numbers of statistical tests possible in the base papers were large - median 13,824, interquartile range 1,536-221,184; range 96-42M, in comparison to statistical test results presented. Statistical test results drawn from the base papers are unlikely to provide unbiased measures for meta-analysis. The p-value plot indicated that heterogeneity of the NO2 results across the base papers is consistent with a two-component mixture. First, it makes no sense to average across a mixture in meta-analysis. Second, the shape of the p-value plot for NO2 appears consistent with the possibility of analysis manipulation to obtain small p-values in several of the cohort studies. As for PM2.5, all corresponding p-values fall on a 45-degree line indicating complete randomness rather than a true association. Our interpretation of the meta-analysis is that the random p-values indicating no cause-effect associations are more plausible and that their meta-analysis will not likely replicate in the absence of bias. We conclude that claims made in the base papers used for meta-analysis are unreliable due to bias induced by multiple testing and multiple modelling, MTMM. We also show there is evidence that the heterogeneity across the base papers used for meta-analysis is more complex than simple sampling from a normal process.

stat.AP↗

arXiv2020

Evaluation of a meta-analysis of ambient air quality as a risk factor for asthma exacerbation

False-positive results and bias may be common features of the biomedical literature today, including risk factor-chronic disease research. A study was undertaken to assess the reliability of base studies used in a meta-analysis examining whether carbon monoxide, particulate matter 10 and 2.5 micro molar, sulfur dioxide, nitrogen dioxide and ozone are risk factors for asthma exacerbation (hospital admission and emergency room visits for asthma attack). The number of statistical tests and models were counted in 17 randomly selected base papers from 87 used in the meta-analysis. P-value plots for each air component were constructed to evaluate the effect heterogeneity of p-values used from all 87 base papers The number of statistical tests possible in the 17 selected base papers was large, median=15,360 (interquartile range=1,536 to 40,960), in comparison to results presented. Each p-value plot showed a two-component mixture with small p-values less than .001 while other p-values appeared random (p-values greater than .05). Given potentially large numbers of statistical tests conducted in the 17 selected base papers, p-hacking cannot be ruled out as explanations for small p-values. Our interpretation of the meta-analysis is that the random p-values indicating null associations are more plausible and that the meta-analysis will not likely replicate in the absence of bias. We conclude the meta-analysis and base papers used are unreliable and do not offer evidence of value to inform public health practitioners about air quality as a risk factor for asthma exacerbation. The following areas are crucial for enabling improvements in risk factor chronic disease observational studies at the funding agency and journal level: preregistration, changes in funding agency and journal editor (and reviewer) practices, open sharing of data and facilitation of reproducibility research.

stat.AP↗

arXiv2019

Evaluation of a meta-analysis of air quality and heart attacks, a case study

It is generally acknowledged that claims from observational studies often fail to replicate. An exploratory study was undertaken to assess the reliability of base studies used in meta-analysis of short-term air quality-myocardial infarction risk and to judge the reliability of statistical evidence from meta-analysis that uses data from observational studies. A highly cited meta-analysis paper examining whether short-term air quality exposure triggers myocardial infarction was evaluated as a case study. The paper considered six air quality components - carbon monoxide, nitrogen dioxide, sulfur dioxide, particulate matter 10 and 2.5 micrometers in diameter (PM10 and PM2.5), and ozone. The number of possible questions and statistical models at issue in each of 34 base papers used were estimated and p-value plots for each of the air components were constructed to evaluate the effect heterogeneity of p-values used from the base papers. Analysis search spaces (number of statistical tests possible) in the base papers were large, median of 12,288, interquartile range: 2,496 to 58,368, in comparison to actual statistical test results presented. Statistical test results taken from the base papers may not provide unbiased measures of effect for meta-analysis. Shapes of p-value plots for the six air components were consistent with the possibility of analysis manipulation to obtain small p-values in several base papers. Results suggest the appearance of heterogeneous, researcher-generated p-values used in the meta-analysis rather than unbiased evidence of real effects for air quality. We conclude that this meta-analysis does not provide reliable evidence for an association of air quality components with myocardial risk.

stat.AP↗