Statistical Evaluation of Medical Journal Manuscripts

Schor, Stanley S.

doi:10.1001/jama.1966.03100130097026

Cited by 158 publications

(42 citation statements)

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“…Andersen goes on to enumerate many examples of errors since the 1950s. Systematic sampling of 149 studies from popular medical journals concluded that only 28% of the sample was considered "acceptable" (62). More than 20 y later, an analysis of 196 drug trials to treat rheumatoid arthritis concluded that 76% of the conclusions or abstracts contained "doubtful or invalid statements" (63).…”

Section: The Prevalence and Consequences Of Errorsmentioning

confidence: 99%

Issues with data and analyses: Errors, underlying themes, and potential solutions

Brown

Kaiser

Allison

2018

Proc. Natl. Acad. Sci. U.S.A.

131

102

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Some aspects of science, taken at the broadest level, are universal in empirical research. These include collecting, analyzing, and reporting data. In each of these aspects, errors can and do occur. In this work, we first discuss the importance of focusing on statistical and data errors to continually improve the practice of science. We then describe underlying themes of the types of errors and postulate contributing factors. To do so, we describe a case series of relatively severe data and statistical errors coupled with surveys of some types of errors to better characterize the magnitude, frequency, and trends. Having examined these errors, we then discuss the consequences of specific errors or classes of errors. Finally, given the extracted themes, we discuss methodological, cultural, and systemlevel approaches to reducing the frequency of commonly observed errors. These approaches will plausibly contribute to the self-critical, self-correcting, ever-evolving practice of science, and ultimately to furthering knowledge.rigor | statistical errors | quality control | reproducibility | data analysis

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Section: The Prevalence and Consequences Of Errorsmentioning

confidence: 99%

Issues with data and analyses: Errors, underlying themes, and potential solutions

Brown

Kaiser

Allison

2018

Proc. Natl. Acad. Sci. U.S.A.

131

102

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show abstract

“…In 1966, Schor and Karten [16] addressed the issue of what constitutes statistical significance. As part of their statistical evaluation of published clinical trials, then in a generally unsatisfactory state, they proposed a standard of p !…”

Section: Issues and Solutions In Statistical Evaluation Of Pharmacolomentioning

confidence: 99%

“…Nonrandomized studies claiming or implying causal associations continue to be performed and published [8,10,11] , even when they do not satisfy standards for credible science described by Hill [17] , Schor and Karten [16] , Sackett [18] , and Feinstein [6] . Such studies suffer from the inescapable fundamental flaw of nonrandomization.…”

Section: Current Tribulations In Pharmacology and Statisticsmentioning

confidence: 99%

Pharmacology and Statistics: Recommendations to Strengthen a Productive Partnership

Spector

Vesell

2006

Pharmacology

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Critical to the discovery, development and rational use of drugs and vaccines are the foundational principles and proper application of statistics. However, in too many cases, there has been misuse of statistics and/or overemphasis on statistical significance (p < 0.05), as though this criterion possessed truth-guaranteeing properties. To clarify confusion about the proper use of statistics in pharmacology, we summarize briefly the foundational principles of probability; the role of statistics in assessment of causality; the three basic uses of statistical methods, especially those employed in hypothesis testing; and current statistical issues in pharmacological research. We then review and provide examples of the meaning of statistical significance, the consequences of lack of randomization in epidemiology/observation studies, the criteria for measurement instrument validation, the problems with subgroup analyses, the need for multiple comparison statistical methods, and how to handle dropouts and missing data. Finally, based on sound experimental and statistical principles, we make a series of recommendations to both experimentalists and journal editors to improve published pharmacological experiments. These include widespread use of blinding and randomization and/or random selection of subjects in both basic and clinical pharmacology, mandatory use of rigorous evidentiary criteria in epidemiology/observation studies claiming causal associations, proper interpretation of statistical versus clinical/ pharmacological significance, appropriate interpretation of meta-analyses, meaningful validation of methods, and a more rational statistical approach to subgroup analyses and genetic association studies.

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“…Schor and Karten (1966) studied the statistical methods used in a large series of medical studies reported in several journals, and found that only 28 percent of them were statistically acceptable. This finding led the American Statistical Association to raise the question of whether a code of principles can be maintained to assure basic levels of statistical competence, or whether formal certification is necessary to assure credibility of an author.…”

Section: Introductionmentioning

confidence: 99%

“…In fields such as visual quality where the concepts in question are sometimes harder to quantify than those in medical research, and where less professional statistical input is employed, the problem is likely to be worse than that reported by Schor and Karten (1966). The fact is, despite its numerical base, statistics is art as well as science.…”

Section: Introductionmentioning

confidence: 99%

Evaluating statistical validity of research reports: a guide for managers, planners, and researchers

Golbeck¹

1986

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Statistical Evaluation of Medical Journal Manuscripts

Cited by 158 publications

References 0 publications

Issues with data and analyses: Errors, underlying themes, and potential solutions

Issues with data and analyses: Errors, underlying themes, and potential solutions

Pharmacology and Statistics: Recommendations to Strengthen a Productive Partnership

Evaluating statistical validity of research reports: a guide for managers, planners, and researchers

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