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A teaching tool about the fickle p value and other statistical principles based on real-life data.

ABSTRACT: A poor understanding of statistical analysis has been proposed as a key reason for lack of replicability of many studies in experimental biomedicine. While several authors have demonstrated the fickleness of calculated p values based on simulations, we have experienced that such simulations are difficult to understand for many biomedical scientists and often do not lead to a sound understanding of the role of variability between random samples in statistical analysis. Therefore, we as trainees and trainers in a course of statistics for biomedical scientists have used real data from a large published study to develop a tool that allows scientists to directly experience the fickleness of p values. A tool based on a commonly used software package was developed that allows using random samples from real data. The tool is described and together with the underlying database is made available. The tool has been tested successfully in multiple other groups of biomedical scientists. It can also let trainees experience the impact of randomness, sample sizes and choice of specific statistical test on measured p values. We propose that live exercises based on real data will be more impactful in the training of biomedical scientists on statistical concepts.

SUBMITTER: Alawbathani S

PROVIDER: S-EPMC8208928 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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A teaching tool about the fickle p value and other statistical principles based on real-life data.

Alawbathani Salem S Batool Mehreen M Fleckhaus Jan J Hamad Sarkawt S Hassenrück Floyd F Hou Yanhong Y Li Xia X Salmanton-García Jon J Ullah Sami S Wieters Frederique F Michel Martin C MC

Naunyn-Schmiedeberg's archives of pharmacology 20210114 6

A poor understanding of statistical analysis has been proposed as a key reason for lack of replicability of many studies in experimental biomedicine. While several authors have demonstrated the fickleness of calculated p values based on simulations, we have experienced that such simulations are difficult to understand for many biomedical scientists and often do not lead to a sound understanding of the role of variability between random samples in statistical analysis. Therefore, we as trainees a ...[more]

PMID: 33443615

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Project description:Randomized clinical trials constitute the gold-standard for evaluating new anti-cancer therapies; however, real-life data are key in complementing clinically useful information. We developed a computational tool for real-life data analysis and applied it to the metastatic colorectal cancer (mCRC) setting. This tool addressed the impact of oncology/non-oncology parameters on treatment patterns and clinical outcomes.The developed tool enables extraction of any computerized information including comorbidities and use of drugs (oncological/non-oncological) per individual HMO member. The study in which we evaluated this tool was a retrospective cohort study that included Maccabi Healthcare Services members with mCRC receiving bevacizumab with fluoropyrimidines (FP), FP plus oxaliplatin (FP-O), or FP plus irinotecan (FP-I) in the first-line between 9/2006 and 12/2013.The analysis included 753 patients of whom 15.4% underwent subsequent metastasectomy (the Surgery group). For the entire cohort, median overall survival (OS) was 20.5 months; in the Surgery group, median duration of bevacizumab-containing therapy (DOT) pre-surgery was 6.1 months; median OS was not reached. In the Non-surgery group, median OS and DOT were 18.7 and 11.4 months, respectively; no significant OS differences were noted between FP-O and FP-I, whereas FP use was associated with shorter OS (12.3 month; p <0.002; notably, these patients were older). Patients who received both FP-O- and FP-I-based regimens achieved numerically longer OS vs. those who received only one of these regimens (22.1 [19.9-24.0] vs. 18.9 [15.5-21.9] months). Among patients assessed for wild-type KRAS and treated with subsequent anti-EGFR agent, OS was 25.4 months and 18.7 months for 124 treated vs. 37 non-treated patients (non-significant). Cox analysis (controlling for age and gender) identified several non-oncology parameters associated with poorer clinical outcomes including concurrent use of diuretics and proton-pump inhibitors.Our tool provided insights that confirmed/complemented information gained from randomized-clinical trials. Prospective tool implementation is warranted.

Project description:ObjectivesThe COVID-19 pandemic has posed unprecedented challenges to health systems and populations, particularly in India. Comprehensive, population-level studies of the burden of disease could inform planning, preparedness and policy, but are lacking in India. In West Bengal, India, we conducted a detailed analysis of the burden caused by COVID-19 from its onset to 7 January 2022.SettingOpen-access, population-level and administrative data sets for West Bengal were used.Primary and secondary outcome measuresDisability-adjusted life years (DALYs), years of potential productive life lost (YPPLL), cost of productivity lost (CPL: premature mortality and absenteeism), years of potential life lost (YPLL), premature years of potential life lost, working years of potential life lost (WYPLL) and value of statistical life (VSL) were estimated across scenarios (21 for DALY and 3 each for YPLL and VSL) to evaluate the effects of different factors.ResultsCOVID-19 had a higher impact on the elderly population with 90.2% of deaths arising from people aged above 45. In males and females, respectively, DALYs were 190 568.1 and 117 310.0 years, YPPLL of the productive population was 28 714.7 and 16 355.4 years, CPL due to premature mortality was INR3 198 259 615.6 and INR583 397 335.1 and CPL due to morbidity was INR2 505 568 048.4 and INR763 720 886.1. For males and females, YPLL ranged from 189 103.2 to 272 787.5 years and 117 925.5 to 169 712.0 years for lower to higher age limits, and WYPLL was 54 333.9 and 30 942.2 years. VSL (INR million) for the lower, midpoint and upper life expectancies was 883 330.8; 882 936.4; and 880 631.3, respectively. Vaccination was associated with reduced mortality.ConclusionsThe losses incurred due to COVID-19 in terms of the computed estimates in West Bengal revealed a disproportionately higher impact on the elderly and males. Analysis of various age-gender subgroups enhances localised and targeted policymaking to minimise the losses for future pandemics.

Dataset Information

A teaching tool about the fickle p value and other statistical principles based on real-life data.

Publications

A teaching tool about the fickle p value and other statistical principles based on real-life data.

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