El valor de p del test no es un ‘índice matemático’, es simplemente una frecuencia relativa / The p-value of the test is not a ‘mathematical index’, it is simply a relative frequency

Las revistas científicas más importantes en campos como medicina, biología y sociología publican reiteradamente artículos y editoriales denunciando que un gran porcentaje de médicos no entiende los conceptos básicos del análisis estadístico, lo que favorece el riesgo de cometer errores al interpretar los datos, los hace más vulnerables frente a informaciones falsas y reduce la eficacia de la investigación. Este problema se extiende a lo largo de toda su carrera profesional y se debe, en gran parte, a una enseñanza deficiente en estadística que es común en países desarrollados. En palabras de H. Halle y S. Krauss, ‘el 90% de los profesores universitarios alemanes que usan con asiduidad el valor de p de los test no entiende lo que mide ese valor’. Es importante destacar que los razonamientos básicos del análisis estadístico son similares a los que realizamos en nuestra vida cotidiana y que comprender los conceptos básicos del análisis estadístico no requiere conocimiento matemático alguno. En contra de lo que muchos investigadores creen, el valor de p del test no es un ‘índice matemático’ que nos permita concluir claramente si, por ejemplo, un fármaco es más efectivo que el placebo. El valor de p del test es simplemente un porcentaje.(AU)

Abstract. Leading scientific journals in fields such as medicine, biology and sociology repeatedly publish articles and editorials claiming that a large percentage of doctors do not understand the basics of statistical analysis, which increases the risk of errors in interpreting data, makes them more vulnerable to misinformation and reduces the effectiveness of research. This problem extends throughout their careers and is largely due to the poor training they receive in statistics – a problem that is common in developed countries. As stated by H. Halle and S. Krauss, ‘90% of German university lecturers who regularly use the p-value in tests do not understand what that value actually measures’. It is important to note that the basic reasoning of statistical analysis is similar to what we do in our daily lives and that understanding the basic concepts of statistical analysis does not require any knowledge of mathematics. Contrary to what many researchers believe, the p-value of the test is not a ‘mathematical index’ that allows us to clearly conclude whether, for example, a drug is more effective than a placebo. The p-value of the test is simply a percentage.(AU)

OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery.

Contingency tables, data represented as counts matrices, are ubiquitous across quantitative research and data-science applications. Existing statistical tests are insufficient however, as none are simultaneously computationally efficient and statistically valid for a finite number of observations. In this work, motivated by a recent application in reference-free genomic inference [K. Chaung et al., Cell 186, 5440-5456 (2023)], we develop Optimized Adaptive Statistic for Inferring Structure (OASIS), a family of statistical tests for contingency tables. OASIS constructs a test statistic which is linear in the normalized data matrix, providing closed-form P-value bounds through classical concentration inequalities. In the process, OASIS provides a decomposition of the table, lending interpretability to its rejection of the null. We derive the asymptotic distribution of the OASIS test statistic, showing that these finite-sample bounds correctly characterize the test statistic's P-value up to a variance term. Experiments on genomic sequencing data highlight the power and interpretability of OASIS. Using OASIS, we develop a method that can detect SARS-CoV-2 and Mycobacterium tuberculosis strains de novo, which existing approaches cannot achieve. We demonstrate in simulations that OASIS is robust to overdispersion, a common feature in genomic data like single-cell RNA sequencing, where under accepted noise models OASIS provides good control of the false discovery rate, while Pearson's [Formula: see text] consistently rejects the null. Additionally, we show in simulations that OASIS is more powerful than Pearson's [Formula: see text] in certain regimes, including for some important two group alternatives, which we corroborate with approximate power calculations.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Analysis of Adverse Events Following Phenobarbital Administration for Pediatric Patients Categorized by One-Year Age Increments Using the U.S. Food and Drug Administration Adverse Event Reporting System.

Background Organ and body development greatly varies in pediatric patients from year to year. Therefore, the incidence of each adverse event following phenobarbital (PB) administration would vary with age. However, in clinical trials, increasing the sample size of pediatric patients in each age group has been challenging. Therefore, previous studies were conducted by dividing pediatric patients into three or four age groups based on the development stage. Although these results were useful in clinical settings, information on adverse events that occurred at one-year age increments in pediatric patients could further enhance treatment and care. Objectives This study investigated in one-year age increments the occurrence tendency of each adverse event following PB administration in pediatric patients. Methods This study used data obtained from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS). Two inclusion criteria were set: (1) treatment with PB between January 2004 and June 2023 and (2) age 0-15 years. Using the cutoff value obtained using the Wilcoxon-Mann-Whitney test by the minimum p-value approach, this study explored changes in the occurrence tendency of each adverse event in one-year age increments. At the minimum p-value of <0.05, the age corresponding to this p-value was determined as the cutoff value. Conversely, at the minimum p-value of ≥0.05, the cutoff value was considered nonexistent. Results This study investigated all types of adverse events and explored the cutoff value for each adverse event. We identified 34, 16, 15, nine, five, five, eight, three, and eight types of adverse events for the cutoff values of ≤3/>3, ≤4/>4, ≤5/>5, ≤6/>6, ≤7/>7, ≤8/>8, ≤9/>9, ≤10/>10, and ≤11/>11 years, respectively. Conclusions This study demonstrated that adverse events requiring attention in pediatric patients varied with age. The findings help in the improvement of treatment and care in the pediatric clinical settings.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.

BACKGROUND: The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS: We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS: We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION: Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Case weighted power priors for hybrid control analyses with time-to-event data.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

Evidence-based medicine or statistically manipulated medicine? Are we slaves to the P-value?

First popularized almost a century ago in epidemiologic research by Ronald Fisher and Jerzy Neyman, the P-value has become perhaps the most misunderstood and even misused statistical value or descriptor. Indeed, modern clinical research has now come to be centered around and guided by an arbitrary P-value of <0.05 as a magical threshold for significance, so much so that experimental design, reporting of experimental findings, and interpretation and adoption of such findings have become largely dependent on this "significant" P-value. This has given rise to multiple biases in the overall body of biomedical literature that threatens the very validity of clinical research. Ultimately, a drive toward reporting a "significant" P-value (by various statistical manipulations) risks creating a falsely positive body of science, leading to (i) wasted resources in pursuing fruitless research and (ii) futile or even harmful policies/therapeutic recommendations. This article reviews the history of the P-value, the conceptual basis of P-value in the context of hypothesis testing and challenges in critically appraising clinical evidence vis-à-vis the P-value. This review is aimed at raising awareness of the pitfalls of this rigid observation of the threshold of statistical significance when evaluating clinical trials and to generate discussion regarding whether the scientific body needs a rethink about how we decide clinical significance.

Estimating the proportion of true null hypotheses and adaptive false discovery rate control in discrete paradigm.

Storey's estimator for the proportion of true null hypotheses, originally proposed under the continuous framework, has been modified in this work under the discrete framework. The modification results in improved estimation of the parameter of interest. The proposed estimator is used to formulate an adaptive version of the Benjamini-Hochberg procedure. Control over the false discovery rate by the proposed adaptive procedure has been proved analytically. The proposed estimate is also used to formulate an adaptive version of the Benjamini-Hochberg-Heyse procedure. Simulation experiments establish the conservative nature of this new adaptive procedure. Substantial amount of gain in power is observed for the new adaptive procedures over the standard procedures. For demonstration of the proposed method, two important real life gene expression data sets, one related to the study of HIV and the other related to methylation study, are used.

Frequentist, Bayesian Analysis and Complementary Statistical Tools for Geriatric and Rehabilitation Fields: Are Traditional Null-Hypothesis Significance Testing Methods Sufficient?

Null hypothesis significant testing (NHST) is the dominant statistical approach in the geriatric and rehabilitation fields. However, NHST is routinely misunderstood or misused. In this case, the findings from clinical trials would be taken as evidence of no effect, when in fact, a clinically relevant question may have a "non-significant" p-value. Conversely, findings are considered clinically relevant when significant differences are observed between groups. To assume that p-value is not an exclusive indicator of an association or the existence of an effect, researchers should be encouraged to report other statistical analysis approaches as Bayesian analysis and complementary statistical tools alongside the p-value (eg, effect size, confidence intervals, minimal clinically important difference, and magnitude-based inference) to improve interpretation of the findings of clinical trials by presenting a more efficient and comprehensive analysis. However, the focus on Bayesian analysis and secondary statistical analyses does not mean that NHST is less important. Only that, to observe a real intervention effect, researchers should use a combination of secondary statistical analyses in conjunction with NHST or Bayesian statistical analysis to reveal what p-values cannot show in the geriatric and rehabilitation studies (eg, the clinical importance of 1kg increase in handgrip strength in the intervention group of long-lived older adults compared to a control group). This paper provides potential insights for improving the interpretation of scientific data in rehabilitation and geriatric fields by utilizing Bayesian and secondary statistical analyses to better scrutinize the results of clinical trials where a p-value alone may not be appropriate to determine the efficacy of an intervention.

The impact of lowering the study design significance threshold to 0.005 on sample size in randomized cancer clinical trials.

The proposal of improving reproducibility by lowering the significance threshold to 0.005 has been discussed, but the impact on conducting clinical trials has yet to be examined from a study design perspective. The impact on sample size and study duration was investigated using design setups from 125 phase II studies published between 2015 and 2022. The impact was assessed using percent increase in sample size and additional years of accrual with the medians being 110.97% higher and 2.65 years longer respectively. The results indicated that this proposal causes additional financial burdens that reduce the efficiency of conducting clinical trials.

La dramática pérdida de potencia estadística al dicotomizar variables continuas / The dramatic loss of statistical power when dichotomising continuous variables

Una práctica muy habitual en la investigación médica, durante el proceso de análisis de los datos, es dicotomizar variables numéricas en dos grupos. Dicha práctica conlleva la pérdida de información muy útil que puede restar eficacia a la investigación. A través de varios ejemplos, se muestra cómo con la dicotomización de variables numéricas los estudios pierden potencia estadística. Esto puede ser un aspecto crítico que impida valorar, por ejemplo, si un procedimiento terapéutico es más efectivo o si un determinado factor es de riesgo. Por tanto, se recomienda no dicotomizar las variables continuas si no existe un motivo muy concreto para ello. (AU)

Abstract. A very common practice in medical research, during the process of data analysis, is to dichotomise numerical variables in two groups. This leads to the loss of very useful information that can undermine the effectiveness of the research. Several examples are used to show how the dichotomisation of numerical variables can lead to a loss of statistical power in studies. This can be a critical aspect in assessing, for example, whether a therapeutic procedure is more effective or whether a certain factor is a risk factor. Dichotomising continuous variables is therefore not recommended unless there is a very specific reason to do so. (AU)

Assessing the robustness of positive vascular surgery randomized controlled trials using their fragility index.

OBJECTIVE: The fragility index (FI) measures the robustness of statistically significant findings in randomized controlled trials (RCTs) by quantifying the minimum number of event conversions required to reverse a dichotomous outcome's statistical significance. In vascular surgery, many clinical guidelines and critical decision-making points are informed by a handful of key RCTs, especially regarding open surgical versus endovascular treatment. The objective of this study is to evaluate the FI of RCTs with statistically significant primary outcomes that compared open vs endovascular surgery in vascular surgery. METHODS: In this meta-epidemiological study and systematic review, MEDLINE, Embase, and CENTRAL were searched for RCTs comparing open versus endovascular treatments for abdominal aortic aneurysms, carotid artery stenosis, and peripheral arterial disease to December 2022. RCTs with statistically significant primary outcomes were included. Data screening and extraction were conducted in duplicate. The FI was calculated by adding an event to the group with the smaller number of events while subtracting a nonevent to the same group until Fisher's exact test produced a nonstatistically significant result. The primary outcome was the FI and proportion of outcomes where the loss to follow-up was greater than the FI. The secondary outcomes assessed the relationship of the FI to disease state, presence of commercial funding, and study design. RESULTS: Overall, 5133 articles were captured in the initial search with 21 RCTs reporting 23 different primary outcomes being included in the final analysis. The median FI (first quartile, third quartile) was 3 (3, 20) with 16 outcomes (70%) reporting a loss to follow-up greater than its FI. Mann-Whitney U test revealed that commercially funded RCTs and composite outcomes had greater FIs (median, 20.0 [5.5, 24.5] vs median, 3.0 [2.0, 5.5], P = .035; median, 21 [8, 38] vs median, 3.0 [2.0, 8.5], P = .01, respectively). The FI did not vary between disease states (P = .285) or between index and follow-up trials (P = .147). There were significant correlations between the FI and P values (Pearson r = 0.90; 95% confidence interval, 0.77-0.96), and the number of events (r = 0.82; 95% confidence interval, 0.48-0.97). CONCLUSIONS: A small number of event conversions (median, 3) are needed to alter the statistical significance of primary outcomes in vascular surgery RCTs evaluating open surgical and endovascular treatments. Most studies had loss to follow-up greater than its FI, which can call into question trial results, and commercially funded studies had a greater FI. The FI and these findings should be considered in future trial design in vascular surgery.

Defining high confidence targets of differential CpG methylation in response to in utero arsenic exposure and implications for cancer risk.

Arsenic is a relatively abundant metalloid that impacts DNA methylation and has been implicated in various adverse health outcomes including several cancers and diabetes. However, uncertainty remains about the identity of genomic CpGs that are sensitive to arsenic exposure, in utero or otherwise. Here we identified a high confidence set of CpG sites whose methylation is sensitive to in utero arsenic exposure. To do so, we analyzed methylation of infant CpGs as a function of maternal urinary arsenic in cord blood and placenta from geographically and ancestrally distinct human populations. Independent analyses of these distinct populations were followed by combination of results across sexes and populations/tissue types. Following these analyses, we concluded that both sex and tissue type are important drivers of heterogeneity in methylation response at several CpGs. We also identified 17 high confidence CpGs that were hypermethylated across sex, tissue type and population; 11 of these were located within protein coding genes. This pattern is consistent with hypotheses that arsenic increases cancer risk by inducing the hypermethylation of genic regions. This study represents an opportunity to understand consistent, reproducible patterns of epigenomic responses after in utero arsenic exposure and may aid towards novel biomarkers or signatures of arsenic exposure. Identifying arsenic-responsive sites can also contribute to our understanding of the biological mechanisms by which arsenic exposure can affect biological function and increase risk of cancer and other age-related diseases.

Virtual plates: Getting the best out of high content screens.

High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another factor is the recognition that diverse cell phenotypes can be associated with changes in biological pathways relevant to disease processes. There are numerous challenges in HCS campaigns. These include limited ability to support replicates, low availability of precious and unique cells or reagents, high number of experimental batches, lengthy preparation of cells for imaging, image acquisition time (45-60 min per plate) and image processing time, deterioration of image quality with time post cell fixation and variability within wells and batches. To take advantage of the data in HCS, cell population based rather than well-based analyses are required. Historically, statistical analysis and hypothesis testing played only a limited role in non-high content high throughput campaigns. Thus, only a limited number of standard statistical criteria for hit selection in HCS have been developed so far. In addition to complex biological content in HCS campaigns, additional variability is impacted by cell and reagent handling and by instruments which may malfunction or perform unevenly. Together these can cause a significant number of wells or plates to fail. Here we describe an automated approach for hit analysis and detection in HCS. Our approach automates HCS hit detection using a methodology that is based on a documented statistical framework. We introduce the Virtual Plate concept in which selected wells from different plates are collated into a new, virtual plate. This allows the rescue and analysis of compound wells which have failed due to technical issues as well as to collect hit wells into one plate, allowing the user easier access to the hit data.

P-values and confidence intervals of linear rank tests for left-truncated data under truncated binomial design.

The paper provides computations comparing the accuracy of the saddlepoint approximation approach and the normal approximation method in approximating the mid-p-value of Wilcoxon and log-rank tests for the left-truncated data using a truncated binomial design. The paper uses real data examples to apply the comparison, along with some simulated studies. Confidence intervals are provided by the inversion of the tests under consideration.

Exact p-value calculation for XCorr scoring of high-resolution MS/MS data.

Exact p-value (XPV)-based methods for dot product-like score functions-such as the XCorr score implemented in Tide, SEQUEST, Comet or shared peak count-based scoring in MSGF+ and ASPV-provide a fairly good calibration for peptide-spectrum-match (PSM) scoring in database searching-based MS/MS spectrum data identification. Unfortunately, standard XPV methods, in practice, cannot handle high-resolution fragmentation data produced by state-of-the-art mass spectrometers because having smaller bins increases the number of fragment matches that are assigned to incorrect bins and scored improperly. In this article, we present an extension of the XPV method, called the high-resolution exact p-value (HR-XPV) method, which can be used to calibrate PSM scores of high-resolution MS/MS spectra obtained with dot product-like scoring such as the XCorr. The HR-XPV carries remainder masses throughout the fragmentation, allowing them to greatly increase the number of fragments that are properly assigned to the correct bin and, thus, taking advantage of high-resolution data. Using four mass spectrometry data sets, our experimental results demonstrate that HR-XPV produces well-calibrated scores, which in turn results in more trusted spectrum annotations at any false discovery rate level.

Mitos y realidad en el cálculo del tamaño muestral / Myths and reality about calculating sample size

Cuando decidimos hacer un estudio, una de las primeras cuestiones que se plantea es ¿qué número de individuos debo incluir en la muestra para que sea ‘representativa’ y el estudio sea ‘válido’? Como en otros ámbitos de la vida, hay muchas cuestiones para las que no hay una cantidad ‘adecuada’ y son válidas diferentes cantidades. Aquí ocurre lo mismo. La pregunta ‘¿cuántos euros costó esta bicicleta?’ tiene como respuesta un número concreto. Pero la pregunta ‘¿cuántos euros necesito para comprar una bicicleta?’ admite muchas cifras distintas como respuesta, dependiendo del tamaño y otras características de la bicicleta. Los libros de estadística contienen fórmulas que relacionan el tamaño de la muestra con ciertos parámetros y la mayoría de los médicos cree que una de ellas les dará el tamaño ‘adecuado’ para su investigación, y que usándolas queda ‘justificado el tamaño de la muestra’ ante posibles revisores. En este documento se hace una reflexión sobre el verdadero peso que tienen dichas fórmulas y cuál debe ser el uso adecuado que el investigador haga de ellas. Es necesario mostrar errores y simulaciones que no benefician a nadie y perjudican a muchos restando tiempo y energía.(AU)

When we decide to conduct a study, one of the first questions that arises is what number of individuals should be included in the sample for it to be ‘representative’ and for the study to be ‘valid’? As in other areas of life, there are many matters for which there is no ‘right’ amount and different quantities are valid. The same applies here. When asked the question ‘How many euros did this bicycle cost?’, the answer is a definite number. But the question ‘How many euros do I need to buy a bicycle?’ can be answered in many different ways, depending on the size and other characteristics of the bicycle. Statistics textbooks contain formulas relating sample size to certain parameters and most doctors believe that one of these will give them the ‘right’ size for their research, and that by using them their choice of sample size will be justified in the eyes of potential reviewers. This document reflects on the true value of these formulas and how researchers should make proper use of them. It is necessary to show errors and simulations that benefit no one and hinder many by taking up large amounts of time and energy.(AU)

OASIS: An interpretable, finite-sample valid alternative to Pearson's X2 for scientific discovery.

Contingency tables, data represented as counts matrices, are ubiquitous across quantitative research and data-science applications. Existing statistical tests are insufficient however, as none are simultaneously computationally efficient and statistically valid for a finite number of observations. In this work, motivated by a recent application in reference-free genomic inference (1), we develop OASIS (Optimized Adaptive Statistic for Inferring Structure), a family of statistical tests for contingency tables. OASIS constructs a test-statistic which is linear in the normalized data matrix, providing closed form p-value bounds through classical concentration inequalities. In the process, OASIS provides a decomposition of the table, lending interpretability to its rejection of the null. We derive the asymptotic distribution of the OASIS test statistic, showing that these finite-sample bounds correctly characterize the test statistic's p-value up to a variance term. Experiments on genomic sequencing data highlight the power and interpretability of OASIS. The same method based on OASIS significance calls detects SARS-CoV-2 and Mycobacterium Tuberculosis strains de novo, which cannot be achieved with current approaches. We demonstrate in simulations that OASIS is robust to overdispersion, a common feature in genomic data like single cell RNA-sequencing, where under accepted noise models OASIS still provides good control of the false discovery rate, while Pearson's X2 test consistently rejects the null. Additionally, we show on synthetic data that OASIS is more powerful than Pearson's X2 test in certain regimes, including for some important two group alternatives, which we corroborate with approximate power calculations.

Nephroprotective effect of vitamin D Against Levofloxacin-induced renal injury: an observational study.

The pathogenesis of kidney damage involves complicated interactions between vascular endothelial and tubular cell destruction. Evidence has shown that vitamin D may have anti-inflammatory effects in several models of kidney damage. In this study, we evaluated the effects of synthetic vitamin D on levofloxacin-induced renal injury in rats. Forty-two white Albino rats were divided into six groups, with each group comprising seven rats. Group I served as the control (negative control) and received intraperitoneal injections of normal saline (0.5 ml) once daily for twenty-one days. Group II and Group III were treated with a single intraperitoneal dose of Levofloxacin (50 mg/kg/day) and (100 mg/kg/day), respectively, for 14 days (positive control groups). Group IV served as an additional negative control and received oral administration of vitamin D3 (500 IU/rat/day) for twenty-one days. In Group V, rats were orally administered vitamin D3 (500 IU/rat/day) for twenty-one days, and intraperitoneal injections of Levofloxacin (50 mg/kg/day) were administered on day 8 for 14 days. Group VI received oral vitamin D3 supplementation (500 IU/rat/day) for twenty-one days, followed by intraperitoneal injections of Levofloxacin (100 mg/kg/day) on day 8 for fourteen days. Blood samples were collected to measure creatinine, urea, malondialdehyde, glutathione reductase, and superoxide dismutase levels. Compared to the positive control group, vitamin D supplementation lowered creatinine, urea, and malondialdehyde levels, while increasing glutathione reductase and superoxide dismutase levels. Urea, creatinine, and malondialdehyde levels were significantly (p<0.05) higher in rats administered LFX 50mg and 100mg compared to rats given (LFX + vitamin D). The main findings of this study show that vitamin D reduces renal dysfunction, suggesting that vitamin D has antioxidant properties and may be used to prevent renal injury.