Statistical Code to Support the Scientific Story

Localio, A. Russell; Goodman, Steven N.; Meibohm, Anne R.; Cornell, John E.; Stack, Catharine B.; Ross, Eric A.; Mulrow, Cynthia D.

doi:10.7326/m17-3431

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…Finally, the omnipresence of proprietary software such as GraphPad Prism, which are based on a graphical user interface (GUI) and whose codes are generally not accessible (unlike open-source packages), strongly suggests that the mandatory disclosure of code may be very difficult at the moment in preclinical science. This situation is different from what has been recommended in other fields 21 . Therefore, the full disclosure of the software used (including the exact version and complete description of commands implemented) might remain acceptable at present in articles using packages such as Prism.…”

Section: Discussioncontrasting

confidence: 79%

Insufficient transparency of statistical reporting in preclinical research: a scoping review

Gosselin

2021

Sci Rep

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Non-transparent statistical reporting contributes to the reproducibility crisis in life sciences, despite guidelines and educational articles regularly published. Envisioning more effective measures for ensuring transparency requires the detailed monitoring of incomplete reporting in the literature. In this study, a systematic approach was used to sample 16 periodicals from the ISI Journal Citation Report database and to collect 233 preclinical articles (including both in vitro and animal research) from online journal content published in 2019. Statistical items related to the use of location tests were quantified. Results revealed that a large proportion of articles insufficiently describe tests (median 44.8%, IQR [33.3–62.5%], k = 16 journals), software (31%, IQR [22.3–39.6%]) or sample sizes (44.2%, IQR [35.7–55.4%]). The results further point at contradictory information as a component of poor reporting (18.3%, IQR [6.79–26.7%]). No detectable correlation was found between journal impact factor and the quality of statistical reporting of any studied item. The under-representation of open-source software (4.50% of articles) suggests that the provision of code should remain restricted to articles that use such packages. Since mounting evidence indicates that transparency is key for reproducible science, this work highlights the need for a more rigorous enforcement of existing guidelines.

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Section: Discussioncontrasting

confidence: 79%

Insufficient transparency of statistical reporting in preclinical research: a scoping review

Gosselin

2021

Sci Rep

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“…All analyses were conducted using R for Windows [ 32 ], using the packages dplyr , MASS , mice , mitools , openxlsx , PresenceAbsence , pROC , rms , rpart , shrink , and tableone . Our work was conducted following the concept of reproducible research and the R-code is available upon request [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Prognostic function to estimate the probability of meaningful clinical improvement after surgery - Results of a prospective multicenter observational cohort study on patients with lumbar spinal stenosis

et al. 2018

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BackgroundApproximately two thirds of patients with lumbar spinal stenosis (LSS) who undergo surgical treatment benefit from the surgery. The objective of this study was to derive a prognostic probability function (PPF) to identify patients with a high probability of post-surgical improvement because there is currently no method available.MethodsIn this multicenter, prospective, observational study, we collected data from eight medical centers in Switzerland in which patients underwent surgery for LSS. The endpoints were meaningful clinically important differences (MCID) in pain and disability one year after baseline. We developed a PPF named PROCESS (PostopeRative OutComE Spinal Stenosis), based on a large set of prognostic indicators extracted from the literature. The PPF was derived using data from a random subset of two thirds of the patients and validated in the remaining third. We addressed overfitting by shrinking the regression coefficients. The area under the ROC curve (AUC) and calibration determined the accuracy of the PPF.ResultsIn this study, 452 LSS patients received surgery. 73% of the 300 patients in the derivation subset reached an MCID in pain and 68% reached an MCID in disability. The corresponding values were 70% and 63% in the validation subset, respectively. In the derivation subsample, the AUC was 0.64 (95% CI 0.57 to 0.71) for of the PPF predicting MCID in pain and 0.71 (0.64 to 0.77) for MCID in disability, after shrinkage. The corresponding numbers were 0.62 (0.52 to 0.72) and 0.70 (0.60 to 0.79) in the validation subsample, and the PPF showed good calibration.ConclusionsSurgical treatment for patients with lumbar spinal stenosis is being performed with increasing frequency. PROCESS is conditional on the individual pattern of preoperatively available prognostic indicators, and may be helpful for clinicians in counselling patients and in guiding the discussion on individual treatment decision in the era of personalized medicine.

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“…The work was carried out following the concept of reproducible research, and the R-code is available upon request [41]. The results of the study were reported according to the TRIPOD guidelines [42]. Figure 1 shows the flowchart for patient inclusion in the study.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Development of a model to predict the probability of incurring a complication during spine surgery

et al. 2021

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Purpose Predictive models in spine surgery are of use in shared decision-making. This study sought to develop multivariable models to predict the probability of general and surgical perioperative complications of spinal surgery for lumbar degenerative diseases. Methods Data came from EUROSPINE's Spine Tango Registry (1.2012–12.2017). Separate prediction models were built for surgical and general complications. Potential predictors included age, gender, previous spine surgery, additional pathology, BMI, smoking status, morbidity, prophylaxis, technology used, and the modified Mirza invasiveness index score. Complete case multiple logistic regression was used. Discrimination was assessed using area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI). Plots were used to assess the calibration of the models. Results Overall, 23′714/68′111 patients (54.6%) were available for complete case analysis: 763 (3.2%) had a general complication, with ASA score being strongly predictive (ASA-2 OR 1.6, 95% CI 1.20–2.12; ASA-3 OR 2.98, 95% CI 2.19–4.07; ASA-4 OR 5.62, 95% CI 3.04–10.41), while 2534 (10.7%) had a surgical complication, with previous surgery at the same level being an important predictor (OR 1.9, 95%CI 1.71–2.12). Respectively, model AUCs were 0.74 (95% CI, 0.72–0.76) and 0.64 (95% CI, 0.62–0.65), and calibration was good up to predicted probabilities of 0.30 and 0.25, respectively. Conclusion We developed two models to predict complications associated with spinal surgery. Surgical complications were predicted with less discriminative ability than general complications. Reoperation at the same level was strongly predictive of surgical complications and a higher ASA score, of general complications. A web-based prediction tool was developed at https://sst.webauthor.com/go/fx/run.cfm?fx=SSTCalculator.

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Statistical Code to Support the Scientific Story

Cited by 16 publications

References 7 publications

Insufficient transparency of statistical reporting in preclinical research: a scoping review

Insufficient transparency of statistical reporting in preclinical research: a scoping review

Prognostic function to estimate the probability of meaningful clinical improvement after surgery - Results of a prospective multicenter observational cohort study on patients with lumbar spinal stenosis

Development of a model to predict the probability of incurring a complication during spine surgery

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