[19,20], or not implementing the intervention as prescribed (i.e. We'll go over how to identify the different types and which ones require, Sulfa allergies are different from sulfite allergies. Bethesda, MD 20894, Web Policies For more detailed coverage of SA, we refer the reader to these references For a primary analysis of data from a prospective randomized controlled trial (RCT), the key questions for investigators (and for readers) include: How confident can I be about the results? The negative binomial model provides an alternative approach for analyzing discrete data where over-dispersion is a problem Normal distribution for continuous outcomes, Poisson distribution for count data, or binomial distribution for binary outcome data). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, Sensitivity analysis, Clinical trials, Robustness. A sensitivity analysis is a method to determine the robustness of trial findings by examining the extent to which results are affected by changes in methods, models, values of unmeasured variables, or assumptions. Sainani KL. An antibiotic that bacteria has shown resistance to shouldnt be used to treat your infection. A: Ideally, one can study the impact of all key elements using a factorial designwhich would allow the assessment of the impact of individual and joint factors. The proposed reporting changes can be as follows: In Methods Section: Report the planned or posthoc sensitivity analyses and rationale for each. Alternatively, one can vary one factor at a time to be able to assess whether the factor is responsible for the resulting impact (if any). If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened. and CARAT-global 18, with . In that case, one needs to incorporate the anticipated sensitivity analyses in the statistical analysis plan (SAP), which needs to be completed before analyzing the data. What are the risks of a sensitivity analysis? For example, for continuous data, one can redo the analysis assuming a Student-T distributionwhich is symmetric, bell-shaped distribution like the Normal distribution, but with thicker tails; for count data, once can use the Negative-binomial distributionwhich would be useful to assess the robustness of the results if over-dispersion is accounted for [52]. [52] to analyze discrete outcome data from a clinical trial designed to evaluate the effectiveness of a pre-habilitation program in preventing functional decline among physically frail, community-living older persons. Tai BC, Grundy R, Machin D: On the importance of accounting for competing risks in pediatric brain cancer: II. JAMA. The United Kingdom (UK) National Institute of Health and Clinical Excellence (NICE) also recommends the use of sensitivity analysis in exploring alternative scenarios and the uncertainty in cost-effectiveness results [9]. Statistics in medicine. They are a critical way to assess the impact, effect or influence of key assumptions or variations--such as different methods of analysis, definitions of outcomes, protocol deviations, missing data, and outliers--on the overall . Guide to the methods of technology appraisal Holbrook A, Thabane L, Keshavjee K, Dolovich L, Bernstein B, Chan D, Troyan S, Foster G, Gerstein H. Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. 10.1002/pds.1200. Some sensitivity analyses in this trial were performed by excluding participants with high baseline levels of depression (outliers) and showed a statistically significant reduction in depression in the intervention group compared to the control. There are several options for dealing with competing risks in survival analyses: (1) to perform a survival analysis for each event separately, where the other competing event(s) is/are treated as censored; the common representation of survival curves using the Kaplan-Meier estimator is in this context replaced by the cumulative incidence function (CIF) which offers a better interpretation of the incidence curve for one risk, regardless of whether the competing risks are independent; (2) to use a proportional sub-distribution hazard model (Fine & Grey approach) in which subjects that experience other competing events are kept in the risk set for the event of interest (i.e. How will ignoring the serial correlation of measurements within a patient impact the results? A previously-reported trial compared low molecular weight heparin (LMWH) with oral anticoagulant therapy for the prevention of recurrent venous thromboembolism (VTE) in patients with advanced cancer, and a subsequent study presented sensitivity analyses comparing the results from standard survival analysis (Kaplan-Meier method) with those from competing risk methodsnamely, the cumulative incidence function (CIF) and Gray's test [52]. Likewise, understanding the distribution of certain variables can help to determine which cut points would be relevant. However, a sensitivity analysis based on the PP analysis (including only those with risky drinking at baseline and who answered the follow-up survey; n=408) suggested a small beneficial effect on weekly alcohol consumption A 2011 paper reported the sensitivity analyses of different strategies for imputing missing data in cluster RCTs with a binary outcome using the community hypertension assessment trial (CHAT) as an example. A researcher might choose to explore differences in the characteristics of the participants who were included in the ITT versus the PP analyses. A previously-reported trial compared low molecular weight heparin (LMWH) with oral anticoagulant therapy for the prevention of recurrent venous thromboembolism (VTE) in patients with advanced cancer, and a subsequent study presented sensitivity analyses comparing the results from standard survival analysis (Kaplan-Meier method) with those from competing risk methodsnamely, the cumulative incidence function (CIF) and Gray's test NICE: Guide to the methods of technology appraisal. Typically subgroup analyses require specification of the subgroup hypothesis and rationale, and performed through inclusion of an interaction term (i.e. 10.1080/10543400903242761. For categorical responses or count data, generalized estimating equations [GEE] and random-effects generalized linear mixed models [GLMM] methods may be used [41, 42]. Williams NH, Edwards RT, Linck P, Muntz R, Hibbs R, Wilkinson C, Russell I, Russell D, Hounsome B: Cost-utility analysis of osteopathy in primary care: results from a pragmatic randomized controlled trial. A sensitivity analysis is the hypothesis of what will happen if variables are changed. What are the results for a sensitivity analysis? The authors declare that they have no competing interests. A drug rash or eruption is a type of drug reaction involving your skin. Youll also likely have to take the combination of drugs for an extended time period. will also be available for a limited time. If similar, just state that the results or conclusions remain robust. Disclaimer, National Library of Medicine In this paper we will provide a detailed exploration of the key aspects of sensitivity analyses including: 1) what sensitivity analyses are, why they are needed, and how often they are used in practice; 2) the different types of sensitivity analyses that one can do, with examples from the literature; 3) some frequently asked questions about sensitivity analyses; and 4) some suggestions on how to report the results of sensitivity analyses in clinical trials. A trial evaluated the effects of lansoprazole on gastro-esophageal reflux disease in children from 19 clinics with asthma. 10.1002/sim.3617. If this assumption is valid, then the complete-case analysis by including predictors of missing observations will provide consistent estimates of the parameter. Multiple imputation (MI) technique is currently the best available method of dealing with missing data under the assumption that data are missing at random (MAR) Lee CS, Blazes M, Lorch A, Pershing S, Hyman L, Ho AC, Haller J, Miller JW, Chew EY, Lum F, Lee AY. These studies further highlight the need for a sensitivity analysis of competing risks when they are present in trials. More specifically, it is analyzing what will happen if one variable is changed. Before Experts provide parents with some advice. Bethesda, MD 20894, Web Policies [18]. In general, a sensitivity analysis studies how different sources of uncertainty in a mathematical model impact a model's overall uncertainty. Missing data on the hypertensive disorders is dependent (conditional) on being pregnant in the first place. Sensitivity analysis can predict the outcomes of an event given a specific range of variables, and an analyst can use this information to understand how a change in one variable affects the other variables or outcomes. For example, in a trial using a composite of death, myocardial infarction or stroke, if someone dies, they cannot experience a subsequent event, or stroke or myocardial infarctiondeath can be a competing risk event. Specificity (true negative rate) refers to the probability of a negative test, conditioned on truly being negative. 2009 Dec 10;27(35):5958-64. doi: 10.1200/JCO.2009.22.4329. 00:30 about sensitivity analysis for observational studies, 00:34 looking back and moving forward. Martnez-Gonzlez M, Martn-Calvo N, Bretos-Azcona T, Carlos S, Delgado-Rodrguez M. Int J Environ Res Public Health. You can learn more about how we ensure our content is accurate and current by reading our. It provides insights into the problems with the model. Clipboard, Search History, and several other advanced features are temporarily unavailable. Bethesda, MD 20894, Web Policies J Am Soc Nephrol. Sensitivity Analysis The sensitivity analysis indicated that parameter p2 is the least critical parameter and the least likely to affect the predictions of the model. There are several methods of accounting or adjusting for similarities within clusters, or clustering in studies where this phenomenon is expected or exists as part of the design (e.g., in cluster randomization trials). Cite this article. T-test vs. analysis without assuming Normality e.g. Although a screening test ideally is both highly sensitive and . Sensitivity analysis in NPV analysis is a technique to evaluate how the profitability of a specific project will change based on changes to underlying input variables. [47]. Q: How do I choose between the results of different sensitivity analyses? A: Ideally, one can study the impact of all key elements using a factorial designwhich would allow the assessment of the impact of individual and joint factors. reported lower costs per quality of life year ratios when they excluded outliers [17]. Assessing robustness of the findings to different methods of analysis was the most common type of sensitivity analysis reported in both types of journals. 2009, 60: 549-576. Using such sensors, it is possible to study physiological mechanisms at the cellular, tissue, and organ levels and determine the state of health and diseases. Q: Do I have to report all the results of the sensitivity analyses? Learn more about the differences between sulfa allergies and sulfite allergies and how to treat, The terms in vivo and in vitro refer to how certain studies, laboratory experiments, and medical procedures are performed. Purposes of Sensitivity Analysis Sensitivity Analysis can help you to find important connections between: Model inputs, Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials. This has led to several reporting guidelines, starting with the CONSORT Statement Chu R, Thabane L, Ma J, Holbrook A, Pullenayegum E, Devereaux PJ. robustness across subgroups). For example, you may feel slight pain or a mild pinching sensation during the blood draw. Sensitivity analysis is a common tool that is used to determine the risk of a model, while identifying the critical input parameters. FOIA Addressing unmeasured confounding in comparative observational research. de Pauw BE, Sable CA, Walsh TJ, Lupinacci RJ, Bourque MR, Wise BA, Nguyen BY, DiNubile MJ, Teppler H. Impact of alternate definitions of fever resolution on the composite endpoint in clinical trials of empirical antifungal therapy for neutropenic patients with persistent fever: analysis of results from the Caspofungin Empirical Therapy Study. The assessment of robustness is often based on the magnitude, direction or statistical significance of the estimates. 10.1146/annurev.psych.58.110405.085530. Clear rationale is needed for every sensitivity analysis. The point for a controlled disease was CARAT10-rhinitis 8, sensitivity of 80% and specificity of 72%; CARAT10-asthma > 16, with sensitivity and specificity of 76%. 2018 Apr;27(4):373-382. doi: 10.1002/pds.4394. A negative binomial regression model was used 10.1002/j.1552-4604.1997.tb04353.x. MM, BD, DK, VBD, RD, VF, MB, JL reviewed and revised draft versions of the manuscript. The complexity of these tasks requires a continuous interplay among different technologies during all the phases of the experimental procedures. Structural determinants of tailored behavioral health services for sexual and gender minorities in the United States, 2010 to 2020: a panel analysis. Uses of Sensitivity Analysis. McKay E, Kirk H, Coxon J, Courtney D, Bellgrove M, Arnatkeviciute A, Cornish K. BMJ Open. The most important factor is the rationale for doing any sensitivity analysis. Youll likely have to take a higher dosage and for a longer time period if youre taking a drug from the intermediate group. Before The goal of a sensitivity analysis is to identify results that are most dependent on questionable or unsupported assumptions. The purpose of this tutorial is to provide an overview of controlled MI procedures for missing data sensitivity analysis and a practical guide to their use for a continuous outcome, with worked examples and Stata code. In clinical trials some participants may not adhere to the intervention they were allocated to receive or comply with the scheduled treatment visits. Socioeconomic, Ethnocultural, Substance- and Cannabinoid-Related Epidemiology of Down Syndrome USA 1986-2016: Combined Geotemporospatial and Causal Inference Investigation. Porta M: A dictionary of epidemiology. 10.1111/j.1755-5922.2009.00109.x. Therefore despite their importance, sensitivity analyses are under-used in practice. [35]. Graham JW: Missing data analysis: making it work in the real world. In Discussion Section: Discuss the key limitations and implications of the results of the sensitivity analyses on the conclusions or findings. Peters TJ, Richards SH, Bankhead CR, Ades AE, Sterne JA: Comparison of methods for analysing cluster randomized trials: an example involving a factorial design. If different, report the results of the sensitivity analyses along with the primary results. Modifiable and non-modifiable risk factors of dementia on midlife cerebral small vessel disease in cognitively healthy middle-aged adults: the PREVENT-Dementia study. The sensitivity and specificity were however determined with a 50% prevalence of PACG (1,000 PACG and 1,000 normals) with PPV of 95%. Analysis of Incomplete Multivariate Data. Normal distribution for continuous outcomes, Poisson distribution for count data, or binomial distribution for binary outcome data). You may also experience medication side effects. Missing data analysis: making it work in the real world. JAMA: the journal of the American Medical Association. Sensitivity Analysis. [, - Perform a survival analysis for each event separately, - Use a proportional sub-distribution hazard model (Fine & Grey approach), - Fit one model by taking into account all the competing risks together An official website of the United States government. We start by describing what sensitivity analysis is, why it is needed and how often it is done in practice. 179, p. 49583 The negative binomial model provided an improved fit to the data than the Poisson regression model. 2010;10:1. doi: 10.1186/1471-2288-10-1. 2007, New York, NY: Springer Verlag, Pintilie M: Competing Risks: A Practical Perspective. All authors reviewed several draft versions of the manuscript and approved the final manuscript. It is this sensitivity analysis we present here. 2009, 28 (19): 2451-2472. Pharmacoepidemiol Drug Saf. 00:37 So this is based on ongoing work; 00:39 with several people Bo Zhang, Ting Ye and Dylan Small; 00:45 at University of Pennsylvania, 00:46 and also Joe Hogan at Brown University. Grams ME, Coresh J, Segev DL, Kucirka LM, Tighiouart H, Sarnak MJ: Vascular disease, ESRD, and death: interpreting competing risk analyses. 2011, 12: 108-10.1186/1745-6215-12-108. a broad or narrow definition is used. That will help you find a family of models you could estimate. SA: Sensitivity analysis; US: United States; FDA: Food and Drug Administration; EMEA: European Medicines Association; UK: United Kingdom; NICE: National Institute of Health and Clinical Excellence; RCT: Randomized controlled trial; ITT: Intention-to-treat; PP: Per-protocol; AT: As-treated; LOCF: Last observation carried forward; MI: Multiple imputation; MAR: Missing at random; GEE: Generalized estimating equations; GLMM: Generalized linear mixed models; CHAT: Community hypertension assessment trial; PSA: Prostate specific antigen; CIF: Cumulative incidence function; ESRD: End stage renal disease; IV: Instrumental variable; ANCOVA: Analysis of covariance; SAP: Statistical analysis plan; CONSORT: Consolidated Standards of Reporting Trials. The Ishigami function (Ishigami and Homma, 1989) is a well-known test function for uncertainty and sensitivity analysis methods because of its strong nonlinearity and peculiar dependence on x 3. Lets discuss the details. Montori VM, Guyatt GH: Intention-to-treat principle. On the other hand, there is some guidance on how sensitivity analyses need to be reported in economic analyses California Privacy Statement, A higher percentage of papers published in health economics than in medical journals (30.8% vs. 20.3%) reported some sensitivity analyses. It is critical to distinguish between sensitivity and supplementary or other. [55]. Zetta S, Smith K, Jones M, Allcoat P, Sullivan F: Evaluating the Angina Plan in Patients Admitted to Hospital with Angina: A Randomized Controlled Trial. In a costutility analysis of a practice-based osteopathy clinic for subacute spinal pain, Williams et al. Technometrics. Springer Nature. Let's say that the cost of raw materials has increased. Clean catch urine culture or catheterized specimen urine culture. In these models it is assumed that missing data are MAR. 1991, 266 (1): 93-98. [28,29]. A: Secondary analyses are typically analyses of secondary outcomes. They help in decision making. 2008, New York, NY: Wiley-Interscience, Hunink MGM, Glasziou PP, Siegel JE, Weeks JC, Pliskin JS, Elstein AS, Weinstein MC: Decision Making in Health and Medicine: Integrating Evidence and Values. de Pauw BE, Sable CA, Walsh TJ, Lupinacci RJ, Bourque MR, Wise BA, Nguyen BY, DiNubile MJ, Teppler H: Impact of alternate definitions of fever resolution on the composite endpoint in clinical trials of empirical antifungal therapy for neutropenic patients with persistent fever: analysis of results from the Caspofungin Empirical Therapy Study. Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials. Sensitivity and specificity are characteristics of a test.. 10.2215/CJN.03460412. State of being sensitive. Ma J, Akhtar-Danesh N, Dolovich L, Thabane L: Imputation strategies for missing binary outcomes in cluster randomized trials. The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2288/13/92/prepub. Viel JF, Pobel D, Carre A. The study concluded that the probability of prognostic imbalance in small trials could be substantial. When data are MAR or MCAR, they are often referred to as ignorable (provided the cause of MAR is taken into account). Competing-risk analysis of ESRD and death among patients with type 1 diabetes and macroalbuminuria. Accessibility Rather, the aim is to assess the robustness or consistency of the results under different methods, subgroups, definitions, assumptions and so on. [52]. 2011, 11: 4-10.1186/1471-2288-11-4. The CHAT intervention was not superior to usual care Typically, it is advisable to limit sensitivity analyses to the primary outcome. The best way to deal with missing data is prevention, by steps taken in the design and data collection stages, some of which have been described by Little et al. A sensitivity analysis is a test that determines the "sensitivity" of bacteria to an antibiotic. HHS Vulnerability Disclosure, Help Article Holbrook A, Thabane L, Keshavjee K, Dolovich L, Bernstein B, Chan D, Troyan S, Foster G, Gerstein H: Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. The complete case analysis, which is less conservative, showed some borderline improvement in the primary outcome (psoriatic arthritis response criteria), while the intention-to-treat analysis did not In each case, we provide examples of actual studies where sensitivity analyses were performed, and the implications of these sensitivity analyses. Understanding the Relationship Between Antibiotics and Bacteria, hematoma (a bruise where blood accumulates under the skin), infection (usually prevented by the skin being cleaned before the needle is inserted), excessive bleeding (bleeding for a long period afterwards may indicate a more serious bleeding condition and should be reported to your doctor). Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada, Lehana Thabane,Lawrence Mbuagbaw,Shiyuan Zhang,Zainab Samaan,Maura Marcucci,Chenglin Ye,Marroon Thabane,Brittany Dennis,Daisy Kosa,Victoria Borg Debono&Charles H Goldsmith, Departments of Pediatrics and Anesthesia, McMaster University, Hamilton, ON, Canada, Center for Evaluation of Medicine, St Josephs Healthcare Hamilton, Hamilton, ON, Canada, Biostatistics Unit, Father Sean OSullivan Research Center, St Josephs Healthcare Hamilton, Hamilton, ON, Canada, Lehana Thabane,Lawrence Mbuagbaw,Shiyuan Zhang,Maura Marcucci,Chenglin Ye,Brittany Dennis,Daisy Kosa,Victoria Borg Debono&Charles H Goldsmith, Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON, Canada, Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, ON, Canada, Population Genomics Program, McMaster University, Hamilton, ON, Canada, Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada, Department of Nephrology, Toronto General Hospital, Toronto, ON, Canada, Department of Pediatrics, McMaster University, Hamilton, ON, Canada, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada, McMaster Integrative Neuroscience Discovery & Study (MiNDS) Program, McMaster University, Hamilton, ON, Canada, Department of Biostatistics, Korea University, Seoul, South Korea, Department of Clinical Epidemiology, University of Ottawa, Ottawa, ON, Canada, Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada, You can also search for this author in In pediatric brain cancer: II Oxford: Oxford University Press, (. Biased estimates and under-estimation of the sensitivity of bacteria, that sensitivity analysis medicine be! 2010 to 2020: a review of methodological considerations some statistical approaches to with. In late-stage oncology trials Publishing ; 2022 Jan. BMC Med Res Methodol et al gives us an idea about we, futility clinical trial sample any area that has an infection and to monitor in. 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