report writing in research study in biostatistics

• BioBoston Consulting

Biostatistics in Clinical Research

Biostatistics, the application of statistical methods to biological and health sciences, is a cornerstone of clinical research. It plays a critical role in designing studies, analyzing data, and interpreting results. This article provides an overview of biostatistics in clinical research, highlighting its importance, key concepts, methodologies, and the challenges faced by biostatisticians.

Importance of Biostatistics in Clinical Research

Biostatistics is essential in clinical research for several reasons:

Study Design : Biostatistics guides the design of clinical trials, ensuring they are scientifically sound and ethically viable. It helps in determining the sample size, randomization methods, and stratification processes.

Data Analysis : Statistical methods are employed to analyze the data collected during clinical trials. This analysis is crucial for assessing the efficacy and safety of new treatments.

Interpretation : Biostatistics aids in interpreting the results, allowing researchers to draw valid and reliable conclusions.

Regulatory Approval : Regulatory bodies, such as the FDA and EMA, require robust statistical evidence to approve new drugs and treatments.

Key Concepts in Biostatistics

Several fundamental concepts underpin biostatistics in clinical research:

Randomization : Randomly assigning participants to different groups to eliminate bias and ensure the comparability of groups.

Blinding : Concealing the treatment allocation from participants and/or researchers to prevent bias.

Sample Size Calculation : Determining the number of participants needed to detect a clinically significant effect with adequate power.

Hypothesis Testing : Formulating and testing hypotheses using statistical methods to determine if observed effects are significant.

Confidence Intervals : Providing a range of values within which the true effect size is likely to fall, offering a measure of precision.

P-values : Assessing the strength of evidence against the null hypothesis, with lower values indicating stronger evidence.

Methodologies in Biostatistics

Biostatisticians employ various methodologies to analyze clinical research data:

Descriptive Statistics : Summarizing data using measures such as mean, median, standard deviation, and proportions.

Inferential Statistics : Making inferences about a population based on sample data. Common methods include t-tests, chi-square tests, and ANOVA.

Regression Analysis : Examining relationships between variables. Linear and logistic regressions are widely used in clinical research.

Survival Analysis : Analyzing time-to-event data, crucial for studies with endpoints like death or disease progression. Methods include Kaplan-Meier curves and Cox proportional hazards models.

Meta-Analysis : Combining data from multiple studies to derive a pooled estimate of effect size, enhancing the statistical power and generalizability of findings.

Challenges in Biostatistics

Biostatisticians face several challenges in clinical research:

Missing Data : Incomplete data can bias results. Techniques like multiple imputation and sensitivity analysis are used to address this issue.

Confounding Variables : Variables that are correlated with both the treatment and the outcome can distort the observed effects. Methods such as stratification and multivariable adjustment are used to control for confounders.

Multiplicity : Conducting multiple comparisons increases the risk of Type I errors (false positives). Adjustments such as the Bonferroni correction are applied to mitigate this risk.

Complex Data Structures : Data from longitudinal studies, clustered designs, or high-dimensional data (e.g., genomics) require advanced statistical techniques.

Reproducibility : Ensuring that results can be replicated is crucial for the credibility of research findings. Transparent reporting and sharing of data and code are essential for reproducibility.

Biostatistics is integral to clinical research, providing the tools needed to design robust studies, analyze complex data, and draw valid conclusions. Despite its challenges, advances in statistical methodologies and computational tools continue to enhance the field's ability to contribute to medical science. As clinical research evolves, the role of biostatistics will remain vital in advancing healthcare and improving patient outcomes.

Contact   BioBoston Consulting  today or visit our   website  to learn more about how we can support your organization.

Recent Posts

70% of Clinical Trials Fail Due to Delays: How to Fix Your Logistics to Avoid Costly Mistakes

Unlocking Success: 7 Alarming Statistics on Clinical Development Plans You Can't Ignore

70% of Clinical Trials Face Data Breaches: Why Your Research Could Be at Risk

Subscribe to Our Newsletter

Thanks for submitting!

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Biostatistics Series Module 5: Determining Sample Size

Affiliations.

• 1 Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, West Bengal, India.
• 2 Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Mumbai, Maharashtra, India.
• PMID: 27688437
• PMCID: PMC5029233
• DOI: 10.4103/0019-5154.190119

Determining the appropriate sample size for a study, whatever be its type, is a fundamental aspect of biomedical research. An adequate sample ensures that the study will yield reliable information, regardless of whether the data ultimately suggests a clinically important difference between the interventions or elements being studied. The probability of Type 1 and Type 2 errors, the expected variance in the sample and the effect size are the essential determinants of sample size in interventional studies. Any method for deriving a conclusion from experimental data carries with it some risk of drawing a false conclusion. Two types of false conclusion may occur, called Type 1 and Type 2 errors, whose probabilities are denoted by the symbols σ and β. A Type 1 error occurs when one concludes that a difference exists between the groups being compared when, in reality, it does not. This is akin to a false positive result. A Type 2 error occurs when one concludes that difference does not exist when, in reality, a difference does exist, and it is equal to or larger than the effect size defined by the alternative to the null hypothesis. This may be viewed as a false negative result. When considering the risk of Type 2 error, it is more intuitive to think in terms of power of the study or (1 - β). Power denotes the probability of detecting a difference when a difference does exist between the groups being compared. Smaller α or larger power will increase sample size. Conventional acceptable values for power and α are 80% or above and 5% or below, respectively, when calculating sample size. Increasing variance in the sample tends to increase the sample size required to achieve a given power level. The effect size is the smallest clinically important difference that is sought to be detected and, rather than statistical convention, is a matter of past experience and clinical judgment. Larger samples are required if smaller differences are to be detected. Although the principles are long known, historically, sample size determination has been difficult, because of relatively complex mathematical considerations and numerous different formulas. However, of late, there has been remarkable improvement in the availability, capability, and user-friendliness of power and sample size determination software. Many can execute routines for determination of sample size and power for a wide variety of research designs and statistical tests. With the drudgery of mathematical calculation gone, researchers must now concentrate on determining appropriate sample size and achieving these targets, so that study conclusions can be accepted as meaningful.

Keywords: Effect size; Type 1 error; Type 2 error; power; sample size.

PubMed Disclaimer

Population vis-à -vis samples

Similar articles

• [Beta risk: an unrecognized risk of statistical error]. Jenny JY. Jenny JY. Rev Chir Orthop Reparatrice Appar Mot. 2001 Apr;87(2):170-2. Rev Chir Orthop Reparatrice Appar Mot. 2001. PMID: 11319429 French.
• Significance, Errors, Power, and Sample Size: The Blocking and Tackling of Statistics. Mascha EJ, Vetter TR. Mascha EJ, et al. Anesth Analg. 2018 Feb;126(2):691-698. doi: 10.1213/ANE.0000000000002741. Anesth Analg. 2018. PMID: 29346210
• How many do I need? Basic principles of sample size estimation. Devane D, Begley CM, Clarke M. Devane D, et al. J Adv Nurs. 2004 Aug;47(3):297-302. doi: 10.1111/j.1365-2648.2004.03093.x. J Adv Nurs. 2004. PMID: 15238124
• Concepts in sample size determination. Rao UK. Rao UK. Indian J Dent Res. 2012 Sep-Oct;23(5):660-4. doi: 10.4103/0970-9290.107385. Indian J Dent Res. 2012. PMID: 23422614 Review.
• Power calculations in genetic studies. Evans DM, Purcell S. Evans DM, et al. Cold Spring Harb Protoc. 2012 Jun 1;2012(6):664-74. doi: 10.1101/pdb.top069559. Cold Spring Harb Protoc. 2012. PMID: 22661434 Review.
• The Relative Role of Family Affluence and Social Support on Depression and Self-Esteem among Adolescents in Nigeria: a Cross-Sectional Study. Edet B, Essien E, Eleazu F, Abang R, Ochijele E, Daniel F. Edet B, et al. Acta Med Acad. 2023 Dec;52(3):201-211. doi: 10.5644/ama2006-124.421. Acta Med Acad. 2023. PMID: 38407087 Free PMC article.
• Tropical Data: Approach and Methodology as Applied to Trachoma Prevalence Surveys. Harding-Esch EM, Burgert-Brucker CR, Jimenez C, Bakhtiari A, Willis R, Bejiga MD, Mpyet C, Ngondi J, Boyd S, Abdala M, Abdou A, Adamu Y, Alemayehu A, Alemayehu W, Al-Khatib T, Apadinuwe SC, Awaca N, Awoussi MS, Baayendag G, Badiane MD, Bailey RL, Batcho W, Bay Z, Bella A, Beido N, Bol YY, Bougouma C, Brady CJ, Bucumi V, Butcher R, Cakacaka R, Cama A, Camara M, Cassama E, Chaora SG, Chebbi AC, Chisambi AB, Chu B, Conteh A, Coulibaly SM, Courtright P, Dalmar A, Dat TM, Davids T, Djaker MEA, de Fátima Costa Lopes M, Dézoumbé D, Dodson S, Downs P, Eckman S, Elshafie BE, Elmezoghi M, Elvis AA, Emerson P, Epée EE, Faktaufon D, Fall M, Fassinou A, Fleming F, Flueckiger R, Gamael KK, Garae M, Garap J, Gass K, Gebru G, Gichangi MM, Giorgi E, Goépogui A, Gómez DVF, Gómez Forero DP, Gower EW, Harte A, Henry R, Honorio-Morales HA, Ilako DR, Issifou AAB, Jones E, Kabona G, Kabore M, Kadri B, Kalua K, Kanyi SK, Kebede S, Kebede F, Keenan JD, Kello AB, Khan AA, Khelifi H, Kilangalanga J, Kim SH, Ko R, Lewallen S, Lietman T, Logora MSY, Lopez YA, MacArthur C, Macleod C, Makangila F, Mariko B, Martin DL, Masika M, Massae P, Massangaie M, Matendechero HS, Mathewos T, McCullagh S, Meite A, Mendes EP… See abstract for full author list ➔ Harding-Esch EM, et al. Ophthalmic Epidemiol. 2023 Dec;30(6):544-560. doi: 10.1080/09286586.2023.2249546. Epub 2023 Dec 12. Ophthalmic Epidemiol. 2023. PMID: 38085791
• Financial Cost of Hypertension in Urban and Rural Tertiary Health Facilities in Southwest, Nigeria: A Comparative Cross-Sectional Study. Ipinnimo TM, Emmanuel EE, Ipinnimo MT, Agunbiade KH, Ilesanmi OA. Ipinnimo TM, et al. Indian J Community Med. 2023 Mar-Apr;48(2):340-345. doi: 10.4103/ijcm.ijcm_431_22. Epub 2023 Apr 7. Indian J Community Med. 2023. PMID: 37323733 Free PMC article.
• Cognitive impairment following traumatic brain injury in Uganda: Prevalence and associated factors. Kintu TM, Katengeke V, Kamoga R, Nguyen T, Najjuma JN, Kitya D, Wakida EK, Obua C, Rukundo GZ. Kintu TM, et al. PLOS Glob Public Health. 2023 Feb 6;3(2):e0001459. doi: 10.1371/journal.pgph.0001459. eCollection 2023. PLOS Glob Public Health. 2023. PMID: 36962918 Free PMC article.
• Combination of electroacupuncture and pharmacological treatment improves insulin resistance in women with polycystic ovary syndrome: Double-blind randomized clinical trial. Muharam R, Ph D, Srilestari A, Mihardja H, Juvanni Callestya L, Kemal Harzif A. Muharam R, et al. Int J Reprod Biomed. 2022 May 23;20(4):289-298. doi: 10.18502/ijrm.v20i4.10900. eCollection 2022 Apr. Int J Reprod Biomed. 2022. PMID: 35822185 Free PMC article.
• Julios SA. Sample sizes for clinical trials with normal data. Stats Med. 2004;23:1921–86. - PubMed
• Devane D, Begley CM, Clarke M. How many do I need? Basic principles of sample size estimation. J Adv Nursing. 2004;47:297–302. - PubMed
• Karlsson J, Engebretsen L, Dainty K. ISAKOS scientific committee. Considerations on sample size and power calculations in randomized clinical trials. Arthroscopy. 2003;19:997–9. - PubMed
• Cleophas TJ, Zwinderman AH, Cleophas TF, Cleophas EP, editors. Statistical power and sample size. Statistics applied to clinical trials. 4th ed. Amsterdam: Springer; 2009. pp. 81–97.
• Kirkwood BR, Sterne JA, editors. Calculation of required sample size. Essential medical statistics. 2nd ed. Oxford: Blackwell Science; 2003. pp. 413–28.

Related information

Linkout - more resources, full text sources.

• Europe PubMed Central
• Medknow Publications and Media Pvt Ltd
• Ovid Technologies, Inc.
• PubMed Central

Other Literature Sources

• scite Smart Citations

Miscellaneous

• NCI CPTAC Assay Portal
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

Biostatistics articles from across Nature Portfolio

Biostatistics is the application of statistical methods in studies in biology, and encompasses the design of experiments, the collection of data from them, and the analysis and interpretation of data. The data come from a wide range of sources, including genomic studies, experiments with cells and organisms, and clinical trials.

Latest Research and Reviews

In-Clinic and Natural Gait Observations master protocol (I-CAN-GO) to validate gait using a lumbar accelerometer

• Miles Welbourn
• Paul Sheriff

Azacitidine and gemtuzumab ozogamicin as post-transplant maintenance therapy for high-risk hematologic malignancies

• Satoshi Kaito
• Yuho Najima
• Noriko Doki

Impact of COVID-19 on antibiotic usage in primary care: a retrospective analysis

• Anna Romaszko-Wojtowicz
• K. Tokarczyk-Malesa
• K. Glińska-Lewczuk

A standardized metric to enhance clinical trial design and outcome interpretation in type 1 diabetes

The use of a standardized outcome metric enhances clinical trial interpretation and cross-trial comparison. Here, the authors show the implementation of such a metric using type 1 diabetes trial data, reassess and compare results from these trials, and extend its use to define response to therapy.

• Alyssa Ylescupidez
• Henry T. Bahnson
• Carla J. Greenbaum

A novel approach to visualize clinical benefit of therapies for chronic graft versus host disease (cGvHD): the probability of being in response (PBR) applied to the REACH3 study

• Norbert Hollaender
• Ekkehard Glimm
• Robert Zeiser

Reproducibility in pharmacometrics applied in a phase III trial of BCG-vaccination for COVID-19

• Rob C. van Wijk
• Laurynas Mockeliunas
• Ulrika S. H. Simonsson

News and Comment

Mitigating immortal-time bias: exploring osteonecrosis and survival in pediatric all - aall0232 trial insights.

• Shyam Srinivasan
• Swaminathan Keerthivasagam

Response to Pfirrmann et al.’s comment on How should we interpret conclusions of TKI-stopping studies

• Junren Chen
• Robert Peter Gale

Cell-free DNA chromosome copy number variations predict outcomes in plasma cell myeloma

• Wanting Qiang

The role of allogeneic haematopoietic cell transplantation as consolidation after anti-CD19 CAR-T cell therapy in adults with relapsed/refractory acute lymphoblastic leukaemia: a prospective cohort study

• Lijuan Zhou

Clinical trials: design, endpoints and interpretation of outcomes

• Megan Othus
• Mei-Jie Zhang

A SAS macro for estimating direct adjusted survival functions for time-to-event data with or without left truncation

• Zhen-Huan Hu
• Hai-Lin Wang

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Open access
• Published: 05 February 2020

Why do you need a biostatistician?

• Antonia Zapf   ORCID: orcid.org/0000-0001-5339-2472 1 ,
• Geraldine Rauch 2 &
• Meinhard Kieser 3  

BMC Medical Research Methodology volume  20 , Article number:  23 ( 2020 ) Cite this article

26k Accesses

16 Citations

24 Altmetric

Metrics details

The quality of medical research importantly depends, among other aspects, on a valid statistical planning of the study, analysis of the data, and reporting of the results, which is usually guaranteed by a biostatistician. However, there are several related professions next to the biostatistician, for example epidemiologists, medical informaticians and bioinformaticians. For medical experts, it is often not clear what the differences between these professions are and how the specific role of a biostatistician can be described. For physicians involved in medical research, this is problematic because false expectations often lead to frustration on both sides. Therefore, the aim of this article is to outline the tasks and responsibilities of biostatisticians in clinical trials as well as in other fields of application in medical research.

Peer Review reports

What is a biostatistician, what does he or she actually do and what distinguishes him or her from, for example, an epidemiologist? If we would ask this our main cooperation partners like physicians or biologists, they probably could not give a satisfying answer. This is problematic because false expectations often lead to frustration on both sides. Therefore, in this article we want to clarify the tasks and responsibilities of biostatisticians.

There are some expressions which are often used interchangeably to the term ‘biostatistician’. In here, we will use the expression ‘(medical) biostatistics’ as a synonym for ‘medical biometry’ and ‘medical statistics’, and analogously we will do for the term ‘biostatistician’.

In contrast to the clearly defined educational and professional career steps of a physician, there is no unique way of becoming a biostatistician. Only very few universities do indeed offer studies in biometry, which is why most people working as biostatisticians studied something related, subjects such as mathematics or statistics, or application subjects such as medicine, psychology, or biology. So a biostatistician cannot be defined by his or her education, but must be defined by his or her expertise and competencies [ 1 ]. This corresponds to our definition of a biostatistician in this article. The International Biometric Society (IBS) provides a definition of biometrics as a ‘field of development of statistical and mathematical methods applicable in the biological sciences’ [ 2 ]. In here, we will focus on (human) medicine as area of application, but the results can be easily transferred to the other biological sciences like, for example, agriculture or ecology. As mentioned above, there are some professions neighbouring biostatistics, and for many cooperation partners, the differences between biostatisticians, medical informaticians, bioinformaticians, and epidemiologists are not clear. According to the current representatives of these four disciplines within the German Association for Medical Informatics, Biometry and Epidemiology (GMDS) e. V.:

‘Medical biostatistics develops, implements, and uses statistical and mathematical methods to allow for a gain of knowledge from medical data.’ ‘Results are made accessible for the individual medical disciplines and for the public by statistically valid interpretations and suitable presentations’ (authors’ translation from [ 3 ]).

‘Medical informatics is the science of the systematic development, management, storage, processing, and provision of data, information and knowledge in medicine and healthcare’ (authors’ translation from [ 4 ]).

Bioinformatics is a science for ‘the research, development and application of computer-based methods used to answer biomolecular and biomedical research questions. Bioinformatics mainly focusses on models and algorithms for data on the molecular and cell-biological level’ [ 5 ].

‘Epidemiology deals with the spread and the course of diseases and the underlying factors in the public. Apart from conducting research into the causes of disease, epidemiology also investigates options of prevention’ (authors’ translation from [ 6 ]).

Another discipline is data science, which is a relatively new expression used in a multitude of different contexts. Often it is meant as a global summarizing term covering all of the above mentioned fields. As there is no common agreement on what data science is and as this term does not correspond to a uniquely defined profession, this expression will not be discussed in more detail.

The self-descriptions as stated above are rather general and not necessarily complete. Therefore, we will in the following describe the specific tasks and responsibilities of biostatisticians in different important application fields in more detail. This allows us to specify what cooperation partners may (or may not) expect from a biostatistician. Furthermore, clarification of the roles of all involved parties and their successful implementation in practice will overall lead to more efficient collaborations and higher quality.

Tasks and responsibilities of biostatisticians

There are many medical areas where biostatisticians can contribute to the general research progress. These fields of application and the related biostatistical methods are not strictly separated, but there are many overlaps and a classification of the related methodology can be done in various ways. We consider in the following the important application fields of clinical trials, systematic reviews and meta-analysis, observational and complex interventional studies, and statistical genetics to highlight the tasks and responsibilities of biostatisticians working in these areas.

Biostatisticians working in the area of clinical trials

The tasks of biostatisticians in clinical trials are not limited to the analysis of the data, but there are many more responsibilities. It is a quite misguided view that biostatisticians are only required after the data has been collected. According to Lewis et al. (1996), statistical considerations are not only relevant for the analysis of data but also for the design of the trial [ 7 ]. This is not a personal view, but general consensus. It is demanded by the ethics committee and confirmed by the principle investigator and / or the sponsor when stating that the clinical trial will be conducted according to Good Clinical Practice (GCP). The corresponding guideline E6 from the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) explicitly states that statistical expertise should be utilized throughout all stages [ 8 ]. In there, it is stated in Section 5.4.1: ‘The sponsor should utilize qualified individuals (e.g. biostatisticians, clinical pharmacologists, and physicians) as appropriate, throughout all stages of the trial process, from designing the protocol and CRFs [case report forms, AZ] and planning the analyses to analyzing and preparing interim and final clinical trial reports.’ Mansmann et al. [ 9 ] provided a more specific guidance about good biometrical practice in medical research and the responsibilities of a biostatistician. In there, the responsibility of a biostatistician is described as a person participating in the planning and the execution of a study, in the dissemination of the results and in statistical refereeing. These are very general descriptions of the tasks and responsibilities of biostatisticians. In the following, we will explain the biostatistician’s mission in more detail based on the guidance on good biometrical practice [ 9 ] and on the E9 guideline from the ICH about Statistical Principles for Clinical Trials [ 10 ].

In the initial phase of a medical research project, a biostatistician should actively participate in the assessment of the relevance and the feasibility of the study. During the planning phase, the biostatistician should already be involved in the discussion of general study aspects as outlined in more detail below. It is evident that the physician must provide the framework for this. However, the biostatistician can and should point out important biostatistical issues which will have important influence on the whole construct of the study. Therefore, an important part of the biostatistician’s work is to be done long before a study can start. For example, the appropriate study population (special subgroups or healthy subjects in early phases versus large representative samples of the targeted patient population in confirmatory trials) and reasonable primary and secondary endpoints (e.g. suitable to the study aim, objectively measurable, clearly and uniquely defined) need to be identified. He also should make the physician aware of the potential problems with multiple or composite primary endpoints and with surrogate or categorised (especially dichotomized) variables. Another very important topic related to the general study design is blinding and randomisation as techniques to avoid bias. Moreover, the comparators or treatment arms must be specified and it has to be defined how they are embedded in the general study design (for example parallel or crossover). It also has to be specified the aim in whether is to show superiority or non-inferiority of the new treatment and whether interim analyses are reasonable (group sequential designs). Moreover the procedures for data capture and processing have to be discussed at this point. Only after fixing all these planning aspects, the biostatistician can provide an elaborated sample size calculation.

During the ongoing study, main tasks and responsibilities consist of biostatistical monitoring (for example as part of a data safety monitoring board) and performing interim analyses (if planned). If any modifications of the study design are urgently required during the ongoing trial (for example changes within an adaptive designs, or early stopping after an interim analysis), the biostatistician has to be involved in the discussions and decisions as otherwise the integrity of the study can be damaged.

The main data analysis is performed after all patients were recruited and fully observed. However, the statistical methods applied within the data analysis must already be specified during the planning phase within the study protocol. The study protocol should already be as detailed as possible in particular with regard to the analysis of the primary endpoint(s). In addition, the statistical analysis plan (SAP), which must be finalized before start for the data analysis, provides a document which describes all details on the primary, secondary and safety analyses. It also covers possible data transformations, applied point and interval estimators, statistical tests, subgroup analyses, and the consideration of interactions and covariates. Furthermore, the used data sets (for example intention to treat or per protocol), the handling of missing values, and a possible adjustment for multiplicity should be described and discussed. Another important issue is how the integrity of the data and the validity of the statistical software can be guaranteed.

In a last step, after the finalization of the data analysis according to the SAP, the biostatistician contributes to reporting the results in the study report as well as in the related publications submitted to medical journals. He or she is responsible for the appropriate presentation and the correct interpretation of the results.

To sum up, in clinical studies, the tasks and responsibilities of biostatisticians thus extend from the planning phase, through the execution of the study to data analysis and publication of the results. In particular, a careful study planning, in which the contribution of a biostatistician is indispensable, is essential to obtain valid study results.

Biostatisticians working in the area of systematic reviews and meta-analysis

To judge the level of evidence of medical research, different systems of evidence grading were suggested. The recent grading system from the Oxford Centre for Evidence-Based Medicine (OCEBM) defines ten evidence levels. The highest level is a systematic review of high quality studies for the therapeutic as well as for the diagnostic and prognostic context [ 11 ]. The need for such reviews results from the huge amount of articles in the medical literature, which has to be aggregated appropriately [ 12 ]. As Gopalakrishnan and Ganeshkumar describe, the aim of a systematic review is to ‘systematically search, critically appraise, and synthesize on a specific issue’ [ 13 ]. A meta-analysis, which additionally provides a quantitative summary, can be part of a systematic review, if a reasonable number of individual studies are available. The task and responsibilities of biostatisticians in this field are described in the following. As in clinical trials, the biostatistician should already be involved during the planning phase of a systematic review/meta-analysis to discuss the design aspects and the feasibility. Beside the literature search and the collection of the study data (most often not available on an individual patient level), the assessment of the study quality and the risk of bias are important topics. There are different tools for the assessment, like the GRADE approach (Grading of Recommendations, Assessment, Development and Evaluation) [ 14 ] or the QUADAS-2 tool (Quality Assessment of Diagnostic Accuracy Studies) for diagnostic meta-analyses [ 15 ]. A general description of these approaches can be found in the Cochrane Handbook [ 16 ]. The main task of biostatisticians in the field of systematic reviews is then to perform the meta-analysis itself including the calculation of weighted summary measures, creation of graphs, and performing subgroup and sensitivity analyses. As a last step, the biostatistician should again support the physicians in interpreting und publishing the results.

In summary, the tasks and responsibilities of biostatisticians in the field of systematic reviews and meta-analyses relate to the proper planning, the evaluation of the quality of the individual studies, the meta-analysis itself and the publication of the results.

Biostatisticians working in the area of observational and complex interventional studies

In observational studies, where confounding plays a major role, statistical modelling aims at incorporating, investigating, and exploiting relationships between variables using mathematical equations. Other important examples for application of the related techniques are longitudinal data measured repeatedly in time for the same subject or data with an inherent hierarchical structure, for example data of patients observed in different departments within various clinics. Valid conclusions from the analysis are only obtained if the functional relationship between the variables is correctly taken into account [ 17 ]. Another prominent task of statistical modelling is prediction, for example to forecast a future outcome of patients. Frequently, the relationship between the involved variables is complex. For example, patients may undergo several states between start of observation and outcome and the transitions between these states as well as potential competing risks have to be adequately considered (see, for example, Hansen et al. [ 18 ]). Extrapolation is another field of growing interest where techniques of statistical modelling are indispensable. This process can be defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product’ [ 19 ]. For example, clinical trial data for adults may be used to assist the development of treatments for children [ 20 ]. Last but not least, statistical modelling may be of help in situations where data of different origin shall be synthesized to increase evidence, for example, from randomized clinical trials, observational studies, and registries. These examples are by far not exhaustive and illustrate the wide spectrum of potential data sources and applications. It is obvious that there are direct connections to the two working areas of biostatisticians described in the preceding subsections, and consequently there are substantial overlaps in the related tasks and responsibilities. As in the other working areas considered, the biostatistician is responsible for choosing a correct and efficient analysis method that includes all relevant information. Due to the complexity of statistical models, this point is especially challenging here. Furthermore, it is the task of biostatisticians to decide whether the mandatory data required to adequately map the underlying relationships are included in the available data set, whether data quality and completeness is sufficiently high to justify a reliable analysis, and to define appropriate methods dealing with missing values. It is highly recommended to prepare an SAP not only for clinical trials (see Biostatisticians working in the area of clinical trials section) but also for analyses using methods of statistical modelling.

Again, the biostatistician is responsible not only for a proper planning and conducting of the analyses but also for appropriate interpretation and presentation of the results. The particular challenge for biostatisticians in this area is to choose appropriate statistical models for the analysis of data with a complex structure.

Biostatisticians working in the area of statistical genetics

Biostatisticians working in the fields of genetics and genomics are often the responsible persons for the final integration of multidisciplinary expertise in mathematics, statistics, genetics, epidemiology, and bioinformatics to only cite some common ingredients. Planning tasks include the design of research studies, which may pursue exploratory and/or confirmatory objectives. There exist a broad range of possible study designs which make use of well-differentiated modelling techniques. Generated data are often pre-processed by bioinformaticians before it reaches the biostatistician. Pre-processing of sequencing data, for instance, usually comprises quality control of sequenced reads, alignment to the human reference genome and markup of duplicates previously to the identification of somatic mutations and indels. Good knowledge of the limitations of applied pre-processing techniques by the statistician is often very helpful. A strong background and a deep understanding of genetics and genomics as well as an interdisciplinary thinking are a must for biostatisticians working in this area. These competences will be even more important in future. For example, emerging fields of research like Mendelian randomization where genetic variants are used as instruments to predict causality will require an even stronger interaction between statistics and genetics.

In the field of statistical genetics, tasks and responsibilities relate in particular to study planning, critical review of pre-processing, and data analysis using appropriate statistical models.

Biostatistics mainly addresses the development, implementation, and application of statistical methods in the field of medical research [ 3 ]. Therefore, an understanding of the medical background and the clinical context of the research problem they are working on is essential for biostatisticians [ 21 ]. Furthermore, a specific professional expertise is inevitable, and also soft skill competencies are very important. Regarding the professional expertise, the ICH E9 guideline states that a trial statistician should be qualified and experienced [ 10 ]. Qualification, which means biostatistical expertise, covers methodological background (mathematics, statistics, and biostatistics), biostatistical application, medical background, medical documentation, and statistical programming. The experience relates to consulting, planning, conducting and analysing medical studies. Jaki et al. [ 22 ] gave a review of training provided by existing medical statistics programmes and made recommendations for a curriculum for biostatisticians working in drug development. Regarding the soft skills of a biostatistician, some literature exists (for example [ 23 ] or [ 24 ]). Furthermore, Zapf et al. [ 1 ] summarize the professional expertise and the needed soft skills of a biostatistician according to the CanMEDS framework [ 25 ], which was developed to describe the required abilities of physician (the original abbreviation ‘Canadian Medical Education Directions for Specialists’ is no longer in use).

In this article, we did not explicitly consider the recently upcoming field of biomedical data science which is applied in many different areas of medical research such as, for example, individualized medicine, omics research, big data analysis. The tasks and responsibilities of biostatisticians working in this domain are not different from those reported above but in fact include all mentioned aspects [ 26 ].

There is evidently an overlap between the tasks and responsibilities of medical biostatisticians and neighbouring professions. However, all disciplines have different focuses. Important application fields of biostatistics are clinical studies, systematic reviews / meta-analysis, observational and complex interventional studies, and statistical genetics.

In all fields of biostatistical activities, the working environment is diverse and multi-disciplinary. Therefore, it is essential for fruitful, efficient, and high-quality collaborations to clearly define the tasks and responsibilities of the cooperating partners. In summary, the tasks and responsibilities of a biostatistician across all application areas cover active participation in a proper planning, consultation during the entire study duration, data analysis using appropriate statistical methods as well as interpretation and suitable presentation of the results in reports and publications. These tasks are similarly formulated by the ICH E6 guideline concerning good clinical practice [ 8 ].

Availability of data and materials

Not applicable.

Abbreviations

Canadian Medical Education Directions for Specialists

Case report form

Good Clinical Practice

German Association for Medical Informatics, Biometry and Epidemiology

Grading of Recommendations, Assessment, Development and Evaluation

International Biometric Society

International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use

Oxford Centre for Evidence-Based Medicine

Quality Assessment of Diagnostic Accuracy Studies

Statistical analysis plan

Zapf A, Hübner M, Rauch G, Kieser M. What makes a biostatistician? Stat Med. 2018;38(4):695–701.

Article   Google Scholar  

Homepage of the International Biometric Society. http://www.biometricsociety.org/about/definition-of-biometrics/ . Accessed 11 Nov 2019.

Homepage of the German Society of Medical Informatics, Biometry and Epidemiology (GMDS), Section Medical Biometry. http://www.gmds.de/fachbereiche/biometrie/index.php . Accessed 11 Nov 2019.

Homepage of the German Society of Medical Informatics, Biometry and Epidemiology (GMDS), Section Medical Informatics. https://gmds.de/aktivitaeten/medizinische-informatik/ . Accessed 11 Nov 2019.

Homepage from the professional group bioinformatics (FaBI). https://www.bioinformatik.de/en/bioinformatics.html . Accessed 11 Nov 2019.

Homepage of the German Society of Medical Informatics, Biometry and Epidemiology (GMDS), Section Epidemiology. https://gmds.de/aktivitaeten/epidemiologie/ . Access 11 Nov 2019.

Lewis JA. Editorial: statistics and statisticians in the regulation of medicines. J R Stat Soc Ser A. 1996;159(3):359–62.

International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (1996). Guideline for good clinical practice E6 (R2). https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-6-r2-guideline-good-clinical-practice-step-5_en.pdf . Access 11 Nov 2019.

Google Scholar  

Mansmann U, Jensen K, Dirschedl P. Good biometrical practice in medical research - guidelines and recommendations. Informatik, Biometrie und Epidemiologie in Medizin und Biologie. 2004;35:63–71.

International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (1998). Statistical principles for clinical trials E9. https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-9-statistical-principles-clinical-trials-step-5_en.pdf . Accessed 11 Nov 2019.

OCEBM. The Oxford 2011 levels of evidence: Oxford Centre for Evidence-Based Medicine; 2011. http://www.cebm.net/oxford-centre-evidence-based-medicine-levels-evidence-march-2009/ . Accessed 11 Nov 2019

Mulrow CD. Systematic reviews: rationale for systematic reviews. BMJ. 1994;309:597–9.

Article   CAS   Google Scholar  

Gopalakrishnan S, Ganeshkumar P. Systematic reviews and meta-analysis: understanding the best evidence in primary healthcare. J Fam Med Prim Care. 2013;2(1):9–14.

Schünemann H, Brożek J, Guyatt G, Oxman A, editors. GRADE handbook for grading quality of evidence and strength of recommendations. Updated October 2013: The GRADE Working Group; 2013. Available from https://gdt.gradepro.org/app/handbook/handbook.html . Accessed 11 Nov 2019

Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MMG, Sterne JAC, Bossuyt PMM, the QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529–36.

Higgins JPT, Green S, editors. Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011]: The Cochrane Collaboration; 2011. Available from http://handbook.cochrane.org . Accessed 11 Nov 2019

Snijders AB, Bosker RJ. Multilevel analysis - an introduction to basic and advanced multilevel modeling. London: SAGE Publications; 1999.

Hansen BE, Thorogood J, Hermans J, Ploeg RJ, van Bockel JH, van Houwelingen JC. Multistate modelling of liver transplantation data. Stat Med. 1994;13:2517–29.

European Medicines Agency (2012). Concept paper on extrapolation of efficacy and safety in medicine development - EMA/129698/2012. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2013/04/WC500142358.pdf . Accessed 11 Nov 2019.

Wadsworth I, Hampson LV, Jaki T. Extrapolation of efficacy and other data to support the development of new medicines for children: a systematic review of methods. Stat Meth Med Res. 2018;27(2):398–413.

Simon R. Challenges for biometry in 21st century oncology. In: Matsui S, Crowley J, editors. Frontiers of biostatistical methods and applications in clinical oncology. Singapore: Springer; 2017. Available from https://link.springer.com/chapter/10.1007%2F978-981-10-0126-0_1 . Accessed 25 Nov 2019.

Jaki T, Gordon A, Forster P, Bijnens L, Bornkamp B, Brannath W, Fontana R, Gasparini M, Hampson LV, Jacobs T, Jones B, Paoletti X, Posch M, Titman A, Vonk R, Koenig F. A proposal for a new PhD level curriculum on quantitative methods for drug development. Pharm Stat. 2018;17:593–606.

CAS   PubMed   PubMed Central   Google Scholar  

Lewis T. Statisticians in the pharmaceutical industry. In: Stonier PD, editor. Discovering new medicines. Chichester: Wiley; 1994. p. 153–63.

Chuang-Stein C, Bain R, Branson M, Burton C, Hoseyni C, Rockhold FW, Ruberg SJ, Zhang J. Statisticians in the pharmaceutical industry: the 21st century. Stat Biopharm Res. 2010;2(2):145–52.

Royal College of Physicians and Surgeons of Canada. CanMEDS: better standards, better physician, better care. http://www.royalcollege.ca/rcsite/canmeds/canmeds-framework-e . Accessed 11 Nov 2019.

Alarcón-Soto Y, Espasandín-Domínguez J, Guler I, Conde-Amboage M, Gude-Sampedro F, Langohr K, Cadarso-Suárez C, Gómez-Melis G (2019) Data Science in Biomedicine. arXiv:1909.04486v1. Available from https://arxiv.org/abs/1909.04486v1 . Accessed 25 Nov 2019.

Download references

Acknowledgements

There was no funding for this project.

Author information

Authors and affiliations.

Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany

Antonia Zapf

Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, 10117, Berlin, Germany

Geraldine Rauch

Institute of Medical Biometry and Informatics, Heidelberg University Hospital, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany

Meinhard Kieser

You can also search for this author in PubMed   Google Scholar

Contributions

AZ drafted the work and all authors substantively revised it. All authors approved the final version and agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated, resolved, and the resolution documented in the literature.

Corresponding author

Correspondence to Antonia Zapf .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Zapf, A., Rauch, G. & Kieser, M. Why do you need a biostatistician?. BMC Med Res Methodol 20 , 23 (2020). https://doi.org/10.1186/s12874-020-0916-4

Download citation

Received : 21 March 2019

Accepted : 28 January 2020

Published : 05 February 2020

DOI : https://doi.org/10.1186/s12874-020-0916-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Medical research
• Biostatistician
• Responsibilities

BMC Medical Research Methodology

ISSN: 1471-2288

• General enquiries: [email protected]

• Biostatistics and Research Methodology

5. REPORT WRITING AND PRESENTATION OF DATA

Path: pharmd/ pharmd notes/ pharmd fourth year notes/ biostatistics and research methodology / report writing and presentation of data., leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Name  *

Email  *

Add Comment  *

Save my name, email, and website in this browser for the next time I comment.

Post Comment

Trending now

00K Page Views

Biostatistics And Research Methodology

Introduction: Statistics, Biostatistics, Frequency distribution Measures of central tendency: Mean, Median, Mode- Pharmaceutical examples Measures of dispersion: Dispersion, Range, standard deviation, Pharmaceutical problems Correlation: Definition, Karl Pearson’s coefficient of correlation, Multiple correlation - Pharmaceuticals examples

Regression: Curve fitting by the method of least squares, fitting the lines y= a + bx and x = a + by, Multiple regression, standard error of regression - Pharmaceutical Examples Probability: Definition of probability, Binomial distribution, Normal distribution, Poisson’s distribution, properties - problems Sample, Population, large sample, small sample, Null hypothesis, alternative hypothesis, sampling, essence of sampling, types of sampling, Error-I type, Error-II type, Standard error of mean (SEM) - Pharmaceutical examples Parametric test: t-test(Sample, Pooled or Unpaired and Paired) , ANOVA, (One way and Two way), Least Significance difference

Non Parametric tests: Wilcoxon Rank Sum Test, Mann-Whitney U test, Kruskal-Wallis test, Friedman Test Introduction to Research: Need for research, Need for design of Experiments, Experiential Design Technique, plagiarism Graphs: Histogram, Pie Chart, Cubic Graph, response surface plot, Counter Plot graph Designing the methodology: Sample size determination and Power of a study, Report writing and presentation of data, Protocol, Cohorts studies, Observational studies, Experimental studies, Designing clinical trial, various phases.

Blocking and confounding system for Two-level factorials Regression modeling: Hypothesis testing in Simple and Multiple regressionmodels Introduction to Practical components of Industrial and Clinical Trials Problems: Statistical Analysis Using Excel, SPSS, MINITAB®, DESIGN OF EXPERIMENTS, R - Online Statistical Software’s to Industrial and Clinical trial approach

Design and Analysis of experiments: Factorial Design: Definition, 2², 2³ design. Advantage of factorial design Response Surface methodology: Central composite design, Historical design, Optimization Techniques

Popular Pages

Meet Our New PhD Students!

We’ll be featuring mini-profiles of our new PhD students over the next few weeks. We look forward to welcoming them into our community!

Hi, my name is Hannah Jin, and I was born and raised in the Greater Philadelphia area. I just finished my undergraduate degree at the University of Pennsylvania (Penn). There, I was a Math major and Neuroscience minor.

As an undergraduate, I applied statistics to evaluate how social and environmental factors relate to clinical outcomes in frontotemporal degeneration (FTD), a neurodegenerative disease.

At the Penn FTD Center, I performed statistical analysis to explore how occupation, job-related environmental exposures, and genetics relate to cognitive reserve, disease progression, and survival in patients with FTD. Additionally, I applied statistics to study racial disparities in clinical presentation among individuals diagnosed with FTD, with the hope that this research will promote health equity by sparking further investigation into the social determinants underlying these disparities. I hope to continue contributing to public health through researching social and environmental determinants of health.

I also did causal inference research at the Penn Statistics and Data Science department in my junior and senior years. In one project, I researched how various types of childhood sports participation affect cognitive and emotional health in adolescents. In a methodological project, I worked on developing a novel statistical test for assessing whether covariate balance among matched quadruples has been achieved, which allows for a matched quadruple design for studying interaction effects.

I hope to contribute to biostatistical methods and public health research in causal inference, environmental health, and neurology at HSPH. Additionally, I am excited to explore other subfields of biostatistics.

Outside of school and research, I have been singing in choirs and a cappella groups for the past fourteen years, so I would love to join a choir at Harvard. I like painting and drawing, so I am excited to wander around art museums in the Boston area. I also like exercising, dancing, and singing karaoke. I am very excited to meet everyone!

Hi, my name is Ethan Lee. I’m from the Philadelphia area, but went to boarding school in New England, so I am excited to be back. I graduated from NYU in 2024 with a major in Mathematics and a minor in Computer Science.

My experience with health sciences began in high school, when I spent two summers working at a microbiology lab at the University of Pennsylvania School of Medicine. During my undergrad, I collaborated closely with a group from the Department of Population Health at NYU medical school. We primarily focused on the topic of domain generalization/domain adaptation, which arises during a prediction task when the training set is not an unbiased sample, that is it may only include data from certain prevalent domains in a population. I felt that this issue itself is highly relevant in today’s society, where minority groups are often underrepresented during sampling.

Our approach utilized a selection-guided approach, meaning we leveraged the fact each individual’s domain selection probability depended on their individual covariates. We eventually expanded this approach to more general contexts, including a semi- supervised approach which utilized unlabeled training data.

During my PhD studies at Harvard, I am keen on exploring more of the potential methods to analyze large datasets, such as EHR. Moreover, I am also interested in the aspects of developing statistics/machine learning methods, such as reinforcement learning, to analyze complex, high-dimensional data collected from mobile and wearable devices.

In my free time, I enjoy baseball statistics and am interested in utilizing statistical methods for areas such as in-season prediction and player evaluation metrics. I also enjoy tennis and squash.

News from the School

Red meat and diabetes

How for-profit medicine is harming health care

A tradition of mentoring

Promising HIV treatment

Warning: The NCBI web site requires JavaScript to function. more...

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Understanding biostatistics interpretation.

Elizabeth Cash ; Sameh W. Boktor .

Affiliations

Last Update: March 13, 2023 .

• Introduction

A basic understanding of statistical concepts is necessary to effectively evaluate existing literature. Statistical results do not, however, allow one to determine the clinical applicability of published findings. Statistical results can be used to make inferences about the probability of an event among a given population. Careful interpretation by the clinician is required to determine the value of the data as it applies to an individual patient or group of patients. [1]

Good research studies will provide a clear, testable hypothesis, or prediction, about what they expect to find in the relationships being tested. [2] The hypothesis will be grounded in the empirical literature, based on clinical observations or expertise, and should be innovative in its tests of a novel relationship or confirmation of a prior study. There are at minimum two hypotheses in any study: (1) the null hypothesis assumes there is no difference or that there is no effect, and (2) the experimental or alternative hypothesis predicts an event or outcome will occur. Often the null hypothesis is not stated or is assumed. Hypotheses are tested by examining relationships between independent variables, or those thought to have some effect, and dependent variables, or those thought to be moved or affected by the independent variable. These also are called predictor and outcome variables, respectively.

Statistics are used to test a study’s alternative or experimental hypothesis. Statistical models are fitted based on the nature, type, and other characteristics of the dataset. Data typically involves levels of measurement, and these determine the type of statistical models that can be applied to test a hypothesis. [3] Nominal data are those variables containing two or more categories without underlying order or value. Examples of nominal data include indicators of group membership, such as male or female. Ordinal data is nominal data that includes an order or rank but has undefined spacing between groups or levels, such as faculty ranking, or educational level. Interval data is ordinal data with clearly defined spacing between the intervals and no absolute zero points. An example of interval data is the temperature scale, as the magnitude of the difference between intervals is consistent and measurable (one degree). Ratio data are interval data that include an absolute zero such as the amount of student loan debt. Nominal and ordinal data are categorical, where entities are divided into distinct groups, whereas, interval and ratio data are considered continuous such that each observation gets a distinct score. [4]

It is up to the researcher to appropriately apply statistical models when testing hypotheses. Several approaches can be used to analyze the same dataset, and how this is accomplished depends heavily on the nature of the wording in a researcher’s hypothesis. [5] There exist a variety of statistical software packages, some available for free while others charge annual license fees, that can be used to analyze data. Nearly all packages require the user to have a basic understanding of the types of data and appropriate application of statistical models for each type. More sophisticated packages require the user to use the program’s proprietary coding language to perform hypothesis tests. These can require a good amount of time to learn, and errors can easily slip past the untrained eye.

It is strongly recommended that unfamiliar users consult with a statistical analyst when designing and running statistical models. Biostatistician consultations can occur at any time during a study, but earlier consultations are wise to prevent the introduction of accidental bias into study data and to help ensure accuracy and collection methods that will be adequate to allow for tests of hypotheses.

• Issues of Concern

Statistical Significance

If the probability of obtaining a test statistic value by chance (p-value) is less than .05, then the experimental hypothesis is accepted as true. Another way of to think about p-values is the probability that the null hypothesis is true, which for a cutoff of p is less than .05 would mean there is a less than 5% chance that the difference observed is not a true difference. [4] However, when interpreting statistical results, the p-value alone is not enough. [6] Significant does not always equate to important. Very small, potentially unimportant effects can turn out to be statistically significant. [7]

• Clinical Significance

To evaluate the clinical relevance or importance of a significant result, one must be certain to consider the size of the effect. [8] Effect measures are standardized to allow application across different scales of measurement. [9] The following are some of the more common ways effect sizes can be estimated:

• Conducting a review of the literature and examining reported results,
• Conducting pilot studies to get an indication of effects that might be seen in larger studies,
• Making educated guesses based on what is clinically or practically meaningful and informed by experience
• Using conventional recommendations for effect size measures

One common measure of effect is the correlation coefficient, r. In general, small effects, or r=.10, indicate that the effect explains 1% of the total variance. Likewise, r=.30 is considered a medium effect, and r=.50 is considered large, explaining 25% of the variance and holding greater clinical relevance. The square of a correlational r-value indicates the proportion of variance explained by the relationship tested. Similarly, confidence intervals offer a way to determine the clinical strength or magnitude of observed effects. [10] A 95% confidence interval indicates a range of plausible values around another parameter (e.g., mean or odds ratio) where there is a 95% chance that the data within that interval truly captures the value observed in the population being studied. [4] Confidence intervals also provide information about accuracy, as smaller intervals suggest greater precision; whereas, larger intervals may suggest a high level of variability. It has been recommended that, at a minimum, studies should report estimates of effect and confidence intervals to allow for appropriate interpretation of their results. [9]

It is also important to note that although a study may be designed and statistically tested in a way that suggests inference and causation could be concluded (e.g., longitudinal observations of change over time), only studies that employ a randomized and/or controlled design will permit causative declarations to be made from their results. [11]

• Enhancing Healthcare Team Outcomes

Statistical analysis is essential for any clinical research. Of greater importance is to understand the clinical significance of reported results and to determine whether those results can be extrapolated to the general population. Understanding the definitions and methods described above should help in better understanding and usability for medical professionals and students. 

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Elizabeth Cash declares no relevant financial relationships with ineligible companies.

Disclosure: Sameh Boktor declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Cash E, Boktor SW. Understanding Biostatistics Interpretation. [Updated 2023 Mar 13]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

In this Page

Bulk download.

• Bulk download StatPearls data from FTP

Related information

• PMC PubMed Central citations
• PubMed Links to PubMed

Similar articles in PubMed

• Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. [Cochrane Database Syst Rev. 2022] Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, et al. Cochrane Database Syst Rev. 2022 Feb 1; 2(2022). Epub 2022 Feb 1.
• Unadjusted Bivariate Two-Group Comparisons: When Simpler is Better. [Anesth Analg. 2018] Unadjusted Bivariate Two-Group Comparisons: When Simpler is Better. Vetter TR, Mascha EJ. Anesth Analg. 2018 Jan; 126(1):338-342.
• Statistical Significance. [StatPearls. 2024] Statistical Significance. Tenny S, Abdelgawad I. StatPearls. 2024 Jan
• Review Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification. [Brain Neurotrauma: Molecular, ...] Review Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification. Wolahan SM, Hirt D, Glenn TC. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. 2015
• Review Refinement of the HCUP Quality Indicators [ 2001] Review Refinement of the HCUP Quality Indicators Davies SM, Geppert J, McClellan M, McDonald KM, Romano PS, Shojania KG. 2001 May

Recent Activity

• Understanding Biostatistics Interpretation - StatPearls Understanding Biostatistics Interpretation - StatPearls

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers