Biostatistical Analysis Methods Used in the Development of Biotechnological Vaccines and One Minitab Practise
Öz
Objective: This study aimed to introduce the biostatistical analysis methods used to develop biotechnological vaccines. A Minitab application was conducted for an example case study using one of the techniques introduced.
Material and Method: In this study, the purposes of utilization of biostatistics were introduced. Biostatistics techniques used in vaccine development were explained theoretically. Analysis methods were detailed under the titles of binomial and stochastic transmission models and the evaluation of the protective effects of the developed vaccine. The statistical formulas of the techniques, and the theoretical background which was explained in detail, and the explanations of the formulas were specified. Also, the number of samples required for the vaccine trial was calculated in the Minitab package program by giving an example scenario.
Results: The sample size was calculated in the Minitab package program according to the standard deviation, error tolerance, power, and expected average protection rate. The sample size was calculated as 18 for a protection rate of 80%, 30 for a protection rate of 90%, and 58 for a protection rate of 99%.
Conclusion: It has been observed that different statistical techniques are needed for different stages of the vaccine development process. Statistical methods used in the vaccine development process range from analyzing a small sample to randomly conducted field trials. For this reason, statistics has become an indispensable component of the vaccine development process by using modern technology. Binomial and stochastic transmission models, deterministic differential equation models, and statistical analyzes in which the protective effects of the developed vaccines were evaluated are commonly used among these methods.
Anahtar Kelimeler
Kaynakça
- mikrobiyoloji.org [internet]. Viral Aşılar; 2022 [cited 12 Nisan 2022] Available from: http://www.mikrobiyoloji.org/TR/Genel/BelgeGoster.aspx?F6E10F8892433CFFA79D6F5E6C1B43FF4B5119729FFAA2F9.
- Gebre MS, Brito LA, Tostanoski LH, Edwards DK, Carfi A, Barouch DH. Novel approaches for vaccine development. Cell. 2021 March 18; 184(6): 1589-1603. Available from: https://doi.org/10.1016/j.cell.2021.02.030.
- Meuleman TJ, Cowton VM, Patel AH, Liskamp RMJ. Design and synthesis of hcv-e2 glycoprotein epitope mimics in molecular construction of potential synthetic vaccines. Viruses. 2021; 13(2): 326. Available from: https://doi.org/10.3390/v13020326
- Moyle PM. Biotechnology approaches to produce potent, self-adjuvantting antigen-adjuvantt fusion protein subunit vaccines. Biotechnology Advances. 2017 May-June; 35: 375-389.
- Ho W, Gao M, Li F, Li Z, Zhang XQ, Xu X. Next‐generation vaccines: nanoparticle‐mediated dna and mrna delivery. Advanced Healthcare Materials. 2021 January 18; 10(8): 1-17. Available from: https://doi. org/10.1002/adhm.202001812
- Ho NI, Huis in 't Veld LGM, Raaijmakers TK, Adema GJ. Adjuvants enhancing cross-presentation by dendritic cells: the key to more effective vaccines?. Frontiers in Immunology. 2018 December 13; 9: 1-12.
- Pearson K. Antityphoid inoculation. British Medical Journal. 1904 December 17; 2: 1667–1668.
- Greenwood M, UG Yule. The statistics of anti-typhoid and anti-cholera inoculations, and the interpretation of such statistics in general. Proc R Soc Med, 1915 June 4; 8(part 2): 113–194.
- Kendrick P, Eldering G. A study in active immunization against pertussis. with statistical analyses of the data by aj borowski. American Journal of Hygiene. 1939; 29: 133-53.
- Halloran ME, Longini IM, Struchiner CJ, Longini IM. Design and analysis of vaccine studies. New York: Springer; 2010. 103-129p.
- Adak D, Majumder A, Bairagi N. Mathematical perspective of Covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models. Chaos, Solitons & Fractals. 2020 October 19; 142: 1-11.
- Longini Jr, Ira M. Chain binomial model. Encyclopedia of Biostatistics. Wiley Online Library Encyclopedia of Biostatistics. 2005 July 5. Available from: https://doi.org/10.1002/0470011815.b2a07008
- Kyvsgaard NC, Vang Johansen M, Carabin H. Simulating transmission and control of taenia solium infections using a reed-frost stochastic model. International Journal for Parasitology. 2006 November 24; 37(5): 547-558. Available from: 10.1016/j.ijpara.2006.11.018
- Greenwood M. The statistical study of ınfectious diseases. Journal of the Royal Statistical Society. 1946; 109(2): 85–110.
- Riley MC, Clare M, King RD. Locational distribution of gene functional classes in arabidopsis thaliana. BMC Bioinformatics. 2007 March 30; 8(112): 1-12.
- Ulutürk Akman S. Biyoistatistik [Internet]. İstanbul Üniversitesi Açık ve Uzaktan Eğitim Fakültesi [Cited 15 February 2022]. Available from: http://auzefkitap.istanbul.edu.tr/kitap/kok/biyo istatistik.pdf
- Akpınar H. Bulaşıcı hastalıkların yayılımının tahmininde deterministik modellerin kullanılması. Öneri Dergisi. 2012 Temmuz; 10(38): 97-103.
- By Minitab Inc. Minitab User Guide 2: Data Analysis and Quality Tools Release 13 for Windows, Windows 95, Windows 98, and Windows NT 2000. ISBN 0-925636-44-4; 2000. Chapter 9 (1-13p.)
- uroturk.org.tr [Internet]. [cited 2022 September 10] Available from: https://uroturk.org.tr/ urolojiData/Uploads/files/gelecek-akademisyen-1210/Sunum-9.pdf
- McNulty CAM, Bowen JK, Williams AJ. Hepatitis B vaccination in predialysis chronic renal failure patients a comparison of two vaccination schedules. Vaccine. 2005 July 14; 23(32): 4142-4147.
- Alhindi Y, Albarakati R, Almatrafi L, Fatta G, Fatani B. Report on the Investigating Factors Associated with Vaccine Hesitancy in Makkah [Internet]. KSA [cited 2022 1 April]. 1-8 p. Available from: https://drive. uqu.edu.sa/up/profile/Books/4290019/book_path_SqaGX9FFlr.pdf
Biyoteknolojik Aşıların Geliştirilmesinde Kullanılan Biyoistatistik Analiz Yöntemleri ve Bir Minitab Uygulaması
Öz
Amaç: Bu çalışmada, biyoteknolojik aşı geliştirmek için kullanılan biyoistatistik analiz yöntemlerinin tanıtılması amaçlanmıştır. Tanıtılan tekniklerden örneklem büyüklüğü belirleme yöntemi kullanılarak örnek olay üzerinden Minitab uygulaması yapılmıştır.
Gereç ve Yöntem: Araştırma kapsamında öncelikli olarak biyoistatistiğin kullanım amaçları tanıtılmıştır. Aşı geliştirmede kullanılan biyoistatistik teknikler teorik olarak anlatılmıştır. Binom ve stokastik aktarım modelleri ve geliştirilen aşının koruyucu etkilerinin değerlendirilmesi başlıkları dâhilinde analiz yöntemleri detaylandırılmıştır. Teorik altyapısı detaylı biçimde anlatılan tekniklerin istatistik formülleri ve formüllerin açıklamaları belirtilmiştir. Örnek bir senaryo verilerek Minitab paket programında aşı denemesi için gereken örneklem sayısı hesaplanmıştır.
Bulgular: Standart sapma, hata payı, güç ve beklenen ortalama koruyuculuk oranına göre Minitab paket programında örneklem sayısı hesaplanmıştır. %80 koruyuculuk oranı için örneklem sayısı 18, %90 koruyuculuk oranı için örneklem sayısı 30 ve %99 koruyuculuk oranı için örneklem sayısı 58 olarak hesaplanmıştır.
Sonuç: Aşı geliştirme sürecinin farklı aşamaları için farklı istatistik tekniklere ihtiyaç duyulduğu görülmüştür. Aşı geliştirme sürecinde başvurulan istatistik yöntemler küçük bir örneklemin analiz edilmesinden random olarak gerçekleştirilen saha denemelerine kadar çeşitlenmektedir. Bu nedenle İstatistik bilimi modern teknoloji kullanılarak aşı geliştirme sürecinin vazgeçilmez bir unsuru haline gelmiştir. Bu yöntemlerin başlıcalarının; binom ve stokastik aktarım modelleri, deterministik diferansiyel denklem modelleri ve geliştirilen aşıların koruyucu etkilerinin değerlendirildiği istatistik analizler olduğu görülmüştür.
Anahtar Kelimeler
Kaynakça
- mikrobiyoloji.org [internet]. Viral Aşılar; 2022 [cited 12 Nisan 2022] Available from: http://www.mikrobiyoloji.org/TR/Genel/BelgeGoster.aspx?F6E10F8892433CFFA79D6F5E6C1B43FF4B5119729FFAA2F9.
- Gebre MS, Brito LA, Tostanoski LH, Edwards DK, Carfi A, Barouch DH. Novel approaches for vaccine development. Cell. 2021 March 18; 184(6): 1589-1603. Available from: https://doi.org/10.1016/j.cell.2021.02.030.
- Meuleman TJ, Cowton VM, Patel AH, Liskamp RMJ. Design and synthesis of hcv-e2 glycoprotein epitope mimics in molecular construction of potential synthetic vaccines. Viruses. 2021; 13(2): 326. Available from: https://doi.org/10.3390/v13020326
- Moyle PM. Biotechnology approaches to produce potent, self-adjuvantting antigen-adjuvantt fusion protein subunit vaccines. Biotechnology Advances. 2017 May-June; 35: 375-389.
- Ho W, Gao M, Li F, Li Z, Zhang XQ, Xu X. Next‐generation vaccines: nanoparticle‐mediated dna and mrna delivery. Advanced Healthcare Materials. 2021 January 18; 10(8): 1-17. Available from: https://doi. org/10.1002/adhm.202001812
- Ho NI, Huis in 't Veld LGM, Raaijmakers TK, Adema GJ. Adjuvants enhancing cross-presentation by dendritic cells: the key to more effective vaccines?. Frontiers in Immunology. 2018 December 13; 9: 1-12.
- Pearson K. Antityphoid inoculation. British Medical Journal. 1904 December 17; 2: 1667–1668.
- Greenwood M, UG Yule. The statistics of anti-typhoid and anti-cholera inoculations, and the interpretation of such statistics in general. Proc R Soc Med, 1915 June 4; 8(part 2): 113–194.
- Kendrick P, Eldering G. A study in active immunization against pertussis. with statistical analyses of the data by aj borowski. American Journal of Hygiene. 1939; 29: 133-53.
- Halloran ME, Longini IM, Struchiner CJ, Longini IM. Design and analysis of vaccine studies. New York: Springer; 2010. 103-129p.
- Adak D, Majumder A, Bairagi N. Mathematical perspective of Covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models. Chaos, Solitons & Fractals. 2020 October 19; 142: 1-11.
- Longini Jr, Ira M. Chain binomial model. Encyclopedia of Biostatistics. Wiley Online Library Encyclopedia of Biostatistics. 2005 July 5. Available from: https://doi.org/10.1002/0470011815.b2a07008
- Kyvsgaard NC, Vang Johansen M, Carabin H. Simulating transmission and control of taenia solium infections using a reed-frost stochastic model. International Journal for Parasitology. 2006 November 24; 37(5): 547-558. Available from: 10.1016/j.ijpara.2006.11.018
- Greenwood M. The statistical study of ınfectious diseases. Journal of the Royal Statistical Society. 1946; 109(2): 85–110.
- Riley MC, Clare M, King RD. Locational distribution of gene functional classes in arabidopsis thaliana. BMC Bioinformatics. 2007 March 30; 8(112): 1-12.
- Ulutürk Akman S. Biyoistatistik [Internet]. İstanbul Üniversitesi Açık ve Uzaktan Eğitim Fakültesi [Cited 15 February 2022]. Available from: http://auzefkitap.istanbul.edu.tr/kitap/kok/biyo istatistik.pdf
- Akpınar H. Bulaşıcı hastalıkların yayılımının tahmininde deterministik modellerin kullanılması. Öneri Dergisi. 2012 Temmuz; 10(38): 97-103.
- By Minitab Inc. Minitab User Guide 2: Data Analysis and Quality Tools Release 13 for Windows, Windows 95, Windows 98, and Windows NT 2000. ISBN 0-925636-44-4; 2000. Chapter 9 (1-13p.)
- uroturk.org.tr [Internet]. [cited 2022 September 10] Available from: https://uroturk.org.tr/ urolojiData/Uploads/files/gelecek-akademisyen-1210/Sunum-9.pdf
- McNulty CAM, Bowen JK, Williams AJ. Hepatitis B vaccination in predialysis chronic renal failure patients a comparison of two vaccination schedules. Vaccine. 2005 July 14; 23(32): 4142-4147.
- Alhindi Y, Albarakati R, Almatrafi L, Fatta G, Fatani B. Report on the Investigating Factors Associated with Vaccine Hesitancy in Makkah [Internet]. KSA [cited 2022 1 April]. 1-8 p. Available from: https://drive. uqu.edu.sa/up/profile/Books/4290019/book_path_SqaGX9FFlr.pdf
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Erken Görünüm Tarihi | 29 Eylül 2022 |
| Yayımlanma Tarihi | 30 Eylül 2022 |
| Gönderilme Tarihi | 28 Nisan 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 7 Sayı: 3 |
Kaynak Göster
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