Article Sidebar
Published:
Aug 1, 2022DOI:
10.4314/ahs.v22i2.34Keywords:
Article Details
While African Health Sciences has been freely accessible online there have been questions on whether it is Open Access or not. We wish to clearly state that indeed African Health Sciences is Open Access. There are key issues regarding Open Access needing clarification for avoidance of doubt:
- 1. Henceforth, papers in African Health Sciences will be published under the CC BY (Creative Commons Attribution License) 4.0 International. See details on https://creativecomons.org/)
- 2. The copyright owners or the authors grant the 3rd party (perpetually and in advance) the right to disseminate, reproduce, or use the research papers in part or in full, format/medium as long as:
- No substantive errors are introduced in the process
- Attribution of authorship and correct citation details are given
- The referencing details are not changed.
Should the papers be reproduced in part, this must be clearly stated.
- 3. The papers will be freely and universally accessible online in an easily readable format such as XML in at least one widely recognized open access repository such as PUBMED CENTRAL.
B. ABRIDGED LICENCE AGREEMENT BETWEEN AUTHORS AND African Health Sciences
I submitted my manuscript to African Health Sciences and would like to affirm that:
1.0 I am authorized by my co-authors to enter into these arrangements.
2.0 I guarantee, on behalf of self and co-authors:
- That the paper is original, and has not been published in any other peer-reviewed journal; nor is it under consideration by other journal (s). It does not infringe existing copyright or any other person’s rights
- That we are/I am the sole author(s) of the paper and with authority to enter into this agreement. My granting rights to African Health Sciences is not in breach of any other obligation
- That the paper contains nothing unlawful, or libelous. Nor anything that would constitute a breach of contract, confidence or commitment given to secrecy, if published
- That I/we have taken care to ensure the integrity of the article.
3.0 I and all co-authors, agree that the paper, if accepted for publication, shall be licensed under the Creative Commons Attribution License 4.0. (see https://creativecommons.org/)
Main Article Content
Pilot study of new statistical models for prognostic factors in short term survival of oral cancer
Abstract
Background: Survival times of oral cancer are poorly documented in Nigeria. This is partly due to poor documentation and limited investigations to elicit sufficient prognostic factors. In this study, we applied a new statistical model for survival times of oral cancer patients considering limited prognostic factors.
Methods: A total of 29 cases of Oral cancer patients with stage I to IV invasive primary oral cancer treated at the University of Port Harcourt, Nigeria between 2008 and 2015 were used to generate prognostic models. Profiled prognostic factors include age, stage of tumor development, habitus, and treatment modalities. The baseline statistical distributions considered were Exponential, Weibull, Lognormal and Log-logistic distributions. The Chi-Square test was considered for the suitability of the model chosen. A comparison of the model performance was done using the Akaike Information Criterion (AIC).
Results: Seventeen (58.6%) of the oral cancer patients were males and 12(41.4%) were females within the age range of 19 and 73 years. Sixteen (55.2%) of the patients were censored while 13(44.8%) were not censored. The estimated median survival time (MST) for the males was 29.50 months while that of the female was 7 months. Four parametric statistical models were tested and all identified tumor stage [cTNM stage (p= 0.000)] and treatment modality (p= 0.000) as more important predictors of survival. The models were then compared, using the Akaike Information Criterion (AIC) to determine the model best fit for the data. The
model with the lowest AIC and so considered the best was the Weibull Statistical Model (WSM) with AIC= 100.76.
Conclusions: This study suggests that the Weibull survival model is the best fit for estimating oral cancer survival times especially where only limited prognostic factors are available. Larger studies are required to validate the findings of this pilot.
Keywords: New statistical; short term survival; oral cancer.
