Pub Date : 2021-04-30DOI: 10.26420/AUSTINBIOMANDBIOSTAT.2021.1038
S. El, Reyes Jc, P. Cm, Mattei H, Lopez-Cepero A
Aims: To describe and project the number of COVID-19 cases and deaths reported in Puerto Rico, according to age and sex. Methods: We used surveillance data from March 8, 2020 to March 13, 2021 to describe and predict, by age and sex, the number of cases and deaths in Puerto Rico using Generalized Additive Models. The statistical modeling was performed in R software using the mgcv package. Results: The analytic sample consisted of 95,208 confirmed cases and 2,080 deaths reported by the Puerto Rico Department of Health until the second week of March 2021. The risk of COVID-19 infection was highest among adults aged 20-59 years, as compared with those younger than 20 years (RR20-39 vs. <20: 2.35 [95% CI: 1.80-3.06] and (RR20-59 vs. <20: 2.30 [95% CI: 1.76-3.00]). However, the pattern in the risk of death showed an inverse relationship: the highest risk of death occurred in adults 60 years and over as compared with those younger than 60 years (RR≥80 vs. <60: 22.4 [95% CI: 18.9-26.5] and (RR60-79 vs. <60: 6.7 [95% CI: 5.6-7.9]). Although there were no significant differences in the risk of infection (p>0.1) by sex, males had a 70% (95% CI: 50-100%) greater risk of death than their female counterparts. The projected weekly number of confirmed cases of COVID-19 showed a downward trend; we expected approximately 510 confirmed cases of COVID-19 in the week ending March 27, 2021. Similarly, the projected weekly number of COVID-19 deaths showed a downward trend. Conclusion: Future studies are needed to understand age and sex differences in COVID-19 infections and deaths. Increments in the number of COVID-19 cases in the short term are of great concern to justify more substantial preventive restrictions.
{"title":"Modeling the Number of COVID-19 Confirmed Cases and Deaths in Puerto Rico: One-year Experience","authors":"S. El, Reyes Jc, P. Cm, Mattei H, Lopez-Cepero A","doi":"10.26420/AUSTINBIOMANDBIOSTAT.2021.1038","DOIUrl":"https://doi.org/10.26420/AUSTINBIOMANDBIOSTAT.2021.1038","url":null,"abstract":"Aims: To describe and project the number of COVID-19 cases and deaths reported in Puerto Rico, according to age and sex. Methods: We used surveillance data from March 8, 2020 to March 13, 2021 to describe and predict, by age and sex, the number of cases and deaths in Puerto Rico using Generalized Additive Models. The statistical modeling was performed in R software using the mgcv package. Results: The analytic sample consisted of 95,208 confirmed cases and 2,080 deaths reported by the Puerto Rico Department of Health until the second week of March 2021. The risk of COVID-19 infection was highest among adults aged 20-59 years, as compared with those younger than 20 years (RR20-39 vs. <20: 2.35 [95% CI: 1.80-3.06] and (RR20-59 vs. <20: 2.30 [95% CI: 1.76-3.00]). However, the pattern in the risk of death showed an inverse relationship: the highest risk of death occurred in adults 60 years and over as compared with those younger than 60 years (RR≥80 vs. <60: 22.4 [95% CI: 18.9-26.5] and (RR60-79 vs. <60: 6.7 [95% CI: 5.6-7.9]). Although there were no significant differences in the risk of infection (p>0.1) by sex, males had a 70% (95% CI: 50-100%) greater risk of death than their female counterparts. The projected weekly number of confirmed cases of COVID-19 showed a downward trend; we expected approximately 510 confirmed cases of COVID-19 in the week ending March 27, 2021. Similarly, the projected weekly number of COVID-19 deaths showed a downward trend. Conclusion: Future studies are needed to understand age and sex differences in COVID-19 infections and deaths. Increments in the number of COVID-19 cases in the short term are of great concern to justify more substantial preventive restrictions.","PeriodicalId":91208,"journal":{"name":"Austin biometrics and biostatistics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49570218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-03DOI: 10.26420/AUSTINBIOMANDBIOSTAT.2021.1037
S. Hong, Ying Sun, H. Li, Lynn Hs
Random forest has proven to be a successful machine learning method, but it also can be time-consuming for handling large datasets, especially for doing iterative tasks. Machine learning iterative imputation methods have been well accepted by researchers for imputing missing data, but such methods can be more time-consuming than standard imputation methods. To overcome this drawback, different parallel computing strategies have been proposed but their impact on imputation results and subsequent statistical analyses are relatively unknown. Newly proposed random forest implementations, such as ranger and randomForestSRC, have provided alternatives for easier parallelization, but their validity for doing iterative imputation are still unclear. Using random-forest imputation algorithm missForest as an example, this study examines two parallelized methods using newly proposed random forest implementations in comparison with the two parallel strategies (variable-wise distributed computation and model-wise distributed computation) using language-level parallelization from the software package. Results from the simulation experiments showed that the parallel strategies could influence both the imputation process and the final imputation results differently. Different parallel strategies can improve computational speed to a variable extent, and based on simulations, ranger can provide performance boost for datasets of different sizes with reasonable accuracy. Specifically, even though different strategies can produce similar normalized root mean squared prediction errors, the variable-wise distributed strategy led to additional biases when estimating the mean and inter-correlation of the covariates and their regression coefficients. And parallelization by randomForestSRC can lead to changes in both prediction errors and estimates.
{"title":"A Note on the Required Sample Size of Model-Based Dose-Finding Methods for Molecularly Targeted Agents","authors":"S. Hong, Ying Sun, H. Li, Lynn Hs","doi":"10.26420/AUSTINBIOMANDBIOSTAT.2021.1037","DOIUrl":"https://doi.org/10.26420/AUSTINBIOMANDBIOSTAT.2021.1037","url":null,"abstract":"Random forest has proven to be a successful machine learning method, but it also can be time-consuming for handling large datasets, especially for doing iterative tasks. Machine learning iterative imputation methods have been well accepted by researchers for imputing missing data, but such methods can be more time-consuming than standard imputation methods. To overcome this drawback, different parallel computing strategies have been proposed but their impact on imputation results and subsequent statistical analyses are relatively unknown. Newly proposed random forest implementations, such as ranger and randomForestSRC, have provided alternatives for easier parallelization, but their validity for doing iterative imputation are still unclear. Using random-forest imputation algorithm missForest as an example, this study examines two parallelized methods using newly proposed random forest implementations in comparison with the two parallel strategies (variable-wise distributed computation and model-wise distributed computation) using language-level parallelization from the software package. Results from the simulation experiments showed that the parallel strategies could influence both the imputation process and the final imputation results differently. Different parallel strategies can improve computational speed to a variable extent, and based on simulations, ranger can provide performance boost for datasets of different sizes with reasonable accuracy. Specifically, even though different strategies can produce similar normalized root mean squared prediction errors, the variable-wise distributed strategy led to additional biases when estimating the mean and inter-correlation of the covariates and their regression coefficients. And parallelization by randomForestSRC can lead to changes in both prediction errors and estimates.","PeriodicalId":91208,"journal":{"name":"Austin biometrics and biostatistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42619209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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