Abstract
BackgroundResearch in the predictors of all-cause mortality in HIV-infected people has widely been reported in literature. Making an informed decision requires understanding the methods used.ObjectivesWe present a review on study designs, statistical methods and their appropriateness in original articles reporting on predictors of all-cause mortality in HIV-infected people between January 2002 and December 2011. Statistical methods were compared between 2002–2006 and 2007–2011. Time-to-event analysis techniques were considered appropriate.Data SourcesPubmed/Medline.Study Eligibility CriteriaOriginal English-language articles were abstracted. Letters to the editor, editorials, reviews, systematic reviews, meta-analysis, case reports and any other ineligible articles were excluded.ResultsA total of 189 studies were identified (n = 91 in 2002–2006 and n = 98 in 2007–2011) out of which 130 (69%) were prospective and 56 (30%) were retrospective. One hundred and eighty-two (96%) studies described their sample using descriptive statistics while 32 (17%) made comparisons using t-tests. Kaplan-Meier methods for time-to-event analysis were commonly used in the earlier period (n = 69, 76% vs. n = 53, 54%, p = 0.002). Predictors of mortality in the two periods were commonly determined using Cox regression analysis (n = 67, 75% vs. n = 63, 64%, p = 0.12). Only 7 (4%) used advanced survival analysis methods of Cox regression analysis with frailty in which 6 (3%) were used in the later period. Thirty-two (17%) used logistic regression while 8 (4%) used other methods. There were significantly more articles from the first period using appropriate methods compared to the second (n = 80, 88% vs. n = 69, 70%, p-value = 0.003).ConclusionDescriptive statistics and survival analysis techniques remain the most common methods of analysis in publications on predictors of all-cause mortality in HIV-infected cohorts while prospective research designs are favoured. Sophisticated techniques of time-dependent Cox regression and Cox regression with frailty are scarce. This motivates for more training in the use of advanced time-to-event methods.
Highlights
Appropriate utilization of biostatistical methods is becoming increasingly important in biomedical research
Several papers addressing study design issues and statistical analysis approaches in different clinical fields have been published underpinning the importance of robustness in methodology [1,2,3,4,5,6,7,8]
Selecting the appropriate study design and relevant statistical analysis technique is largely dependent on the complexity of the study and its objectives
Summary
Appropriate utilization of biostatistical methods is becoming increasingly important in biomedical research. Several papers addressing study design issues and statistical analysis approaches in different clinical fields have been published underpinning the importance of robustness in methodology [1,2,3,4,5,6,7,8]. There is consensus that inappropriate study designs and statistical methodology lead to incorrect results, poor interpretation of study findings and wrong conclusions. An array of study designs and appropriate statistical techniques with varying levels of complexity exists. Selecting the appropriate study design and relevant statistical analysis technique is largely dependent on the complexity of the study and its objectives.
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