Browsing by Subject "Survival analysis"

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    Open Access
    A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data
    (2017) Nasejje, Justine B; Mwambi, Henry; Sabur, Natasha F; Lesosky, Maia
    Abstract Background Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. Methods In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). Results The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Conclusion Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.
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    Open Access
    Adjusting mortality for loss to follow-up: analysis of five ART programmes in sub-Saharan Africa
    (Public Library of Science, 2010) Brinkhof, Martin W G; Spycher, Ben D; Yiannoutsos, Constantin; Weigel, Ralf; Wood, Robin; Messou, Eugène; Boulle, Andrew; Egger, Matthias; Sterne, Jonathan A C; (IeDEA), for the International epidemiological Database to Evaluate AIDS
    BACKGROUND: Evaluation of antiretroviral treatment (ART) programmes in sub-Saharan Africa is difficult because many patients are lost to follow-up. Outcomes in these patients are generally unknown but studies tracing patients have shown mortality to be high. We adjusted programme-level mortality in the first year of antiretroviral treatment (ART) for excess mortality in patients lost to follow-up. Methods and FINDINGS: Treatment-naïve patients starting combination ART in five programmes in Côte d'Ivoire, Kenya, Malawi and South Africa were eligible. Patients whose last visit was at least nine months before the closure of the database were considered lost to follow-up. We filled missing survival times in these patients by multiple imputation, using estimates of mortality from studies that traced patients lost to follow-up. Data were analyzed using Weibull models, adjusting for age, sex, ART regimen, CD4 cell count, clinical stage and treatment programme. A total of 15,915 HIV-infected patients (median CD4 cell count 110 cells/µL, median age 35 years, 68% female) were included; 1,001 (6.3%) were known to have died and 1,285 (14.3%) were lost to follow-up in the first year of ART. Crude estimates of mortality at one year ranged from 5.7% (95% CI 4.9-6.5%) to 10.9% (9.6-12.4%) across the five programmes. Estimated mortality hazard ratios comparing patients lost to follow-up with those remaining in care ranged from 6 to 23. Adjusted estimates based on these hazard ratios ranged from 10.2% (8.9-11.6%) to 16.9% (15.0-19.1%), with relative increases in mortality ranging from 27% to 73% across programmes. CONCLUSIONS: Naïve survival analysis ignoring excess mortality in patients lost to follow-up may greatly underestimate overall mortality, and bias ART programme evaluations. Adjusted mortality estimates can be obtained based on excess mortality rates in patients lost to follow-up.
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    Flexible modelling of risk factors on the incidence of pneumonia in young children in South Africa using piece-wise exponential additive mixed modelling
    (2021-01-11) Ramjith, Jordache; Roes, Kit C; Zar, Heather J; Jonker, Marianne A
    Introduction Recurrent episodes of pneumonia are frequently modeled using extensions of the Cox proportional hazards model with the underlying assumption of time-constant relative risks measured by the hazard ratio. We aim to relax this assumption in a study on the effect of factors on the evolution of pneumonia incidence over time based on data from a South African birth cohort study, the Drakenstein child health study. Methods We describe and apply two models: a time-constant and a time-varying relative effects model in a piece-wise exponential additive mixed model’s framework for recurrent events. A more complex model that fits in the same framework is applied to study the continuously measured seasonal effects. Results We find that several risk factors (male sex, preterm birth, low birthweight, lower socioeconomic status, lower maternal education and maternal cigarette smoking) have strong relative effects that are persistent across time. When time-varying effects are allowed in the model, HIV exposure status (HIV exposed & uninfected versus HIV unexposed) shows a strong relative effect for younger children, but this effect weakens as children grow older, with a null effect reached from about 15 months. Weight-for-length at birth shows a time increasing relative effect. We also find that children born in the summer have a much higher risk of pneumonia in the 3-to-8-month age period compared with children born in winter. Conclusion This work highlights the usefulness of flexible modelling tools in recurrent events models. It avoids stringent assumptions and allows estimation and visualization of absolute and relative risks over time of key factors associated with incidence of pneumonia in young children, providing new perspectives on the role of risk factors such HIV exposure.
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    Survival of South-African HIV infected patients
    (1998) Post, Frank A; Wood, Robin
    In sub-Saharan Africa, resource-limitation results in scarce availability of HIV prognostic tools such as CD4+ T-Lymphocyte (CD4) count and HIV viral load. To facilitate counselling and clinical decisions in this setting, widely available and inexpensive markers of prognosis are required. Chapter one gives an overview of the epidemiology and pathophysiology of HIV infection (with particular reference to sub-Saharan Africa), and its clinical manifestations. Staging systems for HIV infection and aspects of management in resource-poor environments are briefly discussed. Chapter two describes the epidemiological, pathophysiological and clinical aspects of tuberculosis (TB) in HIV infected patients, the commonest opportunistic infection in sub-Saharan Africa. It further provides HIV and TB prevalence data from the Western Cape, South Africa. In chapter three a study is presented demonstrating the usefulness of the total lymphocyte count (TLC) in combination with the World Health Organisation (WHO) clinical staging system to predict outcome in 831 HIV positive patients. A TLC of 1250/μL was found to be the equivalent of a CD4 count of 200/μL. Patients with early HIV disease (WHO stage 1&2) had low annual rates of progression to AIDS : 3-4% if the TLC was above 1250/μL, 12-14% if the TLC was below 1250/μL. Annual progression to AIDS increased to 25% and 46% in patients with clinical stage 3 and a TLC above or below 1250/μL respectively. Patients with AIDS had 30-55% one-year mortality rates depending on the TLC. Chapter four illustrates that pulmonary tuberculosis (PTB) in HIV infected patients presents with a radiographic spectrum reflecting the degree of HIV induced immune suppression. Chest radiographs and pre-treatment total lymphocyte counts provide prognostic information. Upper zone cavitatory infiltrates typical of reactivation PTB were associated with a preserved CD4 count (mean 389/μL) and predicted a 100% two-year survival. Pleural effusions were associated with a mean CD4 count of 184/μL and predicted 65% two-year survival. Patients with atypical radiographic presentation, including lower and mid-zone infiltrates, hilar and mediastinal adenopathy or interstitial patterns, had low CD4 counts (mean 105/μL) and a 36% survival at two years. Rather than classifying every patient with pleura-pulmonary tuberculosis as WHO stage 3, incorporation of the prognostic value of the chest radiograph into the clinical staging system, such that typical reactivation PTB becomes stage 2, tuberculous pleural effusion stage 3 and atypical PTB stage 4, would enhance the prognostic accuracy of HIV related tuberculosis. Chapter five demonstrates that patients with AIDS could be categorized accord ing to one of three survival patterns, relating to the type of opportunistic illness. One-year survival rates were highest for extra-pulmonary tuberculosis and herpes simplex virus infection (70% ); intermediate for oesophageal candidiasis, cryptococcal meningitis, kaposi sarcoma and pneumocystis carinii pneumonia (45%) ; and poorest for the HIV wasting syndrome, AIDS-dementia complex and performance status 4 (20%). Despite the overall poor prognosis associated with the acquired immunodeficiency syndrome, a substantial proportion of patients survive, even in the absence of anti-retroviral therapy, for a number of years. Chapter six concludes by proposing how the data presented in this thesis could be used in the clinical management of patients with HIV infection in a resource limited environment.