165 research outputs found
Prediction of time to threshold from a repeatedly measured biomarker
No full textBiomerkers spelen een cruciale rol in het medisch onderzoek en helpen de arts om een diagnose te stellen en in het bijzonder om te bepalen wanneer het tijd is om te interveni¨eren.
Jammer genoeg zijn nogal wat biomerkers onderhevig aan een grote mate van schommelingen en belangrijke meetfouten. Uiteraard leidt dit tot bezorgdheid over de geldigheid
van medische beslissingen die op ´e´en enkele meting van een dergelijke merker gestoeld
zijn. Verscheidene studies hebben aangetoond dat beslissingen, genomen op basis van
verscheidene opeenvolgende metingen van eenzelfde merker, veel betrouwbaarder zijn. In
dat verband stellen onderzoekers zogenaamde persistentie-criteria voor, waarmee verwezen
wordt naar het twee (of meer) keer voorkomen van een verhoogde (of verlaagde) waarde
van dezelfde merker; hierbij is de grenswaarde natuurlijk van groot belang (Amornkul et
al., 2013; Zhang, 2015).
In Hoofdstuk 2 beschrijven we vier studies waar de tijd tot het overschrijden van een
bepaalde grenswaarde van groot belang is. De studies komen uit de volgende gebieden:
HIV/AIDS, cardiologie en psychiatrie. In Hoofdstuk 3 schetsen we de basisbeginselen van
de analyse van longitudinale gegevens, met bijzondere aandacht voor het random effect
model. Deze concepten vormen de bouwstenen voor de voorgestelde aanpak voor de
schatting van de tijd tot grenswaarde, die we uitwerken in Hoofdstuk 4.
In Hoofdstuk 4 stellen we dus een methode voor om de tijd tot het overschrijden van
een grenswaarde te schatten, op basis van persistentie-criteria. We maken daarbij gebruik
van een tweetraps methode. In eerste instantie passen we een lineair gemengd model aan de longitudinale metingen aan, waaruit dan voor de pati¨ent specifieke waarden volgen.
Die zijn een functie van zogenaamde vaste effecten en empirische Bayes schatters. Op
basis hiervan wordt de kans bepaald om twee opeenvolgende waarnemingen te hebben
die onder (of boven) een bepaalde grenswaarde liggen. Door dit te doen voor elk van de
geplande meetmomenten, kunnen we de verwachte overschrijdingstijd berekenen. Door
het afleiden van een recursieve relatie van de zogenaamde continueringskansen op elk moment, kunnen we aantonen dat de berekening van de verwachte tijd eenvoudig en effici¨ent
is, en makkelijk ge¨ımplementeerd kan worden met behulp van bestaande software. We
passen deze methodologie toe op twee studies en voeren een sensitiviteitsanalyse uit om
te achterhalen of ze robuust is tegen afwijkingen van de gemaakte veronderstellingen. Een
mogelijke tekortkoming is dat de methode, gegeven random effecten, onafhankelijkheid
veronderstelt van de residuen, de zogenaamde conditionele onafhankelijkheidsassumptie.
In Hoofdstuk 5 breiden we de methodologie, voorgesteld in Hoofstuk 4, zodanig uit
dat ook seri¨ele correlatie kan meegenomen worden. Veronderstellend dat de zogenaamde
Markov eigenschap geldt, en met behulp van de kettingregel voor kansen, laten we zien
dat de continueringskans op elk moment kan uitgedrukt worden als het product van
conditionele kansen. We passen de methode toe op een cohort van HIV positieve personen,
waarbij we de tijd tot aan een CD4 grenswaarde schatten. Om de impact na te gaan
van het over het hoofd zien van seri¨ele correlatie, vergelijken we de aanpak van vorig
hoofdstuk met de hier voorgestelde. We stellen vast dat het verkeerd modelleren van de
correlatiestructuur tot substanti¨ele overschatting van de tijd tot grenswaarde kan leiden.
In Hoofdstuk 6 beschouwen we biomerkers die onderhevig zijn aan detectielimieten
en/of aan censurering. Onze methodologie wordt aan dergelijke situaties aangepast. We
stellen vast dat, mits het opnemen van censurering in de likelihood functie, de methode
uit Hoofdstuk 4 gewoon kan gebruikt worden. We passen ze dan toe op metingen van
virusdruk, gemeten in pati¨enten uit de ACTG 315 studie. Meer bepaald schatten we de
tijd tot behandelsucces.
In Hoofdstukken 4, 5 en 6 gingen we uit van continue biomerkers. Uiteraard vormt dit
een beperking. Immers, de gezondheidstoestand kan bijvoorbeeld ook gemeten worden
op een ordinale schaal, zoals we die vaak tegenkomen in de psychiatrie. In Hoofdstuk 7
stellen we daarom een variant voor van de methode uit Hoofdstuk 4, dus voor het geval van een ordinale merker. We passen de methode toe op gegevens van pati¨enten die behandeld worden voor schizofrenie, waarbij onze interesse uitgaat naar tijd tot remissie. De
methodologie voor tijd tot grenswaarde, voorgesteld in Hoofdstukken 4 t.e.m. 7, gaat uit
van de veronderstelling dat de ontbrekende gegevens ignorable zijn. Deze veronderstelling
wordt onderworpen aan een sensitiviteitsanalyse in Hoofdstuk 8. De thesis sluit af met een
samenvatting van de bijdragen, geleverd in de diverse hoofdstukken. Routes voor verder
onderzoek worden tot slot aangegeven
Supplemental Material - Understanding the impact of women’s correct risk perception on human immunodeficiency virus diagnosis: Insights from South Africa
No full textSupplemental Material for Understanding the impact of women’s correct risk perception on human immunodeficiency virus diagnosis: Insights from South Africa by Handan Wand, Jayajothi Moodley, Tarylee Reddy, Sarita Naidoo in International Journal of STD & AIDS.</p
Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach
No full textBackground: The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. Methods: In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. Results: We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days. Conclusions: By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.Reddy, T (corresponding author), South African Med Res Council, Biostat Res Unit, Cape Town, South Africa ; Hasselt Univ, Censtat, Hasselt, Belgium.
[email protected]
Sustainable Statistical Capacity-Building for Africa: The Biostatistics Case
No full textSeveral major global challenges, including climate change and water scarcity, warrant a scientific approach to generating solutions. Developing high quality and robust capacity in (bio)statistics is key to ensuring sound scientific solutions to these challenges, so collaboration between academic and research institutes should be high on university agendas.To strengthen capacity in the developing world, South-North partnerships should be a priority. The ideas and examples of statistical capacity-building presented in this article are the result of several monthly online discussions between a mixed group of authors having international experience and formal links with Hasselt University in Belgium. The discussion focuses on statistical capacity-building through education (teaching), research, and societal impact. We have adopted an example-based approach, and in view of the background of the authors, the examples refer mainly to biostatistical capacity-building. Although many universities worldwide have already initiated university collaborations for development, we hope and believe that our ideas and concrete examples can serve as inspiration to further strengthen South-North partnerships on statistical capacity-building.We are grateful to the reviewer for the in-depth reading of earlier drafts of this article and for many valuable suggestions. We also acknowledge the support of Kristien Verbrugghen (VLIRUOS director) and Tim Zeuwts (VLIR-UOS staff ) and of Lieve Quanten and Stefanie Kerkhofs (both UHasselt
Maternal and infant outcomes among pregnant women treated for multidrug/rifampicin-resistant tuberculosis in South Africa
No full textCITATION: Marian Loveday, Jennifer Hughes, Babu Sunkari, Iqbal Master, Sindisiwe Hlangu, Tarylee Reddy, Sunitha Chotoo, Nathan Green, James A Seddon, Maternal and Infant Outcomes Among Pregnant Women Treated for Multidrug/Rifampicin-Resistant Tuberculosis in South Africa, Clinical Infectious Diseases 72(7):1158–1168 pages, doi.10.1093/cid/ciaa189The original publication is available at: academic.oup.comBackground
Data on safety and efficacy of second-line tuberculosis drugs in pregnant women and their infants are severely limited due to exclusion from clinical trials and expanded access programs.
Methods
Pregnant women starting treatment for multidrug/rifampicin-resistant (MDR/RR)-tuberculosis at King Dinuzulu Hospital in KwaZulu-Natal, South Africa, from 1 January 2013 to 31 December 2017, were included. We conducted a record review to describe maternal treatment and pregnancy outcomes, and a clinical assessment to describe infant outcomes.
Results
Of 108 pregnant women treated for MDR/RR-tuberculosis, 88 (81%) were living with human immunodeficiency virus.. Favorable MDR/RR-tuberculosis treatment outcomes were reported in 72 (67%) women. Ninety-nine (91%) of the 109 babies were born alive, but overall, 52 (48%) women had unfavorable pregnancy outcomes. Fifty-eight (54%) women received bedaquiline, and 49 (45%) babies were exposed to bedaquiline in utero. Low birth weight was reported in more babies exposed to bedaquiline compared to babies not exposed (45% vs 26%; P = .034). In multivariate analyses, bedaquiline and levofloxacin, drugs often used in combination, were both independently associated with increased risk of low birth weight. Of the 86 children evaluated at 12 months, 72 (84%) had favorable outcomes; 88% of babies exposed to bedaquiline were thriving and developing normally compared to 82% of the babies not exposed.
Conclusions
MDR/RR-tuberculosis treatment outcomes among pregnant women were comparable to nonpregnant women. Although more babies exposed to bedaquiline were of low birth weight, over 80% had gained weight and were developing normally at 1 year.Publisher’s versio
Random effects models for estimation of the probability and time to progression of a continuous biomarker
No full textBiomarkers play a key role in the monitoring of disease progression. The time taken for an individual to reach a biomarker exceeding or lower than a meaningful threshold is often of interest. Due to the inherent variability of biomarkers, persistence criteria are sometimes included in the definitions of progression, such that only two consecutive measurements above or below the relevant threshold signal that "true" progression has occurred. In previous work, a novel approach was developed, which allowed estimation of the time to threshold using the parameters from a linear mixed model where the residual variance was assumed to be pure measurement error. In this paper, we extend this methodology so that serial correlation can be accommodated. Assuming that the Markov property holds and applying the chain rule of probabilities, we found that the probability of progression at each timepoint can be expressed simply as the product of conditional probabilities. The methodology is applied to a cohort of HIV positive individuals, where the time to reach a CD4 count threshold is estimated. The second application we present is based on a study on abdominal aortic aneurysms, where the time taken for an individual to reach a diameter exceeding 55 mm is studied. We observed that erroneously ignoring the residual correlation when it is strong may result in substantial overestimation of the time to threshold. The estimated probability of the biomarker reaching a threshold of interest, expected time to threshold, and confidence intervals are presented for selected patients in both applications.</p
The application of multistate Markov models to HIV disease progression.
No full textThesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2011.Survival analysis is a well developed area which explores time to single
event analysis. In some cases, however, such methods may not adequately
capture the disease process as the disease progression may involve intermediate
events of interest. Multistate models incorporate multiple events
or states. This thesis proposes to demystify the theory of multistate models
through an application based approach. We present the key components of
multistate models, relevant derivations, model diagnostics and techniques
for modeling the effect of covariates on transition intensities.
The methods that are developed in the thesis are applied to HIV and
TB data partly sourced from CAPRISA and the HPP programmes in the
University of KwaZulu-Natal. HIV progression is investigated through the
application of a five state Markov model with reversible transitions such
that state 1: CD4 count 500, state 2: 350 CD4 count < 500, state 3:
200 CD4 count < 350, state 4: CD4 count < 200 and state 5: ARV initiation.
The mean sojourn time in each state and transition probabilities
are presented as well as the effect of covariates namely age, gender and
baseline CD4 count on transition rates.
A key finding, consistent with previous research, is that the rate of decline
in CD4 count tends to decrease at lower levels of the marker. Further,
patients enrolling with a CD4 count less than 350 had a far lower chance
of immune recovery and a substantially higher chance of immune deterioration
compared to patients with a higher CD4 count. We noted that older
patients tend to progress more rapidly through the disease than younger
patients
Modelling CD4 count and mortality in a cohort of patients initiated on HAART.
No full textMaster of Science in Statistics. University of KwaZulu-Natal. Durban, 2018.Longitudinally measured data and time-to-event or survival data are often associated
in some ways, and are traditionally analyzed separately (Asar et al., 2015).
However, separate analyses are not applicable in this case because they may lead to
inefficient or biased results. To remedy this, joint models optimally incorporate all
available information (longitudinal and survival data) simultaneously (Wulfsohn &
Tsiatis, 1997). Furthermore incorporating all sources of data improves the predictive
capability of the joint model and lead to more informative inferences for the purpose
of decision-making (Seyoum & Temesgen, 2017). The primary goal of this analysis
was to determine the effect of repeatedly measured CD4 counts on mortality. The
standard time-to-event models require that the time-dependent covariates of interest
are external; where the value of the covariate at a future time point is not affected by
the occurrence of the event. This requirement would not be fulfilled in this setting,
since the repeatedly measured outcome is directly related to the mortality mechanism.
Hence, a joint modeling approach was required.
We applied the methods developed in this thesis to the CAPRISA AIDS Treatment
program (CAT). We also sought to determine if the patients’ baseline BMI (Body
mass index), baseline age, gender, baseline viral load, baseline CD8 count, baseline
TB status and clinic site, influence the evolution of the CD4 count over time. Various
linear mixed models were fitted to the CD4 count, adjusting for repeated measurements,
as well as including intercept and slope as random effects. Different types of
covariance structures were assessed and the spatial spherical correlation structure
was found to be the best fit. The Cox PH model was employed to model mortality.
Finally the joint model for longitudinal and time-to-event data was fitted.
Out of the 4014 patients, 1457 (36.30%) were male. There were more patients presenting
without TB at ART initiation, 3042 (75.78%) compared to those with prevalent
TB, 972 (24.22%). Results from the multivariable random effects model showed
that the patients gender, age, baseline viral load and baseline CD8 cell count had
statistically significant influences on the rate of change in CD4 cell count over time. The un-adjusted and adjusted hazards regression both found CD4:CD8 ratio, viral
load, gender and age of patients to be significant predictors of mortality. The result
from the joint model in this study indicated that CD4 count change due to HAART
and mortality had been influenced jointly by gender, age, baseline viral load, baseline
CD8 count, time (in years) , CD4:CD8 ratio and by the interaction effects of time
(in years) with TB status, baseline viral load and baseline CD8 cell count. CD4 count
proved to be significantly associated with mortality, after adjusting for age, gender
and other potential confounders
Model diagnostics were performed for validating model assumptions, and our joint
model fitted quite well with fairly good diagnostic attributes. The methods that were
developed in this thesis were applied to the CAPRISA AIDS Treatment program
(CAT) between June 2004 to December 2013
Challenges in assessing COVID-19 vaccine effectiveness in resource-limited settings: Experiences from South Africa
No full textEvaluating the real-world effectiveness of vaccines, including COVID-19 vaccines, and various biomedical interventions is crucial to address gaps in evidence from randomised controlled clinical trials and inform the national rollout of vaccinations. In the context of COVID-19, these gaps may include vaccine effectiveness against variants of concern and in high-risk subgroups such as people living with HIV. Designing vaccine effectiveness studies is more complex than designing randomised controlled clinical trials as it requires the availability of reliable, routinely collected data. Effectiveness studies in low- to middle-income countries (LMICs) are essential for tailoring vaccination strategies, addressing high-risk subgroups, ensuring equitable protection, and contributing valuable data to global health efforts. However, fewer COVID-19 vaccine effectiveness studies have been conducted in LMICs, including on the African continent, compared to high-income countries. Through our experience, it has become clear that national health data systems, resources and infrastructure, as well as adequate statistical capacity – which is crucial when conducting robust effectiveness studies – are lacking in LMICs. While each COVID-19 vaccine effectiveness study employed a specific study design and analytical approaches, none, to our knowledge, provided a rationale for their study design and statistical methods. Drawing from practical experiences, reflections and lessons learnt after designing a COVID-19 vaccine effectiveness study in a resource-limited setting, we present key considerations for data sources needed to run real-world effectiveness studies, for study designs, and for statistical modelling suitable for effectiveness studies. In the context of COVID-19, the study designs and statistical models are suitable for both prime and booster vaccines.
Significance:
• Substantially fewer COVID-19 vaccine effectiveness studies have been conducted in LMICs than in high-income countries.
• The lack of integrated national health data systems contributes to the lack of robust effectiveness studies in general and this was also observed during the COVID-19 pandemic.
• While each COVID-19 vaccine effectiveness study employed a specific study design and analytical approaches, none, to our knowledge, provided a rationale for their study design and statistical methods.
• Therefore, drawing from practical experiences, reflections and lessons learnt after designing a COVID-19 vaccine effectiveness study in a resource-limited setting, we present key considerations for study designs, data requirements and statistical modelling suitable for effectiveness studies
J Acquir Immune Defic Syndr
No full textObjectiveInitiation of antiretroviral therapy (ART) during tuberculosis (TB) treatment improves survival in TB-HIV co-infected patients. In patients with CD4+ counts <50cells/mm3, there is a substantial clinical and survival benefit of early ART initiation. The purpose of this study was to assess the costs and cost effectiveness of starting ART at various time points during TB treatment in patients with CD4+ counts 6550cells/mm3.MethodsIn the SAPiT trial, 642 HIV-TB co-infected patients were randomized to three arms, either receiving ART within 4 weeks of starting TB treatment (early treatment arm; Arm-1), after the intensive phase of TB treatment (late treatment arm; Arm-2), or after completing TB treatment (sequential arm; Arm-3). Direct healthcare costs were measured from a provider perspective using a micro-costing approach. The incremental cost per death averted was calculated using the trial outcomes.ResultsFor patients with CD4+ count 6550cells/mm3, median monthly variable costs per patient were 113 and 4199. There was no difference in mortality between Arm-1 and Arm-2, but Arm-1 was slightly more expensive.ConclusionsInitiation of ART after the completion of the intensive phase of TB treatment is cost effective for patients with CD4+ counts 6550cells/mm3.U19 AI051794/AI/NIAID NIH HHSUnited States/U2G PS001350/PS/NCHHSTP CDC HHSUnited States
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