The effect of covariates on the induction time of AIDS using improved imputation of exact seroconversion times

Imputation methods for doubly censored HIV data

In medical research, it is common to have doubly censored survival data: origin time and event time are both subject to censoring. In this paper, we review simple and probability-based methods that are used to impute interval censored origin time and compare the performance of these methods through extensive simulations in the one-sample problem, two-sample problem and Cox regression model problem. The use of a bootstrap procedure for inference is demonstrated.

[Imputation of the date of HIV seroconversion in cohorts of haemophiliacs]

OBJECTIVES

To describe the methods used to impute HIV seroconversion date in the haemophiliac cohorts from GEMES project and to validate its use.

METHOD

632 haemophiliacs coming from three hemophilia units identified as HIV+ and 1.092 individuals coming from 5 project GEMES cohorts with a seroconversion window (time among test HIV and HIV+) less than 3 years where mid point (PM) was assumed as seroconversion date. For both groups, seroconversion date was imputed after estimating the probability distribution of seroconversion by means of the EM algorithm. Two imputation methods are used: one obtained from the expected value and the other from the geometric mean of 5 random samples. from the estimated distribution. Imputations have been validated in the non haemophiliacs cohorts comparing with the PM seroconversion date. Also AIDS free time and survival from the different seroconversion imputed dates were compared.

RESULTS

Median seroconversion date is located in May of 1993 for the non haemophiliacs and in 1982 for the haemophiliacs. Not big differences are observed among the imputed seroconversion dates and the mid-point seroconversion date in the non-haemophiliac cohorts. Similar results are found for the haemophiliac cohorts. Also no differences are observed in the estimated AIDS-free time for both groups of cohorts.

CONCLUSIONS

Geometric mean imputation from several random samples provides a good estimate of the HIV seroconversion date that can be used to estimate AIDS-free time and survival in haemophiliac cohorts where seroconversion date is ignored.

Viral load detectability profiles for HIV infection

The introduction of potent antiretroviral therapies for treatment of HIV infection typically results in a dramatic reduction in plasma HIV RNA concentration, often to levels undetectable by current measurement practices. However, although a high proportion of patients achieve 'undetectability', many then experience a return to a state of detectability at a later date. As evaluation of virologic response provides a useful measure of therapy efficacy, it is of interest to estimate the proportions of cases with undetectable viral load over time following commencement of treatment. These proportions depend on the rates of transition from detectability to undetectability and subsequent return to detectability, and may be related to covariates or risk factors, possibly differing in both transitions. We consider construction of detectability profiles as estimates of these proportions, based on parametric modelling of the component survival distributions. The method is applied to an examination of the effects of baseline CD4 T-cell lymphocyte counts on virologic response to therapy amongst patients of the Western Australian HIV Cohort Study.

Multinomial analysis of smoothed HIV back-calculation models incorporating uncertainty in the AIDS incidence

Back-calculation models, developed to reconstruct the past trend of human immunodeficiency virus (HIV) and to project future acquired immunodeficiency syndrome incidence (AIDS), are usually and unrealistically based on the assumption that the observed AIDS counts are independently distributed according to a Poisson process. In contrast, we argue that a multinomial framework is more suitable to this situation, leading to a natural covariance structure. The ill-conditioned nature of the problem is solved by modelling the HIV parameters according to a cubic spline function to reduce the dimensionality of the parameter space and obtain smoother parameter estimates. We applied a regression spline technique which yields to a computationally stable basis incorporating the incubation period in the new design matrix. We directly incorporate the reporting delay distribution in the AIDS incidence data, leading to a more complex formulation of the variance and covariance model that is adapted to the iteratively reweighted least square (IRLS) algorithm. In this case we obtain more accurate estimates of the standard error of the HIV incidence, especially in the most recent time. Our model, which uses a cubic spline reparameterization based on a multinomial probability distribution, is applied to the AIDS epidemic data in Italy.

Methods for estimating the AIDS incubation time distribution when date of seroconversion is censored

In most cohort studies on HIV infection and AIDS, data on time from seroconversion to AIDS or death are doubly censored, both at the time origin and at the endpoint of interest. In epidemiological research, the most frequently adopted approach is to restrict the analysis to persons with narrow seroconversion intervals and to impute the midpoint of this interval as date of seroconversion. For many cohort studies, the consequence is that a substantial proportion of the data is not used. We consider four methods that are expected to be less biased when all cohort data are used: two imputation methods, conditional mean and multiple imputation, and two likelihood maximization methods. We derive the likelihood structure of the cohort data and clarify its dependence on study design. All methods are applied to data from the Amsterdam cohort study among injection drug users. In a simulation study the data generation process of this cohort study is imitated. The performance of midpoint, conditional mean and multiple imputation are compared. With midpoint imputation, both an analysis using the full data set, as well as one restricted to the cases with small seroconversion intervals, is performed. Conditional mean imputation comes out as the preferred method. It gives best results with respect to mean squared error. Moreover, when confidence intervals are computed through standard methods that ignore the uncertainty in the imputed date of seroconversion, coverage probabilities are almost correct.

[AIDS-free time and survival of an injecting drug users HIV seroconvertors cohort]

OBJECTIVE

To analyze AIDS free time, survival and the pre-AIDS survival for a injecting drug users cohort (IDU) of HIV seroconvertors.

SUBJECT AND METHODS

Interval for seroconversion was available for 276 IDUs from Centers for AIDS Information and Prevention (CIPS) recruited between 1987 and until June of 1996. AIDS diagnosis and vital status dates were obtained by follow up at hospitals, mortality and AIDS registries, and CIPS visits. The end of follow up was December of 1996. Seroconversion date was estimated as the middle point between last HIV- and first HIV+. Kaplan-Meier extension and Cox regression for truncated data were fitted to estimate AIDS-free and survival times and to observe differences by sex, age consumption time and year of seroconversion. Weibull, and Log-normal parametric models were fitted to estimate median and percentiles of AIDS-free and survival times distribution.

RESULTS

34 cases have been identified as AIDS, 24 as deaths, 9 of them being before AIDS. 63.5% of the individuals were AIDS-free 7 years after seroconversion, and the probability of death was 25.50. Pre-AIDS mortality is around 8.7%. There were not significant differences by sex, age, consumption time and year of seroconversion. Log-normal model fitted better estimating an AIDS-free median time of 10.93 years and 13.67 survival years.

CONCLUSION

The incubation period of HIV infection in a cohort of seroconvertors in our environment was around 11 years, not different from that observed out of the Mediterranean area as Holland, Scotland or United States

Comparison of progression and non-progression in injecting drug users and homosexual men with documented dates of HIV-1 seroconversion. European Seroconverter Study and the Tricontinental Seroconverter Study

OBJECTIVE

To compare the progression and non-progression of HIV infection among 418 injecting drug users (IDU) and 422 homosexual men with documented dates of HIV seroconversion from 12 cohorts.

METHODS

Seroconversion dates were calculated for each subject using a cohort-specific estimate of the cumulative HIV seroincidence over calendar time. In survival analysis, we studied the progression from seroconversion to AIDS and death by risk group. We compared non-progression between both risk groups by evaluating annual CD4 decline over the 7 years following seroconversion among AIDS-free subjects.

RESULTS

The relative hazard (RH) of AIDS for homosexual men compared with IDU was 1.54 before, and 1.21 after, adjusting for age at seroconversion and year of seroconversion. The risk of death from any cause for homosexual men compared with IDU increased over time since seroconversion. Fifty IDU died prior to AIDS, compared with seven homosexual men (unadjusted RH for homosexual men 0.10). Ignoring this pre-AIDS mortality, the crude RH of death for homosexual men compared with IDU was 2.05. Alter adjusting for age at seroconversion and year of seroconversion in multivariate analysis, the RH became 1.42. No differences in progression between subgroups aged 24 years or older could be demonstrated, but subjects < 24 years were found to be at a decreased risk. Proportions of non-progressors based on CD4 slope > or = 0 at 7 years following seroconversion were higher for IDU than for homosexual men. No differences were found in the proportion (approximately 5%) classified as non-progressors by criteria of both slope > or = 0 and absolute CD4 counts > 500 cells x 10(6)/l, even if pre-AIDS deaths and losses to follow-up were included.

CONCLUSIONS

We found little evidence for an effect of risk group on progression and non-progression. Pre-AIDS mortality was much higher among IDU than homosexual men. Pre-AIDS mortality and a nonlinear age effect should be considered in planning interventions as well as studies comparing risk groups and modelling the epidemic.

The association between youth, women, and acquired immunodeficiency syndrome

This article compares the characteristics of women and heterosexual men with AIDS in New York City. The analysis was performed using the New York City AIDS Surveillance Database, namely, those 37,002 persons diagnosed from 1984 to 1993 between ages 15 and 64, excluding men who report sex with other men as their sole risk behavior. The median age at diagnosis was 34 years for women with heterosexually acquired disease, 36 years for women with a history of injection drug use, and 39 years for men, most of whom used injection drugs (p < 0.001). The proportion of women and the rate of increase of this proportion were greater among younger AIDS cases. By 1993 women comprised the majority of cases under age 30, and most of these young women had heterosexually acquired disease. For each decrease in 5-year age group under age 45, the odds of a case being a woman increased by 30% (95% confidence interval = 27, 33%) after adjustment for year. CD4 cell count reporting, and race/ethnicity. There was a somewhat greater youth-gender effect among black persons with AIDS (6% additional increase for each decrease in age group; 95% confidence interval = 3, 10%). Therefore, women are overrepresented among younger persons with AIDS, particularly persons of color. They are largely infected through heterosexual contact with men who have used intravenous drugs.

Seasonal and other short-term influences on United States AIDS incidence

This paper models monthly AIDS diagnosis counts in terms of smooth secular trend, calendar month effects, and the number of workdays per month. A parameterization of month effects allows separation of true seasonal effects from a linear trend over the calendar year and an arbitrary June effect. There is strong evidence for seasonal patterns, other calendar month effects, and workday effects. Examination of subgroups defined by reporting delay, initial diagnosis, risk group, and region shows evidence for seasonal patterns in some diagnosis groups, for effects due to imputed diagnosis dates, and for effects due to patients' choices of when to seek diagnosis.