A practical approach to adjusting for attrition bias in HIV clinical trials with serial marker responses.
OBJECTIVE
To illustrate a simple approach to adjusting for bias due to drop-outs (i.e., attrition bias) when evaluating the effect of a certain therapy in HIV clinical trials using the mean change in plasma viral load. To evaluate its validity and to compare its performance with that of another simple method for handling drop-outs: the last observation carried forward (LOCF) method.


METHODS
Data from a notional treated group of 100 patients followed up to 52 weeks were generated. Attrition bias was introduced by mimicking selective patient drop-out (i.e., more likely in patients doing badly).


METHODS
The difference between the true mean change in HIV RNA levels at 52 weeks and the observed mean change because of drop-outs was calculated (attrition bias). The reduction in bias obtained by using the proposed approach was then calculated and compared with that obtained by using the LOCF method. To assess the performance of the methods over the entire follow-up, the mean areas under the curves were considered.


RESULTS
Our method reduced the bias by a clinically relevant amount in a variety of different settings. In most of our simulations, bias was reduced by a larger amount than that obtainable from using the LOCF method.


CONCLUSIONS
The current situation is that results from trials in HIV infection are invariably presented with no associated attempt to quantify the attrition bias present. Attrition-adjusted plots of mean change in HIV RNA should, we believe, be presented alongside usual plots as a form of sensitivity analysis.
Cozzi Lepri
A
A
Department of Primary Care and Population Sciences, Royal Free and University College London Schools of Medicine, UK.
Smith
G D
GD
Mocroft
A
A
Sabin
C A
CA
Morris
R W
RW
Phillips
A N
AN
D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy* / Q000276:immunology; D015982:Bias; D002986:Clinical Trials as Topic / Q000592:standards / Q000706:statistics & numerical data*; D005500:Follow-Up Studies; D006678:HIV / Q000235:genetics*; D006801:Humans; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D011336:Probability; D012367:RNA, Viral / Q000097:blood*; D015203:Reproducibility of Results; D019562:Viral Load
D012367:RNA, Viral