In ref.study != ref.data : longer object length is not a multiple of shorter object length

[WB99]

Hey, so I’m trying to conduct a meta-analysis of Kaplan-Meier curves and I’m using the "metasurvival" package. Even though every column is of the same length, R keeps telling me that "In ref.study != ref.data : longer object length is not a multiple of shorter object length". Does anyone know how to solve this? Thank you in advance!

library(metaSurvival)

dat<-Example

attach(dat)
head(dat)

results<-msurv(study = study, time = time, n.risk = NbRisk, surv.rate = survival, confidence = "Greenwood", correctionFlag = TRUE, correctionVal = c(0.25,0.5))

^{Created on 2023-03-19 with reprex v2.0.2}
Example data set:

study	time	survival	NbRisk	censor	event
1	0.0000000	1.0000000	20	0	0
1	0.4154789	0.9982487	20	0	0
1	0.4212492	0.9877357	20	0	0
1	0.4212492	0.9669454	20	0	1
1	0.4280217	0.9506460	19	0	0
1	0.4295839	0.9506460	19	0	0
1	0.5407174	0.9506460	19	0	0
1	0.5970652	0.9506460	19	0	0
1	0.6534130	0.9506460	19	0	0
1	0.7661087	0.9506460	19	0	0
1	2.2042515	0.9506460	19	0	0
1	2.3311967	0.9499877	19	0	0
1	2.3600478	0.9285440	19	0	0
1	2.3600478	0.9181488	19	0	1
1	2.3645358	0.9181488	18	0	0
1	2.5043037	0.9015010	18	0	0
1	2.6312488	0.9015010	18	0	0
1	2.7581940	0.9015010	18	0	0
1	2.8851391	0.9009637	18	0	0
1	2.9543819	0.8786882	18	0	0
1	2.9543819	0.8682930	18	0	1
1	2.9607292	0.8537795	17	0	0
1	2.9630373	0.8537795	17	0	0
1	3.0928676	0.8514909	17	0	0
1	3.4737030	0.8514692	17	0	0
1	4.1084288	0.8512038	17	0	0
1	4.1949823	0.8252851	17	0	0
1	4.1949823	0.8148900	17	0	1
1	4.1963139	0.8148900	16	0	0
1	4.2101291	0.8029010	16	0	0
1	4.3392381	0.8014745	16	0	0
1	4.4661833	0.8014386	16	0	0
1	4.5931284	0.8014386	16	0	0
1	4.7200736	0.8014386	16	0	0
1	4.8470187	0.8014386	16	0	0
1	4.9739639	0.8000064	16	0	0
1	4.9843503	0.7883528	16	0	0
1	4.9855043	0.7777897	16	0	0
1	4.9855043	0.7673945	16	0	1
1	4.9927931	0.7536950	15	0	0
1	5.1239900	0.7507022	15	0	0
1	5.1586114	0.7256135	15	0	0
1	5.1586114	0.7152184	15	0	1
1	5.1619086	0.7152184	14	0	0
1	5.1681153	0.7030628	14	0	0
1	5.2855565	0.7001052	14	0	0
1	5.2866057	0.6870087	14	0	0
1	5.2913268	0.6753252	14	0	0
1	5.2913268	0.6649300	14	0	1
1	5.3064736	0.6529410	13	0	0
1	5.4355826	0.6512783	13	0	0
1	5.5625278	0.6502545	13	0	0
1	5.5751174	0.6370250	13	0	0
1	5.5798385	0.6253736	13	0	0
1	5.5798385	0.6149785	13	0	1
1	5.5949853	0.6030780	12	0	0
1	5.7240943	0.6012086	12	0	0
1	5.8510395	0.5997123	12	0	0
1	5.8625799	0.5890982	12	0	0
1	5.8625799	0.5724817	12	0	1
1	5.8716908	0.5524062	11	0	0
1	5.8914311	0.5255739	11	0	0
1	5.8914311	0.5151787	11	0	1
1	5.8946368	0.5151787	10	0	0
1	5.9004070	0.5022378	10	0	0
1	6.0241465	0.4992784	10	0	0
1	6.0299167	0.4888442	10	0	0
1	6.0299167	0.4160781	10	0	2
1	6.0332574	0.4022326	8	0	0
1	6.1626321	0.4017921	8	0	0
1	7.0454779	0.3999985	8	0	0
1	7.0570184	0.3893205	8	0	0
1	7.0570184	0.3685302	8	0	1
1	7.0670404	0.3538457	7	0	0
1	7.2012742	0.3517642	7	0	0
1	7.3282194	0.3517642	7	0	0
1	7.8879321	0.3291221	7	0	0
1	7.8879321	0.3187269	7	0	1
1	7.8948564	0.3187269	6	0	0
1	7.9052428	0.3187269	6	0	0
1	7.9990091	0.3012503	6	0	0
1	9.1285324	0.3012503	6	0	0
1	9.2554776	0.3008048	6	0	0
1	9.2958692	0.2791146	6	0	0
1	9.2958692	0.2687195	6	0	1
1	9.3027935	0.2687195	5	0	0
1	9.3042040	0.2540460	5	0	0
1	9.4069462	0.2505342	5	0	0
1	9.5209084	0.2505342	5	0	0
1	9.6478535	0.2505342	5	0	0
1	9.7747987	0.2505342	5	0	0
1	9.9017438	0.2505342	5	0	0
1	11.0442502	0.2500453	5	0	0
1	11.0796959	0.2343392	5	0	0
1	11.0915661	0.2199622	5	0	1
1	11.0946638	0.2034699	4	0	0
1	11.2288977	0.2008290	4	0	0
1	11.3558428	0.2007931	4	0	0
1	11.4862826	0.2007931	4	0	0
1	11.6097331	0.2007931	4	0	0
1	11.7366783	0.2000411	4	0	0
1	11.7650856	0.1868224	4	0	0
1	11.7655294	0.1735286	4	0	1
1	11.7655294	0.1423431	3	0	0
1	11.7795429	0.1331469	3	0	0
1	11.9097853	0.1331469	3	0	0
1	12.0367304	0.1331469	3	0	0
2	0.000000	1.0000000	16	4	0
2	2.972599	0.9898192	12	0	0
2	2.972868	0.9528280	12	0	1
2	3.007984	0.9360596	11	3	0
2	5.002664	0.9356764	8	1	0
2	5.978137	0.9349110	7	0	0
2	5.978271	0.9164154	7	0	0
2	5.978406	0.8979198	7	0	0
2	5.978540	0.8794242	7	0	0
2	5.978675	0.8609287	7	0	0
2	5.978776	0.8470570	7	0	1
2	5.978843	0.8378092	6	0	0
2	5.978910	0.8285614	6	0	0
2	5.978977	0.8193136	6	0	0
2	5.979044	0.8100658	6	0	0
2	5.979112	0.8008180	6	0	0
2	5.979179	0.7915702	6	0	0
2	5.979246	0.7823224	6	0	0
2	5.979313	0.7730746	6	0	0
2	5.979381	0.7638268	6	0	0
2	5.979414	0.7592029	6	0	0
2	5.979448	0.7545790	6	0	0
2	5.979481	0.7499551	6	0	0
2	5.979515	0.7453312	6	0	0
2	5.979549	0.7407073	6	0	0
2	5.979582	0.7360834	6	0	0
2	5.979616	0.7314595	6	0	0
2	5.979650	0.7268356	6	0	0
2	5.979683	0.7222117	6	0	0
2	5.979717	0.7175878	6	0	1
2	5.979750	0.7129639	5	0	0
2	5.979784	0.7083400	5	0	0
2	5.979818	0.7037161	5	0	0
2	5.979851	0.6990922	5	0	0
2	5.979885	0.6944683	5	0	0
2	5.979986	0.6805966	5	0	0
2	5.980053	0.6713488	5	0	0
2	5.980087	0.6667249	5	0	0
2	5.980120	0.6621010	5	0	0
2	5.980154	0.6574771	5	0	0
2	5.980187	0.6528532	5	0	0
2	5.980221	0.6482293	5	0	0
2	5.980255	0.6436054	5	0	0
2	5.980288	0.6389815	5	0	0
2	5.980322	0.6343576	5	0	0
2	5.980355	0.6297337	5	0	0
2	5.980389	0.6251098	5	0	0
2	5.980423	0.6204859	5	0	0
2	5.980456	0.6158620	5	0	0
2	5.980490	0.6112381	5	0	0
2	5.980524	0.6066142	5	0	0
2	5.980557	0.6019903	5	0	0
2	5.980591	0.5973664	5	0	0
2	5.980624	0.5927425	5	0	0
2	5.980658	0.5881186	5	0	1
2	5.980692	0.5834948	4	0	0
2	5.980725	0.5788709	4	0	0
2	5.980759	0.5742470	4	0	0
2	5.983340	0.5630996	4	4	0
2	8.986028	0.5563300	0	0	0
2	8.986095	0.5470822	0	0	0
2	8.986163	0.5378344	0	0	0
2	8.986230	0.5285866	0	0	0
2	8.986297	0.5193388	0	0	0
2	8.986364	0.5100910	0	0	0
2	8.986431	0.5008432	0	0	0
2	8.986499	0.4915954	0	0	0
2	8.986532	0.4869715	0	0	0
2	8.986566	0.4823476	0	0	0
2	8.986633	0.4730998	0	0	0
2	8.986700	0.4638520	0	0	0
2	8.986768	0.4546042	0	0	0
2	8.986835	0.4453564	0	0	0
2	8.986936	0.4314847	0	0	0
2	8.987003	0.4222369	0	0	0
2	8.987070	0.4129891	0	0	0
2	8.987137	0.4037413	0	0	0
2	8.987205	0.3944935	0	0	0
2	8.987272	0.3852457	0	0	0
2	8.987339	0.3759979	0	0	0
2	8.987406	0.3667501	0	0	0
2	8.987474	0.3575023	0	0	0
2	8.987743	0.3205112	0	0	0
2	10.982461	0.3149261	0	0	0
2	11.992995	0.3049061	0	0	0
2	11.993264	0.2679149	0	0	0
2	11.993398	0.2494194	0	0	0
2	11.993466	0.2401716	0	0	0
2	11.993533	0.2309238	0	0	0
2	11.993600	0.2216760	0	0	0
2	11.993667	0.2124282	0	0	0
2	11.993734	0.2031804	0	0	0
2	12.000000	0.1924876	0	0	0
3	0.0000000	1.0000000	86	0	0
3	0.3771191	0.9664148	86	1	3
3	0.9584646	0.9645975	82	1	0
3	1.5202772	0.9636304	81	1	0
3	2.0530596	0.9553136	80	1	1
3	2.3586153	0.9493715	78	0	0
3	2.5568189	0.9394176	78	1	1
3	2.7635765	0.9273941	76	0	1
3	2.9470398	0.9136588	75	1	1
3	3.2603652	0.8971583	73	0	2
3	3.4837739	0.8797844	71	0	1
3	3.5739556	0.8688014	70	1	1
3	3.7992385	0.8468332	68	0	1
3	3.8091664	0.8468332	67	0	0
3	3.8893630	0.8376799	67	0	1
3	4.0693268	0.8285221	66	1	1
3	4.1639719	0.8197711	64	0	1
3	4.3396434	0.8028921	63	0	1
3	4.3848484	0.7937411	62	0	1
3	4.6543659	0.7937274	61	1	0
3	4.7446046	0.7809147	60	0	1
3	5.0143505	0.7735821	59	0	0
3	5.1493946	0.7644266	59	0	1
3	5.1497942	0.7516184	58	0	1
3	5.1501366	0.7406400	57	1	1
3	5.2373718	0.7127646	55	0	2
3	5.2414027	0.6948920	53	0	1
3	5.4398484	0.6821550	52	1	1
3	6.0777028	0.6270670	50	1	4
3	6.3220985	0.6106694	45	0	1
3	6.5929288	0.5685717	44	0	3
3	6.5934425	0.5521041	41	0	1
3	6.6387045	0.5411234	40	1	1
3	6.9234504	0.5285246	38	1	1
3	7.4060170	0.5137801	36	1	1
3	7.9950469	0.5061289	34	1	1
3	8.5290893	0.4949369	32	1	0
3	9.0210135	0.4832529	31	0	1
3	9.3359918	0.4732862	30	1	1
3	9.6417921	0.4699296	28	1	0
3	10.1383012	0.4593659	27	1	0
3	10.5073361	0.4476621	26	0	1
3	10.6486332	0.4203638	25	0	2
3	10.8203930	0.4073404	23	0	0
3	10.8658263	0.3908704	23	0	1
3	10.8662829	0.3762326	22	1	1
3	11.0413336	0.3628733	20	1	1
3	11.4545212	0.3614794	18	0	0
3	11.7653026	0.3560598	18	0	0
3	11.9351305	0.3426886	18	0	1
3	12.3501705	0.3267543	17	1	1

[technocrat]

@WB99

Even though every column is of the same length

column lengths are not the objects that the function is comparing

library(metaSurvival)

d <- data.frame(
       Study = c(1,1,1,1,1,1,1,1,1,1,1,
                 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
                 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
                 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
                 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
                 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
                 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
                 1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
                 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
                 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
                 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
                 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
                 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,
                 3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
                 3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
                 3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,
                 3,3,3,3),
        Time = c(0,0.4154789,0.4212492,0.4212492,
                 0.4280217,0.4295839,0.5407174,0.5970652,0.653413,0.7661087,
                 2.2042515,2.3311967,2.3600478,2.3600478,2.3645358,2.5043037,
                 2.6312488,2.758194,2.8851391,2.9543819,2.9543819,2.9607292,
                 2.9630373,3.0928676,3.473703,4.1084288,4.1949823,4.1949823,
                 4.1963139,4.2101291,4.3392381,4.4661833,4.5931284,
                 4.7200736,4.8470187,4.9739639,4.9843503,4.9855043,4.9855043,
                 4.9927931,5.12399,5.1586114,5.1586114,5.1619086,5.1681153,
                 5.2855565,5.2866057,5.2913268,5.2913268,5.3064736,5.4355826,
                 5.5625278,5.5751174,5.5798385,5.5798385,5.5949853,
                 5.7240943,5.8510395,5.8625799,5.8625799,5.8716908,5.8914311,
                 5.8914311,5.8946368,5.900407,6.0241465,6.0299167,6.0299167,
                 6.0332574,6.1626321,7.0454779,7.0570184,7.0570184,7.0670404,
                 7.2012742,7.3282194,7.8879321,7.8879321,7.8948564,
                 7.9052428,7.9990091,9.1285324,9.2554776,9.2958692,9.2958692,
                 9.3027935,9.304204,9.4069462,9.5209084,9.6478535,9.7747987,
                 9.9017438,11.0442502,11.0796959,11.0915661,11.0946638,
                 11.2288977,11.3558428,11.4862826,11.6097331,11.7366783,
                 11.7650856,11.7655294,11.7655294,11.7795429,11.9097853,12.0367304,
                 0,2.972599,2.972868,3.007984,5.002664,5.978137,5.978271,
                 5.978406,5.97854,5.978675,5.978776,5.978843,5.97891,
                 5.978977,5.979044,5.979112,5.979179,5.979246,5.979313,5.979381,
                 5.979414,5.979448,5.979481,5.979515,5.979549,5.979582,
                 5.979616,5.97965,5.979683,5.979717,5.97975,5.979784,
                 5.979818,5.979851,5.979885,5.979986,5.980053,5.980087,5.98012,
                 5.980154,5.980187,5.980221,5.980255,5.980288,5.980322,
                 5.980355,5.980389,5.980423,5.980456,5.98049,5.980524,
                 5.980557,5.980591,5.980624,5.980658,5.980692,5.980725,5.980759,
                 5.98334,8.986028,8.986095,8.986163,8.98623,8.986297,
                 8.986364,8.986431,8.986499,8.986532,8.986566,8.986633,8.9867,
                 8.986768,8.986835,8.986936,8.987003,8.98707,8.987137,
                 8.987205,8.987272,8.987339,8.987406,8.987474,8.987743,
                 10.982461,11.992995,11.993264,11.993398,11.993466,11.993533,
                 11.9936,11.993667,11.993734,12,0,0.3771191,0.9584646,
                 1.5202772,2.0530596,2.3586153,2.5568189,2.7635765,2.9470398,
                 3.2603652,3.4837739,3.5739556,3.7992385,3.8091664,3.889363,
                 4.0693268,4.1639719,4.3396434,4.3848484,4.6543659,4.7446046,
                 5.0143505,5.1493946,5.1497942,5.1501366,5.2373718,
                 5.2414027,5.4398484,6.0777028,6.3220985,6.5929288,6.5934425,
                 6.6387045,6.9234504,7.406017,7.9950469,8.5290893,9.0210135,
                 9.3359918,9.6417921,10.1383012,10.5073361,10.6486332,
                 10.820393,10.8658263,10.8662829,11.0413336,11.4545212,11.7653026,
                 11.9351305,12.3501705),
    Survival = c(1,0.9982487,0.9877357,0.9669454,
                 0.950646,0.950646,0.950646,0.950646,0.950646,0.950646,0.950646,
                 0.9499877,0.928544,0.9181488,0.9181488,0.901501,0.901501,
                 0.901501,0.9009637,0.8786882,0.868293,0.8537795,0.8537795,
                 0.8514909,0.8514692,0.8512038,0.8252851,0.81489,0.81489,
                 0.802901,0.8014745,0.8014386,0.8014386,0.8014386,0.8014386,
                 0.8000064,0.7883528,0.7777897,0.7673945,0.753695,
                 0.7507022,0.7256135,0.7152184,0.7152184,0.7030628,0.7001052,
                 0.6870087,0.6753252,0.66493,0.652941,0.6512783,0.6502545,
                 0.637025,0.6253736,0.6149785,0.603078,0.6012086,0.5997123,
                 0.5890982,0.5724817,0.5524062,0.5255739,0.5151787,0.5151787,
                 0.5022378,0.4992784,0.4888442,0.4160781,0.4022326,
                 0.4017921,0.3999985,0.3893205,0.3685302,0.3538457,0.3517642,
                 0.3517642,0.3291221,0.3187269,0.3187269,0.3187269,0.3012503,
                 0.3012503,0.3008048,0.2791146,0.2687195,0.2687195,0.254046,
                 0.2505342,0.2505342,0.2505342,0.2505342,0.2505342,
                 0.2500453,0.2343392,0.2199622,0.2034699,0.200829,0.2007931,
                 0.2007931,0.2007931,0.2000411,0.1868224,0.1735286,0.1423431,
                 0.1331469,0.1331469,0.1331469,1,0.9898192,0.952828,0.9360596,
                 0.9356764,0.934911,0.9164154,0.8979198,0.8794242,
                 0.8609287,0.847057,0.8378092,0.8285614,0.8193136,0.8100658,
                 0.800818,0.7915702,0.7823224,0.7730746,0.7638268,0.7592029,
                 0.754579,0.7499551,0.7453312,0.7407073,0.7360834,0.7314595,
                 0.7268356,0.7222117,0.7175878,0.7129639,0.70834,0.7037161,
                 0.6990922,0.6944683,0.6805966,0.6713488,0.6667249,0.662101,
                 0.6574771,0.6528532,0.6482293,0.6436054,0.6389815,
                 0.6343576,0.6297337,0.6251098,0.6204859,0.615862,0.6112381,
                 0.6066142,0.6019903,0.5973664,0.5927425,0.5881186,0.5834948,
                 0.5788709,0.574247,0.5630996,0.55633,0.5470822,0.5378344,
                 0.5285866,0.5193388,0.510091,0.5008432,0.4915954,0.4869715,
                 0.4823476,0.4730998,0.463852,0.4546042,0.4453564,
                 0.4314847,0.4222369,0.4129891,0.4037413,0.3944935,0.3852457,
                 0.3759979,0.3667501,0.3575023,0.3205112,0.3149261,0.3049061,
                 0.2679149,0.2494194,0.2401716,0.2309238,0.221676,0.2124282,
                 0.2031804,0.1924876,1,0.9664148,0.9645975,0.9636304,
                 0.9553136,0.9493715,0.9394176,0.9273941,0.9136588,0.8971583,
                 0.8797844,0.8688014,0.8468332,0.8468332,0.8376799,0.8285221,
                 0.8197711,0.8028921,0.7937411,0.7937274,0.7809147,
                 0.7735821,0.7644266,0.7516184,0.74064,0.7127646,0.694892,
                 0.682155,0.627067,0.6106694,0.5685717,0.5521041,0.5411234,
                 0.5285246,0.5137801,0.5061289,0.4949369,0.4832529,0.4732862,
                 0.4699296,0.4593659,0.4476621,0.4203638,0.4073404,0.3908704,
                 0.3762326,0.3628733,0.3614794,0.3560598,0.3426886,
                 0.3267543),
      NbRisk = c(20,20,20,20,19,19,19,19,19,
                 19,19,19,19,19,18,18,18,18,18,18,18,
                 17,17,17,17,17,17,17,16,16,16,16,16,16,
                 16,16,16,16,16,15,15,15,15,14,14,14,
                 14,14,14,13,13,13,13,13,13,12,12,12,12,
                 12,11,11,11,10,10,10,10,10,8,8,8,8,8,
                 7,7,7,7,7,6,6,6,6,6,6,6,5,5,5,
                 5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,3,
                 3,3,3,16,12,12,11,8,7,7,7,7,7,7,6,
                 6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,
                 6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,
                 5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,86,86,82,81,80,78,78,76,75,73,71,
                 70,68,67,67,66,64,63,62,61,60,59,59,
                 58,57,55,53,52,50,45,44,41,40,38,36,34,
                 32,31,30,28,27,26,25,23,23,22,20,18,
                 18,18,17),
      censor = c(0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,4,0,0,3,1,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,1,1,1,1,0,1,0,1,0,0,1,0,0,0,1,
                 0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,
                 0,1,1,1,1,1,0,1,1,1,0,0,0,0,1,1,
                 0,0,0,1),
       event = c(0,0,0,1,0,0,0,0,0,0,0,
                 0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,
                 1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,
                 1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,
                 0,1,0,0,1,0,0,0,0,2,0,0,0,0,1,0,
                 0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,
                 0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,
                 0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,3,0,0,1,0,1,1,1,2,1,1,1,0,1,1,
                 1,1,1,0,1,0,1,1,1,2,1,1,4,1,3,
                 1,1,1,1,1,0,1,1,0,0,1,2,0,1,1,1,
                 0,0,1,1)
)

# conformed column names to those used in metasurvival::exampleData
# as used in the help(msurv) example and
# following help(msurv) example attach the data frame;
# this is something that should be avoided, the function
# should have been written to take a data = argument so
# that it wouldn't be necessary
# to avoid future problems, either specify all arguments
# as your_data$study ... etc OR
# be sure to begin each use in a fresh R session

attach(d)
results <- msurv(study = Study, time = Time, n.risk = NbRisk, surv.rate = Survival, confidence = "Greenwood", correctionFlag = TRUE, correctionVal = c(0.25,0.5))
#> Warning in ref.study != ref.data: longer object length is not a multiple of
#> shorter object length

#> Warning in ref.study != ref.data: longer object length is not a multiple of
#> shorter object length

#> Warning in ref.study != ref.data: longer object length is not a multiple of
#> shorter object length
results
#> $verif.data
#>   Sstudy check
#> 1      1     0
#> 2      2     0
#> 3      3     0
#> 
#> $summary.fixed
#> [1] NA
#> 
#> $median.fixed
#> [1] NA
#> 
#> $mean.fixed
#> [1] NA
#> 
#> $heterogeneity
#> [1] NA
#> 
#> $summary.random
#> [1] NA
#> 
#> $median.random
#> [1] NA
#> 
#> $mean.random
#> [1] NA

# within msurv, modified to conform to names of corresponding
# attached data frame d
verif <- function(x) {
  IndiceTimes <- sort(unique(Time))
  ref.study <- sort(Time[Study == x])
  ref.data <- IndiceTimes[IndiceTimes <= max(ref.study)]
  return(1 * (sum(ref.study != ref.data) == 0))
}

# 1:3 corresponds to the three studies
sapply(1:3,verif)
#> Warning in ref.study != ref.data: longer object length is not a multiple of
#> shorter object length

#> Warning in ref.study != ref.data: longer object length is not a multiple of
#> shorter object length

#> Warning in ref.study != ref.data: longer object length is not a multiple of
#> shorter object length
#> [1] 0 0 0

# each has the identical issue
verif(1)
#> Warning in ref.study != ref.data: longer object length is not a multiple of
#> shorter object length
#> [1] 0

# bring objects within verif into .Global

(IndiceTimes <- sort(unique(Time))) |> length()
#> [1] 234
(ref.study <- sort(Time[Study == 1]) ) |> length()
#> [1] 107
(ref.data <- IndiceTimes[IndiceTimes <= max(ref.study)]) |> length()
#> [1] 233

^{Created on 2023-03-21 with reprex v2.0.2}